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C3.ai COVID-19 API Documentation (5.2)

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This is the reference documentation for C3.ai COVID-19 HTTP RESTful API. The API request and responses are in JSON.

Please contribute your questions, answers and insights to the C3.ai COVID-19 Grand Challenge community.

For support, please send email to: covid@c3.ai.

Citing the C3.ai COVID-19 Data Lake

If any publications or research results are derived in full or in part from the C3.ai COVID-19 Data Lake, please make sure to credit the C3.ai COVID-19 Data Lake by referencing the case study at https://c3.ai/customers/covid-19-data-lake/.

Quickstart Guide

Get started using the C3.ai COVID-19 Data Lake with R and Python notebooks. Use the online notebooks to easily try out the C3.ai COVID-19 Data Lake APIs in the cloud without any downloads or local environment setup. Use the downloadable notebooks to edit the code and save your results locally.

R Quickstart

The R Quickstart notebook shows simple API calls and the breadth of data available in the C3.ai COVID-19 Data Lake.

Online R Notebook

To try out the C3.ai COVID-19 Data Lake without any downloads or local environment setup, run the R Notebook in your browser using Binder:

Binder

Downloadable R Notebook

To edit the notebook and save your results locally, follow these steps.

Local environment setup

Ensure that the following are installed on your computer:

Download R Notebooks

Troubleshooting

While opening the R notebook (.Rmd file), if you see the error:

Unable to locate R binary by scanning standard locations.

then you probably did not install R from CRAN. Make sure you install both R Studio and R from CRAN.

Python Quickstart

The Python Quickstart notebook shows simple API calls and the breadth of data available in the C3.ai COVID-19 Data Lake.

Online Python Jupyter Notebook

To try out the C3.ai COVID-19 Data Lake without any downloads or local environment setup, run the Python Jupyter Notebook in your browser using Binder:

Binder

Downloadable Python Jupyter Notebook

To edit the notebook and save your results locally, follow these steps.

Local environment setup

Ensure that the following are installed on your computer:

Download Python Jupyter Notebooks

Troubleshooting

  • Ensure that you have Python 3 and not Python 2.7.

  • While opening the Jupyter Notebook, if you see the error:

    "Error loading notebook: An unknown error occurred while loading this notebook. This version can load notebook formats or earlier. See the server log for details.

    then you can probably resolve this by installing Python from Anaconda.

  • If you see error messages regarding pandas functions such as json_normalize or explode, make sure that you are using a pandas version of at least 1.0.0. See the pandas installation guide for installation instructions.

R and Python Deep Dive: Mobility and Case Counts

The R and Python Deep Dive notebooks explore datasets in mobility and case counts, and provide a starting point for detailed analysis into the relationships between these datasets.

Online R Notebook

Run the Deep Dive notebook in your browser using Binder:

Binder

Downloadable R Notebook

To edit the Deep Dive notebook and save your results locally, download the zip file containing R Notebooks and library functions. For additional details, see the R Quickstart section.

Online Python Notebook

Run the Deep Dive notebook in your browser using Binder:

Binder

Downloadable Python Notebook

To edit the Deep Dive notebook and save your results locally, download the zip file containing Python Notebooks and library functions. For additional details, see the Python Quickstart section.

Python Deep Dive: Clinical and Demographic Data

The Python Deep Dive notebook explores datasets in disease spread, state-level clinical data, and demographics data and provides a starting point for detailed analysis into the relationships between these datasets.

Online Python Notebook

Run the Deep Dive notebook in your browser using Binder:

Binder

Downloadable Python Notebook

To edit the Deep Dive notebook and save your results locally, download the zip file containing Python Notebooks and library functions. For additional details, see the Python Quickstart section.

R Deep Dive: Economic Indicators

The R Deep Dive notebook explores economic indicator datasets and provides a starting point for detailed analysis into the economic impacts of the pandemic.

Online R Notebook

Run the Deep Dive notebook in your browser using Binder:

Binder

Downloadable R Notebook

To edit the Deep Dive notebook and save your results locally, download the zip file containing R Notebooks and library functions. For additional details, see the R Quickstart section.

Release Notes

Release Notes for 5.2 (October 16, 2020)

Version 5.2 provides minor documentation and data availability updates.



Release Notes for 5.1 (September 14, 2020)

Version 5.1 provides updates to data availability and adds new metrics to the following data sources:
  • Opportunity Insights: Economic Tracker
  • Corona Data Scraper



Release Notes for 5.0 (August 11, 2020)

Version 5.0 adds eight new datasets to reach 40 total datasets in C3.ai COVID-19 Data Lake.

What's new:

  • Added eight new datasets (see C3.ai APIs for COVID-19 Unified Data):
    • US Bureau of Labor Statistics: County Unemployment Statistics
    • Realtor.com: Housing Indicators
    • Bureau of Economic Analysis: GDP and Economic Profile by County
    • Swayable and TapResearch: COVID-19 Tracker Poll
    • Opportunity Insights: Economic Tracker
    • Centers for Disease Control and Prevention: Weekly Updates by Select Demographic Characteristics
    • COVID Racial Data Tracker
    • US Census Bureau: County Population by Age, Sex, Race, and Hispanic Origin
  • Added two new C3.ai Types: LaborDetail and SurveyData.
  • Added two Deep Dive notebooks exploring economic data and medical data.
  • Revised Quickstart notebooks to include new C3.ai Types and APIs.
  • Revised document for clarity.

Release Notes for 4.0 (June 23, 2020)

Version 4.0 adds six new datasets.

What's new:

  • Added six new datasets (see C3.ai APIs for COVID-19 Unified Data):
    • The New York Times: All-Cause Mortality,
    • University of Oxford: Coronavirus Government Response Tracker,
    • US Census Bureau: International Census,
    • The World Bank: Finance Related Policy Responses to COVID-19,
    • PlaceIQ Exposure Indices,
    • IBM: Weather Company Data.
  • Added two new C3.ai Types: LocationExposure and PolicyDetail.
  • Added Deep Dive notebook exploring mobility data and case counts.
  • Revised Quickstart notebooks to include new C3.ai Types and APIs.
  • Renamed Policy Type to LocationPolicySummary for clarity. Query via Policy Type is deprecated but still supported.
  • Revised document for clarity.

Release Notes for 3.0 (June 2, 2020)

Version 3.0 adds four new datasets, dataset versioning, and online Quickstart notebooks.

What's new:

  • Added four new datasets (see C3.ai APIs for COVID-19 Unified Data):
    • Corona Data Scraper: COVID-19 Coronavirus Case Data
    • Centers for Disease Control and Prevention (CDC): VaxView
    • Cytel: Global Coronavirus COVID-19 Clinical Trial Tracker
    • Google: COVID-19 Community Mobility Reports
  • Added two new C3.ai Types: VaccineCoverage and ClinicalTrial
  • Added one new API: GetProjectionHistory
  • Revised Quickstart notebooks to include new C3.ai Types and APIs
  • Added online hosting for Quickstart notebooks
  • Revised document for clarity

Release Notes for 2.0 (May 15, 2020)

Version 2.0 doubles the number of datasets incorporated into the C3.ai COVID-19 Data Lake from 11 to 22.

What's new:

Release Notes for 1.0 (April 22, 2020)

Version 1.0 is the full release of the C3.ai COVID-19 API documentation.

What's new:

  • GetArticleMetadata now provides metadata for multiple article using "ids" rather than "id"
  • Added "Interpolated" JHU metrics for OutbreakLocation EvalMetrics API
  • Added additional clarifications and wording adjustments

Release Notes for 0.1

Version 0.1 is the initial release of the C3.ai COVID-19 API documentation.

Data from Multiple Sources

Using these APIs, you can pull together data from multiple COVID-19 data sources with a single API call. This is made possible by using C3.ai Types.

If you are new to the concept of a C3.ai Type, then it is easier to think of a C3.ai Type as an entity that holds the data. Using C3.ai Types makes it possible to programmatically interact with a unified, federated image of COVID-19 data.

On this page, entries such as OutbreakLocation, LineListRecord are the names of C3.ai Types. Each C3.ai Type holds data of a certain kind. For example:

  • OutbreakLocation stores location data such as countries, provinces, cities, where COVID-19 outbeaks are recorded, and
  • LineListRecord stores individual-level information such as symptoms, travel history, reported onset, and discharge status from laboratory-confirmed COVID-19 patients.

While each such C3.ai Type holds the data of a particular kind, you can use these APIs to connect up the data from multiple C3.ai Types. For example, you can join the data from two C3.ai Types, BiologicalAsset and Sequence. This can be accomplished by using the include option in the fetch API call.

The following is an example entity relationship diagram showing how C3.ai Types are connected. Not all fields are shown in the below diagram. Refer to the Fields table for a C3.ai Type for a full listing of the fields for that C3.ai Type. See, for example, LineListRecord.

C3.ai COVID-19 Data Model

Fetching from Multiple Sources

The include parameter is a powerful way to fetch data from multiple C3.ai Types. This parameter can also be used to fetch specific fields from a single C3.ai Type.

When you want to join data from two C3.ai Types, you make a fetch API call to one C3.ai Type, and use include in your request body to refer to the second C3.ai Type. The returned objects will contain fields and data from both the C3.ai Types.

See the section Using Include for detailed examples showing how to use include to combine data from multiple C3.ai Types.

C3.ai APIs for COVID-19 Unified Data

The following APIs are currently suppported:

Use POST requests to access these APIs.

NOTE: If you are new to the concept of RESTful API, this Postman Learning Center is a good place to start. All APIs described in this documentation can be verified using the Postman client.

The following table shows the APIs available for specific data sources (more data sources are being added):

Data Category Data Source C3.ai Types APIs
Daily Case Reports Johns Hopkins University: COVID-19 Data Repository (link to source data) OutbreakLocation evalmetrics
Daily Case Reports COVID Tracking Project (link to source data) OutbreakLocation evalmetrics
Daily Case Reports World Health Organization: Situation Reports OutbreakLocation evalmetrics
Daily Case Reports The New York Times: Coronavirus (Covid-19) Data in the United States OutbreakLocation evalmetrics
Daily Case Reports European Centre for Disease Prevention and Control: Situation Update Worldwide (link to source data) OutbreakLocation evalmetrics
Daily Case Reports University of Washington's Institute for Health Metrics and Evaluation: COVID-19 Projections (updated through June 13, 2020) OutbreakLocation evalmetrics, getprojectionhistory
Daily Case Reports Data Science for COVID-19: South Korea Dataset OutbreakLocation, LineListRecord, PatientRoute fetch, evalmetrics
Daily Case Reports Dipartimento della Protezione Civile – Emergenza Coronavirus: La Risposta Nazionale OutbreakLocation evalmetrics
Daily Case Reports COVID-19 India OutbreakLocation, LineListRecord fetch, evalmetrics
Daily Case Reports Corona Data Scraper: COVID-19 Coronavirus Case Data OutbreakLocation evalmetrics
Daily Case Reports COVID Racial Data Tracker OutbreakLocation evalmetrics
Case Reports The New York Times: All-Cause Mortality OutbreakLocation evalmetrics
Case Reports Centers for Disease Control and Prevention: Weekly Updates by Select Demographic Characteristics OutbreakLocation evalmetrics
Epidemiology Line Lists University of Washington's Institute for Health Metrics and Evaluation: nCoV-2019 Data (updated through April 30, 2020) LineListRecord fetch
Epidemiology Line Lists Laboratory for the Modeling of Biological Socio-technical Systems (MOBS Lab): Situation Report (link to source data) LineListRecord fetch
Genome Sequences National Center for Biotechnology Information Virus Database BiologicalAsset, Sequence, Subsequence, AminoAcidLookup, NucleotideLookup fetch
Journals Allen Institute for AI: COVID-19 Open Research Dataset (CORD-19) (updated through April 8, 2020) BiblioEntry fetch, getarticlemetadata
Clinical Milken Institute: COVID-19 Treatment and Vaccine Tracker (link to source data) TherapeuticAsset fetch
Clinical World Health Organization: COVID-19 Research & Development (link to source data) TherapeuticAsset fetch
Clinical The University of Montreal: COVID-19 Image Data Collection Diagnosis, DiagnosisDetail fetch, getimageurls
Clinical Carbon Health & Braid Health: COVID-19 Clinical Data Repository Diagnosis, DiagnosisDetail fetch
Clinical Definitive Healthcare: Hospital ICU Beds Hospital fetch
Clinical Centers for Disease Control and Prevention (CDC): VaxView VaccineCoverage fetch
Clinical Cytel: Global Coronavirus COVID-19 Clinical Trial Tracker ClinicalTrial fetch
Policy Kaiser Family Foundation: Social Distancing Policies LocationPolicySummary fetch, allversionsforpolicy
Policy University of Oxford: Coronavirus Government Response Tracker PolicyDetail fetch, evalmetrics
Policy The World Bank: Finance Related Policy Responses to COVID-19 PolicyDetail fetch
Mobility Apple: COVID-19 Mobility Trends OutbreakLocation evalmetrics
Mobility Google: COVID-19 Community Mobility Reports OutbreakLocation evalmetrics
Mobility PlaceIQ Exposure Indices OutbreakLocation, LocationExposure getlocationexposures, evalmetrics
Demographics US Census Bureau: Demographic Estimates OutbreakLocation, PopulationData fetch, evalmetrics
Demographics US Census Bureau: International Census PopulationData fetch, evalmetrics
Demographics The World Bank: Global Health Statistics OutbreakLocation, PopulationData fetch
Demographics US Census Bureau: County Population by Age, Sex, Race, and Hispanic Origin OutbreakLocation evalmetrics
Economic US Bureau of Labor Statistics: County Unemployment Statistics OutbreakLocation, LaborDetail fetch, evalmetrics
Economic Realtors.com: Housing Indicators OutbreakLocation evalmetrics
Economic Bureau of Economic Analysis: GDP and Economic Profile by County OutbreakLocation evalmetrics
Economic Opportunity Insights: Economic Tracker OutbreakLocation evalmetrics
Public Surveys Swayable and TapResearch: COVID-19 Tracker Poll SurveyData fetch
Environmental IBM: Weather Company Data OutbreakLocation evalmetrics

Using C3.ai APIs

POST Requests

All C3.ai APIs described in this documentation must be accessed using POST requests.

If you receive the following error:

{
  "message": "Missing Authentication Token"
}

then you are probably using GET or another request, and you should instead use POST. No authentication token is required to access the APIs.

Required Headers

All C3.ai APIs described in this documentation must be used with the following header settings:

Headers Setting
Accept application/json
Content-Type application/json

Using Fetch

The request JSON for the fetch API should be used with the filter key. This filter key can be used in the fetch call to select any combination of the fields in the data. A few examples follow:

IMPORTANT: For a list of fields available for a C3.ai Type, refer to the fields section of that C3.ai Type in this document.

To fetch the data that match the specific values of the id field of the data:

  {
    "spec" : {"filter": 'id == "Afghanistan"'}
  }

A few other examples:

  {
    "spec" : {"filter": 'id == "Afghanistan" && age == 45'}
  }

or,

  {  // See BiologicalAsset
    "spec": {
        "filter": "isolationSource == 'feces' && location == 'Japan'",
        "limit": -1
    }
  }

or, using a "contains(field, "string")" format:

  { // See LineListRecord
    "spec": {
        "filter": "gender == 'male' && lineListSource == 'OPEN' && age <= 20 && contains(relevantTravelHistoryLocation,'Wuhan')"
   }
  }

The fetch API returns two main kinds of information in its response:

  • The data from the C3.ai Type, fetched as an array of the objects.
  • Metadata, or data describing data, such as:
    • The number of objects fetched.
    • Number of rows of information.
    • An indicator if more data exists in the C3.ai Type that was not returned.
  • See the section Limits for the number of entries returned per fetch API call.

For full details on request and response JSON, see the several examples provided in the fetch API for all the C3.ai Types in this documentation.

Using Include

In a C3.ai Type, the data type of a field can be a C3.ai Type. For example, the field links in TherapeuticAsset is of the type ExternalLink. This is how these two C3.ai Types are connected.

For example, to join data from these two connected C3.ai Types, TherapeuticAsset and ExternalLink, use the include parameter as follows:

  • Make a fetch API call to TherapeuticAsset.
  • The field links in TherapeuticAsset is of ExternalLink Type. Using the dot notation on the links field, you can access any field in the ExternalLink. For example, specifying links.url will resolve into ExternalLink.url, which will obtain the url field data from the ExternalLink.
  • Notice that we have not issued a fetch call to ExternalLink. See the full fetch example, including the response objects, below.

Example 1: Join data from TherapeuticAsset and ExternalLink (click arrow to open)

Example 1: Join data from TherapeuticAsset and ExternalLink

HTTP URL: https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request JSON:

  {
    "spec": {
      "include": "productType, description, origin, links.url",
      "filter": "origin =='Milken'",
      "limit" : 3
    }
  }



Response JSON:

  {
    "objs": [
      {
          "productType": "TAK-888, antibodies from recovered COVID-19 patients",
          "origin": "Milken",
          "links": [
              {
                  "url": "https://www.wsj.com/articles/drugmaker-takeda-is-working-on-coronavirus-drug-11583301660?mod=article_inline",
                  "therapeuticAsset": {
                      "id": "milkentreatment_001"
                  },
                  "id": "1919184b-460e-4725-8c2c-0ab225a58c1c",
                  "meta": {
                      "fetchInclude": "[id,url,therapeuticAsset,version]",
                      "fetchType": "ExternalLink"
                  },
                  "version": 1
              },
              {
                  "url": "https://phrma.org/coronavirus",
                  "therapeuticAsset": {
                      "id": "milkentreatment_001"
                  },
                  "id": "fdc646d7-eada-47af-b782-89216996b7ec",
                  "meta": {
                      "fetchInclude": "[id,url,therapeuticAsset,version]",
                      "fetchType": "ExternalLink"
                  },
                  "version": 1
              }
            ],
            "id": "milkentreatment_001",
            "meta": {
                "fetchInclude": "[productType,description,origin,{links:[id,url]},id,version]",
                "fetchType": "TherapeuticAsset"
            },
            "version": 1
        },
        {
            "productType": "Antibodies from mice, REGN3048-3051, against the spike protein",
            "origin": "Milken",
            "links": [
                {
                    "url": "https://www.statnews.com/2020/03/19/an-updated-guide-to-the-coronavirus-drugs-and-vaccines-in-development/",
                    "therapeuticAsset": {
                        "id": "milkentreatment_002"
                    },
                    "id": "154217eb-08fc-4ba8-aeb2-6ac8d93710a1",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                },
                {
                    "url": "https://www.bnnbloomberg.ca/gilead-s-drug-leads-global-race-to-find-coronavirus-treatment-1.1395231",
                    "therapeuticAsset": {
                        "id": "milkentreatment_002"
                    },
                    "id": "2984da95-4e96-4e27-9365-0b17ee04c091",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                },
                {
                    "url": "https://uk.reuters.com/article/uk-china-health-treatments-factbox/factbox-global-efforts-to-develop-vaccines-drugs-to-fight-the-coronavirus-idUKKBN20D2MD?rpc=401&",
                    "therapeuticAsset": {
                        "id": "milkentreatment_002"
                    },
                    "id": "61d19389-fe0b-4e7a-84dc-07dbca2d20c3",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                },
                {
                    "url": "https://www.marketwatch.com/story/these-nine-companies-are-working-on-coronavirus-treatments-or-vaccines-heres-where-things-stand-2020-03-06",
                    "therapeuticAsset": {
                        "id": "milkentreatment_002"
                    },
                    "id": "812e1849-5403-4940-8d46-0d372dc94bbd",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                },
                {
                    "url": "https://www.fiercebiotech.com/research/fast-moving-regeneron-eyes-summer-clinical-trial-for-covid-19-antibody-cocktail-therapy",
                    "therapeuticAsset": {
                        "id": "milkentreatment_002"
                    },
                    "id": "b583ddb9-68e3-4896-b8b8-bb4d283c5b79",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                },
                {
                    "url": "https://www.fiercepharma.com/pharma/regeneron-s-r-d-war-room-sleepless-nights-and-esprit-de-corps-hunt-for-covid-19-therapy?mkt_tok=eyJpIjoiTUROaFpUYzJOVE14TjJNMSIsInQiOiJRT0YxZVBYWHJQQVlNYzFcL3JUSUdac1Zqa0VXMGk2MHpQS3kxZXdmaWhyQlJ2MWFuR1wvb0FGb1pDWExGQ0pYQzYrN1dGMW9BRlFMUlo2WE1XOVZQN2pxaE1MUUFQSHFlYlVCc0xKdmJoTm1xdHdcL0hPeXJKS0llODJTTGR5aHZjQ2Z3ZE1KU1BFZ013R0JyeU9qTkxSZmc9PSJ9&mrkid=72869502",
                    "therapeuticAsset": {
                        "id": "milkentreatment_002"
                    },
                    "id": "f09ff2a3-2ca0-48cd-b373-6bd0a396d221",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                }
            ],
            "id": "milkentreatment_002",
            "meta": {
                "fetchInclude": "[productType,description,origin,{links:[id,url]},id,version]",
                "fetchType": "TherapeuticAsset"
            },
            "version": 1
        },
        {
            "productType": "Antibodies from recovered COVID-19 patients",
            "origin": "Milken",
            "links": [
                {
                    "url": "http://www.koreaherald.com/view.php?ud=20200312000885",
                    "therapeuticAsset": {
                        "id": "milkentreatment_003"
                    },
                    "id": "d2073283-ec4f-4f5f-821a-8cb6019ed80e",
                    "meta": {
                        "fetchInclude": "[id,url,therapeuticAsset,version]",
                        "fetchType": "ExternalLink"
                    },
                    "version": 1
                }
            ],
            "id": "milkentreatment_003",
            "meta": {
                "fetchInclude": "[productType,description,origin,{links:[id,url]},id,version]",
                "fetchType": "TherapeuticAsset"
            },
            "version": 1
        }
    ],
    "count": 3,
    "hasMore": true
  }


Example 2: Join data from BiologicalAsset and Sequence (click arrow to open)

In the following example, the include parameter is used with this keyword, which obtains all the fields from BiologicalAsset and the field sequence from Sequence.

Example 2: Join data from BiologicalAsset and Sequence

HTTP URL: https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request JSON:

  {
    "spec": {
      "include": "this, sequence.sequence",
       "filter": "exists(sequence.sequence)",
       "limit" : 3
    }
  }



Response JSON:

  {
    "objs": [
      {
        "sequence": {
            "sequence": "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQ",
            "id": "5R7Y_A"
        },
        "assetType": "protein sequence",
        "species": "Severe acute respiratory syndrome-related coronavirus",
        "genus": "Betacoronavirus",
        "family": "Coronaviridae",
        "authors": "Fearon,D., Powell,A.J., Douangamath,A., Owen,C.D., Wild,C., Krojer,T., Lukacik,P., Strain-Damerell,C.M., Walsh,M.A., von Delft,F.",
        "genBankTitle": "Chain A, main protease",
        "id": "5R7Y_A",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-02T03:39:25Z",
            "createdBy": "dataloader",
            "updated": "2020-04-02T03:39:25Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-02T03:39:33Z",
            "sourceFile": "proteins_sequence_metadata.csv",
            "fetchInclude": "[this,{sequence:[sequence,id]}]",
            "fetchType": "BiologicalAsset"
        },
        "version": 1
      },
      {
        "sequence": {
            "sequence": "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQ",
            "id": "5R7Z_A"
        },
        "assetType": "protein sequence",
        "species": "Severe acute respiratory syndrome-related coronavirus",
        "genus": "Betacoronavirus",
        "family": "Coronaviridae",
        "authors": "Fearon,D., Powell,A.J., Douangamath,A., Owen,C.D., Wild,C., Krojer,T., Lukacik,P., Strain-Damerell,C.M., Walsh,M.A., von Delft,F.",
        "genBankTitle": "Chain A, main protease",
        "id": "5R7Z_A",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-02T03:39:25Z",
            "createdBy": "dataloader",
            "updated": "2020-04-02T03:39:25Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-02T03:39:33Z",
            "sourceFile": "proteins_sequence_metadata.csv",
            "fetchInclude": "[this,{sequence:[sequence,id]}]",
            "fetchType": "BiologicalAsset"
        },
        "version": 1
      },
      {
        "sequence": {
            "sequence": "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQ",
            "id": "5R80_A"
        },
        "assetType": "protein sequence",
        "species": "Severe acute respiratory syndrome-related coronavirus",
        "genus": "Betacoronavirus",
        "family": "Coronaviridae",
        "authors": "Fearon,D., Powell,A.J., Douangamath,A., Owen,C.D., Wild,C., Krojer,T., Lukacik,P., Strain-Damerell,C.M., Walsh,M.A., von Delft,F.",
        "genBankTitle": "Chain A, main protease",
        "id": "5R80_A",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-02T03:39:25Z",
            "createdBy": "dataloader",
            "updated": "2020-04-02T03:39:25Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-02T03:39:33Z",
            "sourceFile": "proteins_sequence_metadata.csv",
            "fetchInclude": "[this,{sequence:[sequence,id]}]",
            "fetchType": "BiologicalAsset"
        },
        "version": 1
      }
    ],
    "count": 3,
    "hasMore": true
  }


More Filter Examples

  • Filter on dateTime

    {
    "spec" : {
        "filter": "field >= dateTime('YYYY-MM-DD')"
    }
}
// Example: Fetch BiologicalAssets with collectionData post March 10, 2020.
{
    "spec" : {
        "filter": "collectionDate >= dateTime('2020-03-10')"
    }
}

  • Logical operators

    // Note: Multiple filters can be applied with an "and" ("&&") or an "or" ("||") operator.

// Operator "and".
{
  "spec" : {
      "filter": "fieldA == 'stringA' && fieldB == 'stringB'"
  }
}
// Example: Fetch BiologicalAssets with isolationSource 'feces' AND location 'Japan'.
{
  "spec": {
      "filter": "isolationSource == 'feces' && location == 'Japan'"
  }
}

// Operator "or".
{
  "spec" : {
      "filter": "fieldA == 'stringA' || fieldB == 'stringB'"
  }
}
// Example: Fetch BiologicalAssets with isolationSource 'feces' OR location 'Japan'.
{
  "spec": {
      "filter": "isolationSource == 'feces' || location == 'Japan'"
  }
}

  • Filter on contains, startsWith, or endsWith

    // Contains: Retrieve all entries, where the specified field contains matching characters.
// startsWith: Retrieve all entries, where the specified field starts with matching characters.
// endsWith: Retrieve all entries, where the specified field ends with matching characters.
{
  "spec": {
      "filter": "contains(field,'string') || startsWith(field, 'string') || endsWith(field, 'string')"
  }
}
// Example: Fetch Biological Assets where genBankTitle contains "ORF10".
{
  "spec": {
      "filter": "contains(genBankTitle,'ORF10')"
  }
}

  • Filter on empty

    // Empty: Retrieve all entries, where the specified field is NULL.
{
  "spec": {
      "filter": "empty(field)"
  }
}
//Example: Fetch LineListRecords without gender, age, or location data.
{
  "spec": {
      "filter": "empty(gender) && empty(age) && empty(location)"
  }
}

  • Filter on exists

    // Exists: Retrieve all entries where the specified fields exist.
{
  "spec": {
      "filter": "exists(field)"
  }
}
//Example: Fetch LineListRecords with gender, age, or location data.
{
  "spec": {
      "filter": "exists(gender) && exists(age) && exists(symptoms)"
  }
}

  • Filter on matchesRegex

    // matchesRegex: Retrieve all entries where the specified field matches with the regex pattern.
{
  "spec": {
      "filter": "matchesRegex(field, 'regex_string')"
  }
}
//Example: Fetch clinical data of patients whose COVID-19 test results are positive.
{
  "spec": {
      "filter": "matchesRegex(testResults, 'COVID[-]?19:[ ]?[Pp]ositive')"
  }
}

  • Filter on lowerCase

    // lowerCase: Retrieve all entries where the specified field's lowercase matches.
{
  "spec": {
      "filter": "lowerCase(field) == 'string'"
  }
}
//Example: Fetch historical vaccination rates of >= 1 dose of Tdap vaccination for United States teenagers.
{
  "spec": {
      "filter": "contains(lowerCase(vaccineDetails), '>=1 dose tdap vaccination') && location == 'UnitedStates' && lowerCase(vaxView) == 'teenager'"
  }
}

Using EvalMetrics

While the fetch API returns the raw data, the evalmetrics API returns time series data based on the metrics expression you provide. Metrics are instructions you can provide in the request JSON of the evalmetrics API for how to transform the data into time series data. The evalmetrics API will then return the resulting time series data.

The following fields are supported in the request JSON of the evalmetrics API:

IMPORTANT: Limits apply to these fields. See Limits.

  • ids: This is the list of source objects on which you want to evaluate the metrics on. For example: "ids": ["King_Washington_UnitedStates","SanDiego_California_UnitedStates"].
  • expressions: Here you place a list of the metrics that you wish to evaluate. For example, "expressions":["NYT_ConfirmedCases"]. See the EvalMetrics section for a list of supported metrics.
  • start and end: The datetime fields where you can put the start and end dates of the period for which you want to evaluate your metrics. For example, "start":"2020-03-01" and "end":"2020-03-30".
  • interval: The desired interval for the time series output. For example, "interval":"DAY".

IMPORTANT: Depending on the raw data , the available time ranges and frequencies of data vary across data sources. Please refer to the detailed metric documentation in the EvalMetrics section for each data source to set suitable time ranges and interval for your evalmetrics call.

The response JSON for the evalmetrics API consists of the time series data array, the timestamp array, and an array consisting of the fraction of data missing from the time series.

The missing array in the evalmetrics response JSON can be useful to determine whether the requested time range lies partially outside the available range for a particular metric. Each value in this array corresponds to the percentage availability of data in a particular time interval in the timeseries requested. For example, 0 represents no data missing; 100 represents 100% data in the interval missing.

Note the following when using the evalmetrics API:

  1. The evalmetrics request body should be used with the spec key, just as in a fetch request.
  2. The end date field in the JSON request acts as an open interval. That is, if end is set to “2020-04-04”, then only the data upto and including April 3rd is returned. If you need the data for April 4th, then you must set end date to “2020-04-05".
  3. Data from a few locations, such as countries and states, are the aggregate of data from more granular locations, such as counties. For example, in the JHU Dataset, there is no data for the number of confirmed cases in California. This value is computed by adding the number of cases across all counties in California.

Refer to the several examples provided in the evalmetrics API for OutbreakLocation for full details on response JSON.

Limits

Fetch Limits

If limit is not specified in the request body, then by default 2000 rows, or less if the available data is less than 2000 rows, are returned. Most APIs return only a limited number of rows even if the limit setting is higher or is -1. These maximum limits are shown in the table below.

Use the offset parameter to fetch more rows. For example, see Example 6 for Python in LineListRecord.

API Limit
BiblioEntry: /api/1/biblioentry/fetch 2000
PopulationData: /api/1/populationdata/fetch 2000
LaborDetail: /api/1/labordetail/fetch 2000
LineListRecord: /api/1/linelistrecord/fetch 5000
Sequence: /api/1/sequence/fetch 8000
Subsequence: /api/1/subsequence/fetch 8000
BiologicalAsset: /api/1/biologicalasset/fetch 8000

EvalMetrics Limits

Setting Limit
start Start datetime of the time range must be no earlier than 50 years ago from the present day. For example: '1990-01-01'.
end End datetime of the time range must be no earlier than the start datetime specified.
interval DAY, MONTH, and YEAR are supported.
ids The number of ids specified in this array must be less or equal to 10.
expressions The number of expressions specified in this array must be less than or equal to 4.

GetArticleMetadata Limits

Setting Limit
ids The number of ids specified in this array must be less than or equal to 10.

OutbreakLocation

OutbreakLocation stores location data such as countries, provinces, cities, where COVID-19 outbeaks are recorded.

The fetch API provides tabular location data. The evalmetrics API provides time series data, while the getprojectionhistory API provides historical versioned projections of time series data.

OutbreakLocation IDs that should be used in the id field (in the fetch API) and ids field (in the evalmetrics API) are available for download in this Microsoft Excel document.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string The location ID of the country, state or province, and county to fetch for the COVID-19 outbeak. Should be used with the key filter, e.g., "filter": 'id == "Afghanistan"'.
name string Actual name of the country, state or province, and county corresponding to the location ID.
fips string FIPS code for the country, and for the county and county-equivalents in the United States.
lineList LineListRecord List of C3.ai Type LineListRecord objects associated with the location.
assets BiologicalAsset List of C3.ai Type BiologicalAsset objects associated with the location.
diagnoses Diagnosis List of C3.ai Type Diagnosis objects associated with the location.
hospitals Hospital List of C3.ai Type Hospital objects associated with the location.
policy LocationPolicySummary List of C3.ai Type LocationPolicySummary objects associated with the location.
populationData PopulationData List of C3.ai Type PopulationData objects associated with the location.
laborDetail LaborDetail List of C3.ai Type LaborDetail objects associated with the location.
vaccineCoverage VaccineCoverage List of C3.ai Type VaccineCoverage objects associated with the location.
locationExposures LocationExposure List of C3.ai Type LocationExposure objects with this location as locationTarget.
locationExposuresVisited LocationExposure List of C3.ai Type LocationExposure objects with this location as locationVisited.
latestTotalPopulation int Most recent population of the location based on data from The World Bank or the US Census Bureau. Data available at county-level for locations in the United States and country-level globally.
population2019 int Population of the location for the year 2019, based on data from the European Centre for Disease and Control.
populationCDS int Population of the location, based on data from Corona Data Scraper.
hospitalIcuBeds int Total number of hospital intensive care unit (ICU) beds. Available for locations in the United States.
hospitalStaffedBeds int Total number of staffed hospital beds. Available for locations in the United States.
hospitalLicensedBeds int Total number of licensed hospital beds. Available for locations in the United States.
populationOfAllChildren int Most up-to-date total population of all sub-locations (e.g. for all counties in a state) based on available demographic data. Available for locations in the United States.
latestLaborForce int Most up-to-date labor force population of the location based on available Bureau of Labor Statistics data. Available for county locations in the United States.
latestEmployedPopulation int Most up-to-date employed population of the location based on available Bureau of Labor Statistics data. Available for county locations in the United States.
latestUnemployedPopulation int Most up-to-date unemployed population of the location based on available Bureau of Labor Statistics data. Available for county locations in the United States.
latestUnemploymentRate double Most up-to-date unemployment rate of the location based on available Bureau of Labor Statistics data, in percent. Available for county locations in the United States.
laborForceOfAllChildren int Most up-to-date labor force population of all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. Available for US state- and country-level locations.
employedPopulationOfAllChildren int Most up-to-date employed population of all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. Available for US state- and country-level locations.
unemployedPopulationOfAllChildren int Most up-to-date unemployed population of all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. Available for US state- and country-level locations.
unemploymentRateOfAllChildren double Most up-to-date unemployment rate, in percent, over all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. This value is unemployedPopulationOfAllChildren divided by laborForceOfAllChildren, in percent. Available for US state- and country-level locations.
elementarySchoolCount int Total number of elementary schools. Available for locations in South Korea.
kindergartenCount int Total number of kindergartens. Available for locations in South Korea.
universityCount int Total number of universities. Available for locations in South Korea.
nursingHomeCount int Total number of nursing homes. Available for locations in South Korea.
elderlyPopulationRatio double Proportion of population that is elderly, as percent (0-100). Available for locations in South Korea.
elderlyAloneRatio double Proportion of households that are elderly people living alone, as percent (0-100). Available for locations in South Korea.
publicHealthCareCenterBeds int Total number of hospital beds available in public facilities. Available for locations in India.

Examples (Click on the arrows to expand)

The following examples show how to fetch COVID-19 outbreak location data using this API.

Fetch facts about Germany

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/fetch

Request JSON:

{
  "spec": {
    "filter": "id == 'Germany'"
  }
}



Response JSON:

{
  "objs": [
    {
      "hospitalPrediction": {
        "timestamp": "2019-01-01T00:00:00"
      },
      "locationType": "country",
      "populationCDS": 83149300,
      "location": {
        "value": {
          "id": "Germany"
        },
        "timestamp": "2020-05-13T00:00:00Z"
      },
      "fips": {
        "id": "DE"
      },
      "population2018": 82927922,
      "id": "Germany",
      "name": "Germany",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-05T21:00:11Z",
        "createdBy": "dataloader",
        "updated": "2020-05-23T20:22:40Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-23T23:18:02Z",
        "sourceFile": "CanonicalEuropeanCenterForDiseaseControl.csv",
        "fetchInclude": "[]",
        "fetchType": "OutbreakLocation"
      },
      "version": 3670029,
      "typeIdent": "EP_LOC"
    }
  ],
  "count": 1,
  "hasMore": false
}
Fetch facts about Beijing, China (request example only)

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/fetch

Request JSON:

{
  "spec": {
    "filter": "id == 'Beijing_China'"
  }
}
Fetch facts about Santa Clara County, California, United States (request example only)

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/fetch

Request JSON:

{
  "spec": {
    "filter": "id == 'SantaClara_California_UnitedStates'"
  }
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/outbreaklocation/fetch
https://api.c3.ai/covid/api/1/outbreaklocation/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

EvalMetrics

The following tables show the available time series metrics from each data source that can be evaluated using EvalMetrics. Use the expressions from the Metric column in the expressions field of the request JSON of the evalmetrics API. For example, "expressions":["JHU_ConfirmedDeaths"]. Please click on the arrows to expand examples and see request and response JSONs.

Johns Hopkins University: COVID-19 Data Repository

Metrics

Daily case, death, and recovery counts available at:

  • United States: country, state or territory, and county level
  • Global: country and province level
Metric Description
JHU_ConfirmedCases Cumulative total confirmed cases.
JHU_ConfirmedDeaths Cumulative total confirmed deaths.
JHU_ConfirmedRecoveries Cumulative total confirmed recoveries.

Examples

Example 1: Total number of confirmed cases in the United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["UnitedStates"],
    "expressions":["JHU_ConfirmedCases"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "UnitedStates": {
        "JHU_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
                "2020-02-26T00:00:00",
                "2020-02-27T00:00:00",
                "2020-02-28T00:00:00",
                "2020-02-29T00:00:00",
                "2020-03-01T00:00:00",
                "2020-03-02T00:00:00",
                "2020-03-03T00:00:00",
                "2020-03-04T00:00:00",
                "2020-03-05T00:00:00",
                "2020-03-06T00:00:00",
                "2020-03-07T00:00:00",
                "2020-03-08T00:00:00",
                "2020-03-09T00:00:00",
                "2020-03-10T00:00:00",
                "2020-03-11T00:00:00",
                "2020-03-12T00:00:00",
                "2020-03-13T00:00:00",
                "2020-03-14T00:00:00",
                "2020-03-15T00:00:00",
                "2020-03-16T00:00:00",
                "2020-03-17T00:00:00",
                "2020-03-18T00:00:00",
                "2020-03-19T00:00:00",
                "2020-03-20T00:00:00",
                "2020-03-21T00:00:00",
                "2020-03-22T00:00:00",
                "2020-03-23T00:00:00",
                "2020-03-24T00:00:00",
                "2020-03-25T00:00:00",
                "2020-03-26T00:00:00",
                "2020-03-27T00:00:00",
                "2020-03-28T00:00:00",
                "2020-03-29T00:00:00"
            ],
            "data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                1,
                1,
                2,
                2,
                5,
                5,
                5,
                5,
                5,
                7,
                8,
                8,
                11,
                11,
                11,
                11,
                11,
                11,
                11,
                11,
                12,
                12,
                13,
                13,
                13,
                13,
                13,
                13,
                13,
                13,
                15,
                15,
                15,
                15,
                15,
                15,
                16,
                16,
                24,
                30,
                53,
                73,
                104,
                172,
                217,
                336,
                450,
                514,
                708,
                1105,
                1557,
                2147,
                2857,
                2918,
                4307,
                6096,
                8873,
                14024,
                19230,
                25627,
                33746,
                43667,
                53740,
                65778,
                83836,
                101657,
                121478,
                140886
            ],
            "missing": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ],
            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
            "end": "2020-03-30T00:00:00"
        }
    }
  }
}
Example 2: Total number of confirmed deaths in California

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["California_UnitedStates"],
    "expressions":["JHU_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "California_UnitedStates": {
        "JHU_ConfirmedDeaths": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
                "2020-02-26T00:00:00",
                "2020-02-27T00:00:00",
                "2020-02-28T00:00:00",
                "2020-02-29T00:00:00",
                "2020-03-01T00:00:00",
                "2020-03-02T00:00:00",
                "2020-03-03T00:00:00",
                "2020-03-04T00:00:00",
                "2020-03-05T00:00:00",
                "2020-03-06T00:00:00",
                "2020-03-07T00:00:00",
                "2020-03-08T00:00:00",
                "2020-03-09T00:00:00",
                "2020-03-10T00:00:00",
                "2020-03-11T00:00:00",
                "2020-03-12T00:00:00",
                "2020-03-13T00:00:00",
                "2020-03-14T00:00:00",
                "2020-03-15T00:00:00",
                "2020-03-16T00:00:00",
                "2020-03-17T00:00:00",
                "2020-03-18T00:00:00",
                "2020-03-19T00:00:00",
                "2020-03-20T00:00:00",
                "2020-03-21T00:00:00",
                "2020-03-22T00:00:00",
                "2020-03-23T00:00:00",
                "2020-03-24T00:00:00",
                "2020-03-25T00:00:00",
                "2020-03-26T00:00:00",
                "2020-03-27T00:00:00",
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            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
            "end": "2020-03-30T00:00:00"
        }
    }
  }
}
Example 3: Total number of confirmed recoveries in Hubei, China

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Hubei_China"],
    "expressions":["JHU_ConfirmedRecoveries"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "Hubei_China": {
        "JHU_ConfirmedRecoveries": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
                "2020-02-26T00:00:00",
                "2020-02-27T00:00:00",
                "2020-02-28T00:00:00",
                "2020-02-29T00:00:00",
                "2020-03-01T00:00:00",
                "2020-03-02T00:00:00",
                "2020-03-03T00:00:00",
                "2020-03-04T00:00:00",
                "2020-03-05T00:00:00",
                "2020-03-06T00:00:00",
                "2020-03-07T00:00:00",
                "2020-03-08T00:00:00",
                "2020-03-09T00:00:00",
                "2020-03-10T00:00:00",
                "2020-03-11T00:00:00",
                "2020-03-12T00:00:00",
                "2020-03-13T00:00:00",
                "2020-03-14T00:00:00",
                "2020-03-15T00:00:00",
                "2020-03-16T00:00:00",
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                "2020-03-18T00:00:00",
                "2020-03-19T00:00:00",
                "2020-03-20T00:00:00",
                "2020-03-21T00:00:00",
                "2020-03-22T00:00:00",
                "2020-03-23T00:00:00",
                "2020-03-24T00:00:00",
                "2020-03-25T00:00:00",
                "2020-03-26T00:00:00",
                "2020-03-27T00:00:00",
                "2020-03-28T00:00:00",
                "2020-03-29T00:00:00"
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            "data": [
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            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
            "end": "2020-03-30T00:00:00"
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}
Example 4: Total number of confirmed cases, deaths, and recoveries in Santa Clara, California

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["SantaClara_California_UnitedStates"],
    "expressions":["JHU_ConfirmedCases","JHU_ConfirmedDeaths","JHU_ConfirmedRecoveries"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "SantaClara_California_UnitedStates": {
        "JHU_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
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                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
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                "2020-01-14T00:00:00",
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        "JHU_ConfirmedRecoveries": {
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}
Example 5: Total number of confirmed cases in France and Germany

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["France","Germany"],
    "expressions":["JHU_ConfirmedCases"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "France": {
        "JHU_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
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            "start": "2020-01-01T00:00:00",
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        }
    }
  }
}
Example 6: Total number of confirmed Cases, Deaths, and Recoveries in King County, Washington, and San Diego, California

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["King_Washington_UnitedStates","SanDiego_California_UnitedStates"],
    "expressions":["JHU_ConfirmedCases","JHU_ConfirmedDeaths","JHU_ConfirmedRecoveries"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "King_Washington_UnitedStates": {
        "JHU_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
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}

COVID Tracking Project

Metrics

Daily case, death, hospitalization, and testing counts available at country and province level globally.

Metric Description
CovidTrackingProject_ConfirmedCases Cumulative total confirmed cases.
CovidTrackingProject_ConfirmedDeaths Cumulative total confirmed deaths.
CovidTrackingProject_ConfirmedHospitalizations Cumulative total confirmed hospitalizations.
CovidTrackingProject_NegativeTests Cumulative total negative COVID-19 tests.
CovidTrackingProject_PendingTests Non-cumulative daily pending COVID-19 tests.

Examples

Example 1: Total number of confirmed cases in Washington, United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Washington_UnitedStates"],
    "expressions":["CovidTrackingProject_ConfirmedCases"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "Washington_UnitedStates": {
        "CovidTrackingProject_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
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                "2020-01-08T00:00:00",
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                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
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                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
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                "2020-03-08T00:00:00",
                "2020-03-09T00:00:00",
                "2020-03-10T00:00:00",
                "2020-03-11T00:00:00",
                "2020-03-12T00:00:00",
                "2020-03-13T00:00:00",
                "2020-03-14T00:00:00",
                "2020-03-15T00:00:00",
                "2020-03-16T00:00:00",
                "2020-03-17T00:00:00",
                "2020-03-18T00:00:00",
                "2020-03-19T00:00:00",
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            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
            "end": "2020-04-04T00:00:00"
        }
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}
Example 2: Total number of confirmed deaths in Washington, United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Washington_UnitedStates"],
    "expressions":["CovidTrackingProject_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "Washington_UnitedStates": {
        "CovidTrackingProject_ConfirmedDeaths": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
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                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
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                "2020-02-25T00:00:00",
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                "2020-03-01T00:00:00",
                "2020-03-02T00:00:00",
                "2020-03-03T00:00:00",
                "2020-03-04T00:00:00",
                "2020-03-05T00:00:00",
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                "2020-03-07T00:00:00",
                "2020-03-08T00:00:00",
                "2020-03-09T00:00:00",
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                "2020-03-30T00:00:00",
                "2020-03-31T00:00:00",
                "2020-04-01T00:00:00",
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                "2020-04-03T00:00:00"
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            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
            "end": "2020-04-04T00:00:00"
        }
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}
Example 3: Total number of confirmed hospitalizations in Washington, United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Washington_UnitedStates"],
    "expressions":["CovidTrackingProject_ConfirmedHospitalizations"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "Washington_UnitedStates": {
        "CovidTrackingProject_ConfirmedHospitalizations": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
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                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
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                "2020-02-13T00:00:00",
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                "2020-02-15T00:00:00",
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                "2020-03-30T00:00:00",
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            "data": [
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            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
            "end": "2020-04-04T00:00:00"
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}
Example 4: Total number of negative test results in Washington, United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Washington_UnitedStates"],
    "expressions":["CovidTrackingProject_NegativeTests"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "Washington_UnitedStates": {
        "CovidTrackingProject_NegativeTests": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
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                "2020-01-19T00:00:00",
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                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
                "2020-02-26T00:00:00",
                "2020-02-27T00:00:00",
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                "2020-03-07T00:00:00",
                "2020-03-08T00:00:00",
                "2020-03-09T00:00:00",
                "2020-03-10T00:00:00",
                "2020-03-11T00:00:00",
                "2020-03-12T00:00:00",
                "2020-03-13T00:00:00",
                "2020-03-14T00:00:00",
                "2020-03-15T00:00:00",
                "2020-03-16T00:00:00",
                "2020-03-17T00:00:00",
                "2020-03-18T00:00:00",
                "2020-03-19T00:00:00",
                "2020-03-20T00:00:00",
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                "2020-03-24T00:00:00",
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            "interval": "DAY",
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            "end": "2020-04-04T00:00:00"
        }
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}
Example 5: Total number of pending test results in Washington, United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Washington_UnitedStates"],
    "expressions":["CovidTrackingProject_PendingTests"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "Washington_UnitedStates": {
        "CovidTrackingProject_PendingTests": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
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                "2020-01-16T00:00:00",
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                "2020-01-18T00:00:00",
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                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
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                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
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                "2020-02-10T00:00:00",
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                "2020-02-15T00:00:00",
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}

European Centre for Disease Prevention and Control: Situation Update Worldwide

Metrics

Daily cumulative case and death counts available at country level globally.

NOTE: ECDC cumulative counts are calculated relative to the "start" date entered. To retrieve the all-time cumulative total, use a "start" date of "2020-01-01".

Metric Description
ECDC_ConfirmedCases Cumulative total confirmed cases.
ECDC_ConfirmedDeaths Cumulative total confirmed deaths.
ECDC_PerDay_Cumulative14DaysPer100000 Cumulative 14-day case counts per 100,000 people.

Examples

Example 1: Total number of confirmed cases in Italy

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Italy"],
    "expressions":["ECDC_ConfirmedCases"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "Italy": {
        "ECDC_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
                "2020-02-26T00:00:00",
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                "2020-03-10T00:00:00",
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                "2020-04-01T00:00:00",
                "2020-04-02T00:00:00",
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            "data": [
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            "interval": "DAY",
            "start": "2020-01-01T00:00:00",
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    }
  }
}
Example 2: Total number of confirmed deaths in United Kingdom

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["UnitedKingdom"],
    "expressions":["ECDC_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "UnitedKingdom": {
        "ECDC_ConfirmedDeaths": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
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                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
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                "2020-01-31T00:00:00",
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}

The New York Times: Coronavirus (Covid-19) Data in the United States

Metrics

Daily cumulative case and death counts available at:

  • United States: country, state or territory, and county level
  • Global: country and province level
Metric Description
NYT_ConfirmedCases Cumulative total confirmed cases.
NYT_ConfirmedDeaths Cumulative total confirmed deaths.

Examples

Example 1: Total number of confirmed cases in the United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["UnitedStates"],
    "expressions":["NYT_ConfirmedCases"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "UnitedStates": {
        "NYT_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
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                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
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                "2020-02-26T00:00:00",
                "2020-02-27T00:00:00",
                "2020-02-28T00:00:00",
                "2020-02-29T00:00:00",
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}
Example 2: Total number of confirmed deaths in New York

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["NewYork_UnitedStates"],
    "expressions":["NYT_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "NewYork_UnitedStates": {
        "NYT_ConfirmedDeaths": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
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}
Example 3: Total number of confirmed cases and deaths in Cook County, Illinois

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Cook_Illinois_UnitedStates"],
    "expressions":["NYT_ConfirmedCases","NYT_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "Cook_Illinois_UnitedStates": {
        "NYT_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
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}
Example 4: Total number of confirmed cases and deaths in Pennsylvania and Ohio, United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Pennsylvania_UnitedStates","Ohio_UnitedStates"],
    "expressions":["NYT_ConfirmedCases","NYT_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-03-30"
  }
}



Response JSON:

{
  "result": {
    "Ohio_UnitedStates": {
        "NYT_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 89,
            "dates": [
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}

The New York Times: All-Cause Mortality

This dataset provides data on the number of deaths from all causes for a given location to present the full impact of the Covid-19 pandemic, since official Covid-19 death tolls often cannot account for people who have not been tested and those who died at home. All-cause mortality can supplement Covid-19 death counts and provide a better understanding of the true toll of the pandemic.

Metrics

Metric Description
NYT_AllCausesDeathsWeekly_Deaths_AllCauses All-cause death case count, updated weekly.
NYT_AllCausesDeathsWeekly_Excess_Deaths Difference between all-cause deaths and expected deaths, updated weekly.
NYT_AllCausesDeathsWeekly_Expected_Deaths_AllCauses Expected all-cause deaths based on NYT linear model, updated weekly.
NYT_AllCausesDeathsMonthly_Deaths_AllCauses All-cause death case count, updated monthly.
NYT_AllCausesDeathsMonthly_Excess_Deaths Difference between all-cause deaths and expected deaths, updated monthly.
NYT_AllCausesDeathsMonthly_Expected_Deaths_AllCauses Expected all-cause deaths based on NYT linear model, updated monthly.

Examples

Example: Timeseries of excess deaths in Austria and Denmark in April 2020 (case count updated weekly)

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Austria", "Denmark"],
    "expressions": ["NYT_AllCausesDeathsWeekly_Excess_Deaths"],
    "start": "2020-04-01",
    "end": "2020-05-01",
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
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      "NYT_AllCausesDeathsWeekly_Excess_Deaths": {
        "type": "MaterializedTimeseriesDouble",
        "count": 30,
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          "2020-04-01T00:00:00",
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        "start": "2020-04-01T00:00:00",
        "end": "2020-05-01T00:00:00"
      }
    },
    "Denmark": {
      "NYT_AllCausesDeathsWeekly_Excess_Deaths": {
        "type": "MaterializedTimeseriesDouble",
        "count": 30,
        "dates": [
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          "2020-04-02T00:00:00",
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          "2020-04-28T00:00:00",
          "2020-04-29T00:00:00",
          "2020-04-30T00:00:00"
        ],
        "data": [
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-04-01T00:00:00",
        "end": "2020-05-01T00:00:00"
      }
    },
    "UnitedStates": {
      "NYT_AllCausesDeathsWeekly_Excess_Deaths": {
        "type": "MaterializedTimeseriesDouble",
        "count": 30,
        "dates": [
          "2020-04-01T00:00:00",
          "2020-04-02T00:00:00",
          "2020-04-03T00:00:00",
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-04-01T00:00:00",
        "end": "2020-05-01T00:00:00"
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    }
  }
}

Centers for Disease Control and Prevention: Weekly Updates by Select Demographic Characteristics

Metrics

This dataset provides weekly national-level COVID-19 provisional death counts by age and sex for the United States, starting from Feb 1, 2020.

Weekly death counts are recorded as 0 if the value is less than 10.

The table below contains example metrics. The full list of metrics is available for download in this Microsoft Excel document.

Metric Description
AllSex_Under1_CovidDeaths Number of COVID deaths under age 1.
AllSex_Under1_TotalDeaths Number of total deaths of select causes under age 1.
Female_65_74_CovidDeaths Number of COVID deaths from age 65 to 74, female.
Female_65_74_TotalDeaths Number of total deaths by COVID-19, pneumonia, and influenza from age 65 to 74, female.
Male_85AndOver_CovidDeaths Number of COVID deaths with age 85 and over, male.
Male_85AndOver_TotalDeaths Number of total deaths of select causes with age 85 and over, male.

Examples

Example: Compare COVID and total death counts for individuals age 85 and older in the United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["UnitedStates"], 
    "expressions": ["AllSex_85AndOver_CovidDeaths", "AllSex_85AndOver_TotalDeaths"], 
    "start": "2020-07-01", 
    "end": "2020-07-23", 
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
    "UnitedStates": {
      "AllSex_85AndOver_TotalDeaths": {
        "type": "MaterializedTimeseriesDouble",
        "count": 22,
        "dates": [
          "2020-07-01T00:00:00",
          "2020-07-02T00:00:00",
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          "2020-07-13T00:00:00",
          "2020-07-14T00:00:00",
          "2020-07-15T00:00:00",
          "2020-07-16T00:00:00",
          "2020-07-17T00:00:00",
          "2020-07-18T00:00:00",
          "2020-07-19T00:00:00",
          "2020-07-20T00:00:00",
          "2020-07-21T00:00:00",
          "2020-07-22T00:00:00"
        ],
        "data": [
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        "missing": [
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          0,
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        ],
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-07-01T00:00:00",
        "end": "2020-07-23T00:00:00"
      },
      "AllSex_85AndOver_CovidDeaths": {
        "type": "MaterializedTimeseriesDouble",
        "count": 22,
        "dates": [
          "2020-07-01T00:00:00",
          "2020-07-02T00:00:00",
          "2020-07-03T00:00:00",
          "2020-07-04T00:00:00",
          "2020-07-05T00:00:00",
          "2020-07-06T00:00:00",
          "2020-07-07T00:00:00",
          "2020-07-08T00:00:00",
          "2020-07-09T00:00:00",
          "2020-07-10T00:00:00",
          "2020-07-11T00:00:00",
          "2020-07-12T00:00:00",
          "2020-07-13T00:00:00",
          "2020-07-14T00:00:00",
          "2020-07-15T00:00:00",
          "2020-07-16T00:00:00",
          "2020-07-17T00:00:00",
          "2020-07-18T00:00:00",
          "2020-07-19T00:00:00",
          "2020-07-20T00:00:00",
          "2020-07-21T00:00:00",
          "2020-07-22T00:00:00"
        ],
        "data": [
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-07-01T00:00:00",
        "end": "2020-07-23T00:00:00"
      }
    }
  }
}

COVID Racial Data Tracker

Metrics

This dataset provides daily state-level race-specific and ethnicity-specific COVID-19 case and death count data, available from April 2, 2020 and updated twice a week.

Metric Description
CTP_CovidDeaths_AIAN Number of COVID-19 deaths for race: American Indian or Alaska Native.
CTP_CovidDeaths_Asian Number of COVID-19 deaths for race: Asian.
CTP_CovidDeaths_Black Number of COVID-19 deaths for race: Black.
CTP_CovidDeaths_EthnicityHispanic Number of COVID-19 deaths for ethnicity: Hispanic.
CTP_CovidDeaths_EthnicityNonHispanic Number of COVID-19 deaths for ethnicity: Non-Hispanic.
CTP_CovidDeaths_EthnicityUnknown Number of COVID-19 deaths for ethnicity: Unknown.
CTP_CovidDeaths_LatinX Number of COVID-19 deaths for race: LatinX.
CTP_CovidDeaths_Multiracial Number of COVID-19 deaths for race: Multiracial.
CTP_CovidDeaths_NHPI Number of COVID-19 deaths for race: Native Hawaiian and Pacific Islander.
CTP_CovidDeaths_Other Number of COVID-19 deaths for race: Other.
CTP_CovidDeaths_Unknown Number of COVID-19 deaths for race: Unknown.
CTP_CovidDeaths_White Number of COVID-19 deaths for race: White.
CTP_CovidCases_AIAN Number of COVID-19 cases for race: American Indian or Alaska Native.
CTP_CovidCases_Asian Number of COVID-19 cases for race: Asian.
CTP_CovidCases_Black Number of COVID-19 cases for race: Black.
CTP_CovidCases_EthnicityHispanic Number of COVID-19 cases for ethnicity: Hispanic.
CTP_CovidCases_EthnicityNonHispanic Number of COVID-19 cases for ethnicity: Non-Hispanic.
CTP_CovidCases_EthnicityUnknown Number of COVID-19 cases for ethnicity: Unknown.
CTP_CovidCases_LatinX Number of COVID-19 cases for race: LatinX.
CTP_CovidCases_Multiracial Number of COVID-19 cases for race: Multiracial.
CTP_CovidCases_NHPI Number of COVID-19 cases for race: Native Hawaiian and Pacific Islander.
CTP_CovidCases_Other Number of COVID-19 cases for race: Other.
CTP_CovidCases_Unknown Number of COVID-19 cases for race: Unknown.
CTP_CovidCases_White Number of COVID-19 cases for race: White.

Examples

Example: Compare COVID-19 death counts for Hispanic and non-Hispanic populations in Texas, United States in May, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Texas_UnitedStates"],
    "expressions": ["CTP_CovidDeaths_EthnicityHispanic", "CTP_CovidDeaths_EthnicityNonHispanic"],
    "start": "2020-05-01",
    "end": "2020-06-01",
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
    "Texas_UnitedStates": {
      "CovidDeaths_EthnicityNonHispanic": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-05-01T00:00:00",
          "2020-05-02T00:00:00",
          "2020-05-03T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
          "2020-05-07T00:00:00",
          "2020-05-08T00:00:00",
          "2020-05-09T00:00:00",
          "2020-05-10T00:00:00",
          "2020-05-11T00:00:00",
          "2020-05-12T00:00:00",
          "2020-05-13T00:00:00",
          "2020-05-14T00:00:00",
          "2020-05-15T00:00:00",
          "2020-05-16T00:00:00",
          "2020-05-17T00:00:00",
          "2020-05-18T00:00:00",
          "2020-05-19T00:00:00",
          "2020-05-20T00:00:00",
          "2020-05-21T00:00:00",
          "2020-05-22T00:00:00",
          "2020-05-23T00:00:00",
          "2020-05-24T00:00:00",
          "2020-05-25T00:00:00",
          "2020-05-26T00:00:00",
          "2020-05-27T00:00:00",
          "2020-05-28T00:00:00",
          "2020-05-29T00:00:00",
          "2020-05-30T00:00:00",
          "2020-05-31T00:00:00"
        ],
        "data": [
          172.0,
          172.0,
          319.0,
          319.0,
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        "missing": [
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        "unit": {
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-05-01T00:00:00",
        "end": "2020-06-01T00:00:00"
      },
      "CovidDeaths_EthnicityHispanic": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-05-01T00:00:00",
          "2020-05-02T00:00:00",
          "2020-05-03T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
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          "2020-05-09T00:00:00",
          "2020-05-10T00:00:00",
          "2020-05-11T00:00:00",
          "2020-05-12T00:00:00",
          "2020-05-13T00:00:00",
          "2020-05-14T00:00:00",
          "2020-05-15T00:00:00",
          "2020-05-16T00:00:00",
          "2020-05-17T00:00:00",
          "2020-05-18T00:00:00",
          "2020-05-19T00:00:00",
          "2020-05-20T00:00:00",
          "2020-05-21T00:00:00",
          "2020-05-22T00:00:00",
          "2020-05-23T00:00:00",
          "2020-05-24T00:00:00",
          "2020-05-25T00:00:00",
          "2020-05-26T00:00:00",
          "2020-05-27T00:00:00",
          "2020-05-28T00:00:00",
          "2020-05-29T00:00:00",
          "2020-05-30T00:00:00",
          "2020-05-31T00:00:00"
        ],
        "data": [
          59.0,
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        "missing": [
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        "unit": {
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-05-01T00:00:00",
        "end": "2020-06-01T00:00:00"
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}

World Health Organization: Situation Reports

Metrics

Daily cumulative case and death counts available at:

  • Global: country level
  • China: province level prior to March 15, 2020
Metric Description
WHO_ConfirmedCases Cumulative total confirmed cases.
WHO_ConfirmedDeaths Cumulative total confirmed deaths.

Examples

Example 1: Total number of confirmed cases in South Korea

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["KoreaSouth"],
    "expressions":["WHO_ConfirmedCases"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "KoreaSouth": {
        "WHO_ConfirmedCases": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
                "2020-01-03T00:00:00",
                "2020-01-04T00:00:00",
                "2020-01-05T00:00:00",
                "2020-01-06T00:00:00",
                "2020-01-07T00:00:00",
                "2020-01-08T00:00:00",
                "2020-01-09T00:00:00",
                "2020-01-10T00:00:00",
                "2020-01-11T00:00:00",
                "2020-01-12T00:00:00",
                "2020-01-13T00:00:00",
                "2020-01-14T00:00:00",
                "2020-01-15T00:00:00",
                "2020-01-16T00:00:00",
                "2020-01-17T00:00:00",
                "2020-01-18T00:00:00",
                "2020-01-19T00:00:00",
                "2020-01-20T00:00:00",
                "2020-01-21T00:00:00",
                "2020-01-22T00:00:00",
                "2020-01-23T00:00:00",
                "2020-01-24T00:00:00",
                "2020-01-25T00:00:00",
                "2020-01-26T00:00:00",
                "2020-01-27T00:00:00",
                "2020-01-28T00:00:00",
                "2020-01-29T00:00:00",
                "2020-01-30T00:00:00",
                "2020-01-31T00:00:00",
                "2020-02-01T00:00:00",
                "2020-02-02T00:00:00",
                "2020-02-03T00:00:00",
                "2020-02-04T00:00:00",
                "2020-02-05T00:00:00",
                "2020-02-06T00:00:00",
                "2020-02-07T00:00:00",
                "2020-02-08T00:00:00",
                "2020-02-09T00:00:00",
                "2020-02-10T00:00:00",
                "2020-02-11T00:00:00",
                "2020-02-12T00:00:00",
                "2020-02-13T00:00:00",
                "2020-02-14T00:00:00",
                "2020-02-15T00:00:00",
                "2020-02-16T00:00:00",
                "2020-02-17T00:00:00",
                "2020-02-18T00:00:00",
                "2020-02-19T00:00:00",
                "2020-02-20T00:00:00",
                "2020-02-21T00:00:00",
                "2020-02-22T00:00:00",
                "2020-02-23T00:00:00",
                "2020-02-24T00:00:00",
                "2020-02-25T00:00:00",
                "2020-02-26T00:00:00",
                "2020-02-27T00:00:00",
                "2020-02-28T00:00:00",
                "2020-02-29T00:00:00",
                "2020-03-01T00:00:00",
                "2020-03-02T00:00:00",
                "2020-03-03T00:00:00",
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            "interval": "DAY",
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}
Example 2: Total number of confirmed deaths in France

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["France"],
    "expressions":["WHO_ConfirmedDeaths"],
    "interval":"DAY",
    "start":"2020-01-01",
    "end":"2020-04-04"
  }
}



Response JSON:

{
  "result": {
    "France": {
        "WHO_ConfirmedDeaths": {
            "type": "MaterializedTimeseriesDouble",
            "count": 94,
            "dates": [
                "2020-01-01T00:00:00",
                "2020-01-02T00:00:00",
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}

Dipartimento della Protezione Civile – Emergenza Coronavirus: La Risposta Nazionale

Metrics

Daily counts available for Italy at country, region, and province level.

Metric Description
ITA_Active Non-cumulative active cases.
ITA_ActiveDelta Change in active cases from previous day.
ITA_ConfirmedCases Cumulative total confirmed cases.
ITA_ConfirmedDelta Change in cumulative total confirmed cases from previous day.
ITA_Deaths Cumulative total deaths.
ITA_HomeConfined Non-cumulative count of individuals in home confinement.
ITA_Hospitalized Non-cumulative count of hospitalized patients.
ITA_InIcuCurrently Non-cumulative count of patients in intensive care unit (ICU).
ITA_Recovered Cumulative total recoveries.
ITA_TotalPeopleTested Cumulative total individuals tested.
ITA_TotalTests Cumulative total tests.

Examples

Example: Active cases, hospitalizations, recoveries, and deaths over time in Tuscany, Italy

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": [
      "Tuscany_Italy"
    ],
    "expressions": [
      "ITA_Active",
      "ITA_Hospitalized",
      "ITA_Recovered",
      "ITA_Deaths"
    ],
    "start": "2020-01-01",
    "end": "2020-05-12",
    "interval": "DAY"
  }
}



Response JSON:

{
  "result": {
    "Tuscany_Italy": {
      "ITA_Active": {
        "type": "MaterializedTimeseriesDouble",
        "count": 132,
        "dates": [
          "2020-01-01T00:00:00",
          "2020-01-02T00:00:00",
          "2020-01-03T00:00:00",
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        "type": "MaterializedTimeseriesDouble",
        "count": 132,
        "dates": [
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          "2020-01-19T00:00:00",
          "2020-01-20T00:00:00",
          "2020-01-21T00:00:00",
          "2020-01-22T00:00:00",
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          "2020-01-24T00:00:00",
          "2020-01-25T00:00:00",
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          "2020-02-12T00:00:00",
          "2020-02-13T00:00:00",
          "2020-02-14T00:00:00",
          "2020-02-15T00:00:00",
          "2020-02-16T00:00:00",
          "2020-02-17T00:00:00",
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          "2020-02-19T00:00:00",
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          "2020-02-23T00:00:00",
          "2020-02-24T00:00:00",
          "2020-02-25T00:00:00",
          "2020-02-26T00:00:00",
          "2020-02-27T00:00:00",
          "2020-02-28T00:00:00",
          "2020-02-29T00:00:00",
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          "2020-03-02T00:00:00",
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          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
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          "2020-03-11T00:00:00",
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          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00",
          "2020-04-01T00:00:00",
          "2020-04-02T00:00:00",
          "2020-04-03T00:00:00",
          "2020-04-04T00:00:00",
          "2020-04-05T00:00:00",
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          "2020-04-20T00:00:00",
          "2020-04-21T00:00:00",
          "2020-04-22T00:00:00",
          "2020-04-23T00:00:00",
          "2020-04-24T00:00:00",
          "2020-04-25T00:00:00",
          "2020-04-26T00:00:00",
          "2020-04-27T00:00:00",
          "2020-04-28T00:00:00",
          "2020-04-29T00:00:00",
          "2020-04-30T00:00:00",
          "2020-05-01T00:00:00",
          "2020-05-02T00:00:00",
          "2020-05-03T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
          "2020-05-07T00:00:00",
          "2020-05-08T00:00:00",
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        "missing": [
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        ],
        "unit": {
          "id": "person"
        },
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-01-01T00:00:00",
        "end": "2020-05-12T00:00:00"
      }
    }
  }
}

COVID-19 India

Metrics

Daily cumulative case counts available for India at country and state or territory level.

NOTE: India cumulative counts are calculated relative to the "start" date entered. To retrieve the all-time cumulative total, use a "start" date of "2020-01-01".

Metric Description
India_ConfirmedCases Cumulative total cases.
India_RecoveredCases Cumulative total recoveries.
India_DeceasedCases Cumulative total deaths.
India_TotalTested Cumulative total individuals tested.
India_TotalTestedNegative Cumulative total individuals tested negative.

Data Science for COVID-19: South Korea Dataset

Metrics

Daily cumulative case counts available for South Korea at country and province level.

Metric Description
KCDC_ConfirmedCases Cumulative total confirmed cases.
KCDC_ConfirmedDeaths Cumulative total confirmed deaths.
KCDC_ConfirmedRecoveries Cumulative total confirmed recoveries.
KCDC_ConfirmedNegativeTests Cumulative total confirmed negative tests.
KCDC_ConfirmedTested Cumulative total individuals tested.
KCDC_ConfirmedCases_0s Cumulative total confirmed cases among individuals ages 0-9.
KCDC_ConfirmedCases_10s Cumulative total confirmed cases among individuals ages 10-19.
KCDC_ConfirmedCases_20s Cumulative total confirmed cases among individuals ages 20-29.
KCDC_ConfirmedCases_30s Cumulative total confirmed cases among individuals ages 30-39.
KCDC_ConfirmedCases_40s Cumulative total confirmed cases among individuals ages 40-49.
KCDC_ConfirmedCases_50s Cumulative total confirmed cases among individuals ages 50-59.
KCDC_ConfirmedCases_60s Cumulative total confirmed cases among individuals ages 60-69.
KCDC_ConfirmedCases_70s Cumulative total confirmed cases among individuals ages 70-79.
KCDC_ConfirmedCases_80s Cumulative total confirmed cases among individuals ages 80-89.
KCDC_ConfirmedDeaths_0s Cumulative total confirmed deaths among individuals ages 0-9.
KCDC_ConfirmedDeaths_10s Cumulative total confirmed deaths among individuals ages 10-19.
KCDC_ConfirmedDeaths_20s Cumulative total confirmed deaths among individuals ages 20-29.
KCDC_ConfirmedDeaths_30s Cumulative total confirmed deaths among individuals ages 30-39.
KCDC_ConfirmedDeaths_40s Cumulative total confirmed deaths among individuals ages 40-49.
KCDC_ConfirmedDeaths_50s Cumulative total confirmed deaths among individuals ages 50-59.
KCDC_ConfirmedDeaths_60s Cumulative total confirmed deaths among individuals ages 60-69.
KCDC_ConfirmedDeaths_70s Cumulative total confirmed deaths among individuals ages 70-79.
KCDC_ConfirmedDeaths_80s Cumulative total confirmed deaths among individuals ages 80-89.
KCDC_ConfirmedCases_Male Cumulative total confirmed cases among males.
KCDC_ConfirmedCases_Female Cumulative total confirmed cases among females.
KCDC_ConfirmedDeaths_Male Cumulative total confirmed deaths among males.
KCDC_ConfirmedDeaths_Female Cumulative total confirmed deaths among females.

Corona Data Scraper: COVID-19 Coronavirus Case Data

Metrics

Daily counts available at a country, province, county, and city level globally.

Metric Description
CDS_Active Non-cumulative active cases.
CDS_Cases Cumulative total cases.
CDS_Deaths Cumulative total deaths.
CDS_Discharged Cumulative total individuals discharged from hospital care.
CDS_GrowthFactor Daily growth factor in cumulative cases, expressed as the ratio of the day's cumulative cases to the previous day's cumulative cases.
CDS_Hospitalized Cumulative count of hospitalizations.
CDS_Hospitalized_Current Non-cumulative active hospitalizations.
CDS_ICU Cumulative count of admissions to intensive care units (ICUs).
CDS_ICU_Current Non-cumulative active ICU patients.
CDS_Recovered Cumulative total recoveries.
CDS_Tested Cumulative total individuals tested.

University of Washington's Institute for Health Metrics and Evaluation: COVID-19 Projections

Metrics

Projections of hospital resource use and COVID-19 deaths available daily at country and province level globally. Projections are available for future dates up to several months following the latest projection date.

Metric Description
UniversityOfWashington_AdmisMean Mean number of hospital admissions per day.
UniversityOfWashington_AdmisLower Lower uncertainy bound of number of hospital admissions per day.
UniversityOfWashington_AdmisUpper Upper uncertainy bound of number of hospital admissions per day.
UniversityOfWashington_AllbedMean Mean number of COVID-19 hospital beds needed per day.
UniversityOfWashington_AllbedLower Lower uncertainy bound of number of COVID-19 hospital beds needed per day.
UniversityOfWashington_AllbedUpper Upper uncertainy bound of number of COVID-19 hospital beds needed per day.
UniversityOfWashington_BedoverMean Mean BedOver, computed as (Number of COVID-19 hospital beds needed per day - Total hospital bed capacity - Average hospital bed use).
UniversityOfWashington_BedoverLower Lower uncertainty bound of BedOver.
UniversityOfWashington_BedoverUpper Upper uncertainty bound of BedOver.
UniversityOfWashington_IcubedMean Mean number of COVID-19 ICU beds needed per day.
UniversityOfWashington_IcubedLower Lower uncertainty bound of number of COVID-19 ICU beds needed per day.
UniversityOfWashington_IcubedUpper Upper uncertainty bound of number of COVID-19 ICU beds needed per day.
UniversityOfWashington_IcuoverMean Mean ICUOver, computed as (Number of COVID-19 ICU beds needed per day - Total ICU bed capacity - Average ICU bed use).
UniversityOfWashington_IcuoverLower Lower uncertainy bound of ICUOver.
UniversityOfWashington_IcuoverUpper Upper uncertainy bound of ICUOver.
UniversityOfWashington_InvvenMean Mean number of invasive ventilation procedures needed per day.
UniversityOfWashington_InvvenLower Lower uncertainty bound of number of invasive ventilation procedures needed per day.
UniversityOfWashington_InvvenUpper Upper uncertainty bound of number of invasive ventilation procedures needed per day.
UniversityOfWashington_NewicuMean Mean number of new ICU admissions per day .
UniversityOfWashington_NewicuLower Lower uncertainty bound of new ICU admissions per day .
UniversityOfWashington_NewicuUpper Upper uncertainty bound of new ICU admissions per day .
UniversityOfWashington_DeathsMean Mean number of COVID-19 deaths per day.
UniversityOfWashington_DeathsLower Lower uncertainy bound of number of COVID-19 deaths per day.
UniversityOfWashington_DeathsUpper Upper uncertainy bound of number of COVID-19 deaths per day.
UniversityOfWashington_TotdeaMean Mean number of cumulative COVID-19 deaths .
UniversityOfWashington_TotdeaLower Lower uncertainty bound of number of cumulative COVID-19 deaths .
UniversityOfWashington_TotdeaUpper Upper uncertainty bound of number of cumulative COVID-19 deaths

US Census Bureau: Demographic Estimates

Metrics

Demographic data available annually for the United States at a country, state, and county level. The table below contains example metrics. The full list of metrics is available for download in this Microsoft Excel document.

NOTE: Please set the interval field to YEAR and start field to a date no earlier than 2011-01-01 in the API request to ensure performance.

Metric Description
TotalPopulation Total populaton.
Male_Total_Population Total male population.
Female_Total_Population Total female population.
MaleAndFemale_Under18_Population Total population, under age 18 (both sexes).
MaleAndFemale_AtLeast65_Population Total population, at least age 65 (both sexes).

Examples

Example: Total population over time in Georgia and South Carolina

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["SouthCarolina_UnitedStates", "Georgia_UnitedStates"],
    "expressions": ["TotalPopulation"],
    "start": "2011-01-01",
    "end": "2020-01-01",
    "interval": "YEAR"
  }
}



Response JSON:

{
  "result": {
    "SouthCarolina_UnitedStates": {
      "TotalPopulation": {
        "type": "MaterializedTimeseriesDouble",
        "count": 9,
        "dates": [
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          4671422.0,
          4717112.0,
          4764153.0,
          4823793.0,
          4892253.0,
          4958235.0,
          5021219.0,
          5084127.0,
          5148714.0
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2011-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
    },
    "Georgia_UnitedStates": {
      "TotalPopulation": {
        "type": "MaterializedTimeseriesDouble",
        "count": 9,
        "dates": [
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          9801578.0,
          9901496.0,
          9973326.0,
          1.0069001E7,
          1.0181111E7,
          1.0304763E7,
          1.0413055E7,
          1.0519475E7,
          1.0617423E7
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2011-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
    }
  }
}

US Census Bureau: International Census

Metrics

Global demographic data and population projections available annually at country level. The full list of metrics is available for download in this Microsoft Excel document.

Metric Description
Female0to4_InternationalCensus Total female population, with age from 0 to 4.
Male25to29_InternationalCensus Total male population, with age from 25 to 29.
Female33_InternationalCensus Total female population, with age 33.
FemaleAtLeast100_InternationalCensus Total female population, with age at least 100.
Malefemale60_InternationalCensus Total population of both sexes, with age 60.
Fertility15To19International Age specific fertility rate for age 15-19 (births per 1,000 population).
CrudeBirthRateInternational Live births during a given year, per 1,000 mid-year total population.
CrudeDeathRateInternational Deaths during a given year, per 1,000 mid-year total population.
GrossReproductionInternational Gross reproduction rate (lifetime female births per woman).
GrowthRateInternational Average annual percent change in the population.
InfantMortalityInternational Both sexes infant mortality rate (infant deaths per 1,000 population).
InfantMortalityFemaleInternational Female infant mortality rate (infant deaths per 1,000 population).
InfantMortalityMaleInternational Male infant mortality rate (infant deaths per 1,000 population).
LifeExpectancyInternational Both sexes life expectancy at birth (years).
LifeExpectancyFemaleInternational Female life expectancy at birth (years).
LifeExpectancyMaleInternational Male life expectancy at birth (years).
MortalityRateUnder5International Both sexes under-5 mortality rate (probability of dying between ages 0 and 5) per year.
MortalityRateUnder5InternationalFemale Female sexes under-5 mortality rate (probability of dying between ages 0 and 5) per year.
MortalityRateUnder5InternationalMale Male sexes under-5 mortality rate (probability of dying between ages 0 and 5) per year.
NaturalIncreaseRateInternational Natural increase (births - deaths) per 1,000 population per year.
NetMigrationInternational Difference between the number of migrants entering and those leaving a country in a year, per 1,000 mid-year population.
SexRatioInternational Sex ratio at birth (male births per female birth).

Examples

Example: Net migration ratio over time in Mexico and United States

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Mexico", "UnitedStates"],
    "expressions": ["NetMigrationInternational"],
    "start": "2011-01-01",
    "end": "2020-01-01",
    "interval": "YEAR"
  }
}



Response JSON:

{
  "result": {
    "Mexico": {
      "NetMigrationInternational": {
        "type": "MaterializedTimeseriesDouble",
        "count": 9,
        "dates": [
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          -0.31,
          -0.96,
          -1.6,
          -1.64,
          -1.68,
          -1.73,
          -1.77,
          -1.81,
          -1.85
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "unit": {
          "id": "migrationRate"
        },
        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2011-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
    },
    "UnitedStates": {
      "NetMigrationInternational": {
        "type": "MaterializedTimeseriesDouble",
        "count": 9,
        "dates": [
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          2.8,
          2.9,
          3.0,
          3.4,
          3.4,
          3.5,
          3.4,
          3.83,
          3.82
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "unit": {
          "id": "migrationRate"
        },
        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2011-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
    }
  }
}

US Census Bureau: County Population by Age, Sex, Race, and Hispanic Origin

Metrics

Population estimates of gender-specific, race-specific, and ethnicity-specific groups available for counties and states in the United States from 2010 to 2019.

To see the methodology adopted by the US Census Bureau to produce these estimates, please see their documentation here.

NOTE: Please set the interval field to YEAR and start field to a date no earlier than 2011-01-01 in the API request to ensure performance.

The table below contains example metrics. The full list of metrics is available for download in this Microsoft Excel document.

Metric Description
MaleTotal_AnyEthnicity_WA_USCensus Estimate of Male Hispanic/Not Hispanic population with any age and race: White alone.
FemaleTotal_AnyEthnicity_BA_USCensus Estimate of Female Hispanic/Not Hispanic population with any age and race: Black or African American alone.
MaleTotal_AnyEthnicity_IA_USCensus Estimate of Male Hispanic/Not Hispanic population with any age and race: American Indian and Alaska Native alone.
FemaleTotal_AnyEthnicity_AA_USCensus Estimate of Female Hispanic/Not Hispanic population with any age and race: Asian alone.
MaleTotal_AnyEthnicity_NA_USCensus Estimate of Male Hispanic/Not Hispanic population with any age and race: Native Hawaiian and Other Pacific Islander alone.
FemaleTotal_AnyEthnicity_TOM_USCensus Estimate of Female Hispanic/Not Hispanic population with any age and race: Two or More Races.
MaleTotal_NotHispanic_AnyRace_USCensus Estimate of Male Not Hispanic population with any age and race: Any race.

Examples

Example: Total non-Hispanic Black or African American female population over time in Albany, New York

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Albany_NewYork_UnitedStates"],
    "expressions": ["FemaleTotal_NotHispanic_BA_USCensus"],
    "start": "2011-01-01",
    "end": "2020-01-01",
    "interval": "YEAR"
  }
}



Response JSON:

{
  "result": {
    "Albany_NewYork_UnitedStates": {
      "FemaleTotal_NotHispanic_BA_USCensus": {
        "type": "MaterializedTimeseriesDouble",
        "count": 9,
        "dates": [
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          19282.0,
          19564.0,
          19718.0,
          19796.0,
          19877.0,
          20173.0,
          20369.0,
          20498.0,
          20503.0
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2011-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
    }
  }
}

The World Bank: Global Health Statistics

Metrics

Demographic and economic data available annually at a country level. The table below contains example metrics. The full list of metrics is available for download in this Microsoft Excel document.

Metric Description
PopulationGrowth Population growth (annual %).
SurvivalRateto65_Male Survival to age 65, male (% of cohort).
TotalFertilityRate Fertility rate, total (births per woman).
HandwashingFacilities_PercentPopulation People with basic handwashing facilities including soap and water (% of population).
Physicians Physicians (per 1,000 people).
HospitalBeds Hospital beds (per 1,000 people).
OfficialExchangeRate Official exchange rate (LCU per US$, period average).
ConsumerPriceIndex Consumer price index (2010 = 100).

Bureau of Economic Analysis: GDP and Economic Profile by County

Metrics

Annual or quarterly economic data at county- and state-level for United States, including compensation, GDP, and job data by industry. When a metric is called on a state-level location and at a monthly interval, if quarterly data is available, the metric will be evaluated based on quarterly data.

The table below contains example metrics. The full list of metrics is available for download in this Microsoft Excel document. See more detail about the definitions and methods used by the BEA to produce this data in the Description column of the document.

Metric Description
BEA_AverageEarningsPerJob_Dollars Average earnings per job in dollars, calculated by dividing total earnings by total full-time and part-time employment.
BEA_AverageWagesAndSalaries_Dollars Average wages and salaries in dollars, calculated by dividing wages and salaries by total wage and salary employment.
BEA_CompensationOfEmployees_ApparelManufacturing_Dollars Employee compensation in dollars for the Apparel Manufacturing industry.
BEA_Employment_AirTransportation_Jobs Number of jobs in the Air Transportation industry.
BEA_NominalGDP_Construction_Dollars Nominal GDP of the Construction industry, in dollars.
BEA_PersonalCurrentTransferReceipts_MedicalBenefits_Dollars Personal current transfer receipts for medical benefits, in dollars. This value consists of income payments to persons for which no current services are performed and net insurance settlements.
BEA_PersonalIncome_Dollars Personal income in dollars.
BEA_RealGDP_EducationalServices_2012Dollars Real GDP of the Education Services industry, measured in 2012 dollars.
BEA_TotalEmployment_Jobs Total number of employment.

Examples

Example 1: Real annual GDP of the food service industry and the finance & insurance industry in Alameda county, California from 2000 to 2019

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Alameda_California_UnitedStates"],
    "expressions": [
      "BEA_RealGDP_AccommodationAndFoodServices_2012Dollars",
      "BEA_RealGDP_FinanceAndInsurance_2012Dollars"

    ],
    "start": "2000-01-01",
    "end": "2020-01-01",
    "interval":"YEAR"
  }
}



Response JSON:

{
  "result": {
    "Alameda_California_UnitedStates": {
      "BEA_RealGDP_AccommodationAndFoodServices_2012Dollars": {
        "type": "MaterializedTimeseriesDouble",
        "count": 20,
        "dates": [
          "2000-01-01T00:00:00",
          "2001-01-01T00:00:00",
          "2002-01-01T00:00:00",
          "2003-01-01T00:00:00",
          "2004-01-01T00:00:00",
          "2005-01-01T00:00:00",
          "2006-01-01T00:00:00",
          "2007-01-01T00:00:00",
          "2008-01-01T00:00:00",
          "2009-01-01T00:00:00",
          "2010-01-01T00:00:00",
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          0.0,
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          2.121226E9,
          2.073027E9,
          1.791307E9,
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          2.464973E9,
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          2.800007E9,
          2.920658E9,
          0.0
        ],
        "missing": [
          100,
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          0,
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          0,
          0,
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          0,
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        ],
        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2000-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      },
      "BEA_RealGDP_FinanceAndInsurance_2012Dollars": {
        "type": "MaterializedTimeseriesDouble",
        "count": 20,
        "dates": [
          "2000-01-01T00:00:00",
          "2001-01-01T00:00:00",
          "2002-01-01T00:00:00",
          "2003-01-01T00:00:00",
          "2004-01-01T00:00:00",
          "2005-01-01T00:00:00",
          "2006-01-01T00:00:00",
          "2007-01-01T00:00:00",
          "2008-01-01T00:00:00",
          "2009-01-01T00:00:00",
          "2010-01-01T00:00:00",
          "2011-01-01T00:00:00",
          "2012-01-01T00:00:00",
          "2013-01-01T00:00:00",
          "2014-01-01T00:00:00",
          "2015-01-01T00:00:00",
          "2016-01-01T00:00:00",
          "2017-01-01T00:00:00",
          "2018-01-01T00:00:00",
          "2019-01-01T00:00:00"
        ],
        "data": [
          0.0,
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          0.0
        ],
        "missing": [
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        "timeZone": "NONE",
        "interval": "YEAR",
        "start": "2000-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
    }
  }
}
Example 2: Quarterly data on total wages & salaries and total Social Security benefit receipts in Illinois from 2010 to 2019

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Illinois_UnitedStates"],
    "expressions": [
      "BEA_WagesAndSalaries_Dollars",
      "BEA_PersonalCurrentTransferReceipts_SocialSecurityBenefits_Dollars"
    ],
    "start": "2010-01-01",
    "end": "2020-01-01",
    "interval":"MONTH"
  }
}



Response JSON:

{
  "result": {
    "Illinois_UnitedStates": {
      "BEA_WagesAndSalaries_Dollars": {
        "type": "MaterializedTimeseriesDouble",
        "count": 120,
        "dates": [
          "2010-01-01T00:00:00",
          "2010-02-01T00:00:00",
          "2010-03-01T00:00:00",
          "2010-04-01T00:00:00",
          "2010-05-01T00:00:00",
          "2010-06-01T00:00:00",
          "2010-07-01T00:00:00",
          "2010-08-01T00:00:00",
          "2010-09-01T00:00:00",
          "2010-10-01T00:00:00",
          "2010-11-01T00:00:00",
          "2010-12-01T00:00:00",
          "2011-01-01T00:00:00",
          "2011-02-01T00:00:00",
          "2011-03-01T00:00:00",
          "2011-04-01T00:00:00",
          "2011-05-01T00:00:00",
          "2011-06-01T00:00:00",
          "2011-07-01T00:00:00",
          "2011-08-01T00:00:00",
          "2011-09-01T00:00:00",
          "2011-10-01T00:00:00",
          "2011-11-01T00:00:00",
          "2011-12-01T00:00:00",
          "2012-01-01T00:00:00",
          "2012-02-01T00:00:00",
          "2012-03-01T00:00:00",
          "2012-04-01T00:00:00",
          "2012-05-01T00:00:00",
          "2012-06-01T00:00:00",
          "2012-07-01T00:00:00",
          "2012-08-01T00:00:00",
          "2012-09-01T00:00:00",
          "2012-10-01T00:00:00",
          "2012-11-01T00:00:00",
          "2012-12-01T00:00:00",
          "2013-01-01T00:00:00",
          "2013-02-01T00:00:00",
          "2013-03-01T00:00:00",
          "2013-04-01T00:00:00",
          "2013-05-01T00:00:00",
          "2013-06-01T00:00:00",
          "2013-07-01T00:00:00",
          "2013-08-01T00:00:00",
          "2013-09-01T00:00:00",
          "2013-10-01T00:00:00",
          "2013-11-01T00:00:00",
          "2013-12-01T00:00:00",
          "2014-01-01T00:00:00",
          "2014-02-01T00:00:00",
          "2014-03-01T00:00:00",
          "2014-04-01T00:00:00",
          "2014-05-01T00:00:00",
          "2014-06-01T00:00:00",
          "2014-07-01T00:00:00",
          "2014-08-01T00:00:00",
          "2014-09-01T00:00:00",
          "2014-10-01T00:00:00",
          "2014-11-01T00:00:00",
          "2014-12-01T00:00:00",
          "2015-01-01T00:00:00",
          "2015-02-01T00:00:00",
          "2015-03-01T00:00:00",
          "2015-04-01T00:00:00",
          "2015-05-01T00:00:00",
          "2015-06-01T00:00:00",
          "2015-07-01T00:00:00",
          "2015-08-01T00:00:00",
          "2015-09-01T00:00:00",
          "2015-10-01T00:00:00",
          "2015-11-01T00:00:00",
          "2015-12-01T00:00:00",
          "2016-01-01T00:00:00",
          "2016-02-01T00:00:00",
          "2016-03-01T00:00:00",
          "2016-04-01T00:00:00",
          "2016-05-01T00:00:00",
          "2016-06-01T00:00:00",
          "2016-07-01T00:00:00",
          "2016-08-01T00:00:00",
          "2016-09-01T00:00:00",
          "2016-10-01T00:00:00",
          "2016-11-01T00:00:00",
          "2016-12-01T00:00:00",
          "2017-01-01T00:00:00",
          "2017-02-01T00:00:00",
          "2017-03-01T00:00:00",
          "2017-04-01T00:00:00",
          "2017-05-01T00:00:00",
          "2017-06-01T00:00:00",
          "2017-07-01T00:00:00",
          "2017-08-01T00:00:00",
          "2017-09-01T00:00:00",
          "2017-10-01T00:00:00",
          "2017-11-01T00:00:00",
          "2017-12-01T00:00:00",
          "2018-01-01T00:00:00",
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          "2018-04-01T00:00:00",
          "2018-05-01T00:00:00",
          "2018-06-01T00:00:00",
          "2018-07-01T00:00:00",
          "2018-08-01T00:00:00",
          "2018-09-01T00:00:00",
          "2018-10-01T00:00:00",
          "2018-11-01T00:00:00",
          "2018-12-01T00:00:00",
          "2019-01-01T00:00:00",
          "2019-02-01T00:00:00",
          "2019-03-01T00:00:00",
          "2019-04-01T00:00:00",
          "2019-05-01T00:00:00",
          "2019-06-01T00:00:00",
          "2019-07-01T00:00:00",
          "2019-08-01T00:00:00",
          "2019-09-01T00:00:00",
          "2019-10-01T00:00:00",
          "2019-11-01T00:00:00",
          "2019-12-01T00:00:00"
        ],
        "data": [
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        "timeZone": "NONE",
        "interval": "MONTH",
        "start": "2010-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      },
      "BEA_PersonalCurrentTransferReceipts_SocialSecurityBenefits_Dollars": {
        "type": "MaterializedTimeseriesDouble",
        "count": 120,
        "dates": [
          "2010-01-01T00:00:00",
          "2010-02-01T00:00:00",
          "2010-03-01T00:00:00",
          "2010-04-01T00:00:00",
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          "2011-03-01T00:00:00",
          "2011-04-01T00:00:00",
          "2011-05-01T00:00:00",
          "2011-06-01T00:00:00",
          "2011-07-01T00:00:00",
          "2011-08-01T00:00:00",
          "2011-09-01T00:00:00",
          "2011-10-01T00:00:00",
          "2011-11-01T00:00:00",
          "2011-12-01T00:00:00",
          "2012-01-01T00:00:00",
          "2012-02-01T00:00:00",
          "2012-03-01T00:00:00",
          "2012-04-01T00:00:00",
          "2012-05-01T00:00:00",
          "2012-06-01T00:00:00",
          "2012-07-01T00:00:00",
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          "2013-05-01T00:00:00",
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          "2017-01-01T00:00:00",
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          "2017-03-01T00:00:00",
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        "interval": "MONTH",
        "start": "2010-01-01T00:00:00",
        "end": "2020-01-01T00:00:00"
      }
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}

US Bureau of Labor Statistics: County Unemployment Statistics

Metrics

Monthly labor statistics available at county, state, and national level for the United States. See more detail about the definition of unemployment and labor force here.
Metric Description
BLS_LaborForcePopulation Population of the labor force.
BLS_EmployedPopulation Civilian noninstitutional population aged 16 years and over that is employed.
BLS_UnemployedPopulation Number of civilians aged 16 years or older who: had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment sometime during the 4-week period ending with the reference week.
BLS_UnemploymentRate Unemployed population divided by labor force population, in percent.

Examples

Example 1: Unemployment rate for Austin, Texas from January, 2019 to May, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Austin_Texas_UnitedStates"],
    "expressions": [
      "BLS_UnemploymentRate"
    ],
    "start": "2019-01-01",
    "end": "2020-06-01",
    "interval":"MONTH"
  }
}



Response JSON:

{
  "result": {
    "Austin_Texas_UnitedStates": {
      "BLS_UnemploymentRate": {
        "type": "MaterializedTimeseriesDouble",
        "count": 17,
        "dates": [
          "2019-01-01T00:00:00",
          "2019-02-01T00:00:00",
          "2019-03-01T00:00:00",
          "2019-04-01T00:00:00",
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          "2019-08-01T00:00:00",
          "2019-09-01T00:00:00",
          "2019-10-01T00:00:00",
          "2019-11-01T00:00:00",
          "2019-12-01T00:00:00",
          "2020-01-01T00:00:00",
          "2020-02-01T00:00:00",
          "2020-03-01T00:00:00",
          "2020-04-01T00:00:00",
          "2020-05-01T00:00:00"
        ],
        "data": [
          3.9724652230030117,
          3.568382143872619,
          3.393922181198523,
          2.857142857142857,
          3.0197030268418046,
          3.6737014140552833,
          3.8167398385497804,
          3.635204081632653,
          3.296547821165818,
          3.2514603420367374,
          3.410209534523977,
          3.2441942294159043,
          3.72561447989295,
          3.5438278911090997,
          5.1931512664496955,
          9.642643242164105,
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        ],
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        "timeZone": "NONE",
        "interval": "MONTH",
        "start": "2019-01-01T00:00:00",
        "end": "2020-06-01T00:00:00"
      }
    }
  }
}
Example 2: Unemployed population and labor force population for California from January, 2019 to May, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["California_UnitedStates"],
    "expressions": [
      "BLS_UnemployedPopulation",
      "BLS_LaborForcePopulation"
    ],
    "start": "2019-01-01",
    "end": "2020-06-01",
    "interval":"MONTH"
  }
}



Response JSON:

{
  "result": {
    "California_UnitedStates": {
      "BLS_UnemployedPopulation": {
        "type": "MaterializedTimeseriesDouble",
        "count": 17,
        "dates": [
          "2019-01-01T00:00:00",
          "2019-02-01T00:00:00",
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          "2019-11-01T00:00:00",
          "2019-12-01T00:00:00",
          "2020-01-01T00:00:00",
          "2020-02-01T00:00:00",
          "2020-03-01T00:00:00",
          "2020-04-01T00:00:00",
          "2020-05-01T00:00:00"
        ],
        "data": [
          929788.0,
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        "interval": "MONTH",
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        "end": "2020-06-01T00:00:00"
      },
      "BLS_LaborForcePopulation": {
        "type": "MaterializedTimeseriesDouble",
        "count": 17,
        "dates": [
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          "2020-03-01T00:00:00",
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        "data": [
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        "timeZone": "NONE",
        "interval": "MONTH",
        "start": "2019-01-01T00:00:00",
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  }
}

Opportunity Insights: Economic Tracker

Metrics

This dataset provides the following high-frequency economic data for United States locations:

  • Daily (Jan 25, 2020 - July 6, 2020) and weekly (July, 2020 - present) consumer spending data from Affinity Solutions available at city, county, state, and national level
  • Weekly job posting data from Burning Glass available at city, state, and national level
  • Weekly unemployment insurance claim data from the Department of Labor (national and state-level) and individual state agencies (county-level)
  • Weekly employment levels from Paychex, Earnin, and Intuit at city, county, state, and national level
  • Daily small business openings and revenue data from Womply available at city, county, state, and national level until August 9, 2020
  • Daily earning and employment data for low income workers in all businesses and small businesses from Earnin and Homebase available at city, county, state, and national level until May 30, 2020

The table below contains example metrics. Note that some metrics are only available up to several weeks or months before the current date, depending on availability in the source dataset. The full list of metrics, their descriptions, and their availability at different location levels is available for download in this Microsoft Excel document.

See more detail about the source and definition of these economic indicators here.

Metric Description
OIET_Affinity_SpendAcf Seasonally adjusted credit/debit card spending relative to January 4-31, 2020 in accomodation and food service (ACF) MCCs, 7 day moving average, 7 day moving average.
OIET_BurningGlass_BgPosts Average level of job postings relative to January 4-31, 2020.
OIET_BurningGlass_BgPostsSs30 Average level of job postings relative to January 4-31, 2020 in manufacturing (NAICS supersector 30).
OIET_UIClaims_InitialClaims Count of newly requested claims to begin a period of unemployment insurance eligibility.
OIET_UIClaims_InitialClaimsRate Initial claims per 100 people in the 2019 labor force.
OIET_Employment_All Employment level relative to Jan 4-31, 2020 for all workers.
OIET_Employment_IncLow Employment level relative to Jan 4-31, 2020 for workers in the bottom quartile of the income distribution (incomes approximately under $27,000).
OIET_Employment_ss60 Employment level relative to Jan 4-31, 2020 for workers in professional and business services (NAICS supersector 60).
OIET_Employment_ss70 Employment level relative to Jan 4-31, 2020 for workers in leisure and hospitality (NAICS supersector 70).
OIET_WomplyRevenue_RevenueAll Percent change in net revenue for small businesses, calculated as a seven-day moving average, seasonally adjusted, and indexed to January 4-31 2020.
OIET_WomplyMerchants_MerchantsAll Percent change in number of small businesses open calculated as a seven-day moving average seasonally adjusted and indexed to January 4-31 2020.

Examples

Example: Trend of consumer spending in healthcare overlaid with the trend of low-income group earnings in the Health Care and Social Assistance sector in California from January, 2020 to May, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["California_UnitedStates"],
    "expressions": [
      "OIET_Affinity_SpendHcs",
      "OIET_LowIncEmpAllBusinesses_Emp62"
    ],
    "start": "2020-01-01",
    "end": "2020-06-01",
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
    "California_UnitedStates": {
      "OIET_Affinity_SpendHcs": {
        "type": "MaterializedTimeseriesDouble",
        "count": 152,
        "dates": [
          "2020-01-01T00:00:00",
          "2020-01-02T00:00:00",
          "2020-01-03T00:00:00",
          "2020-01-04T00:00:00",
          "2020-01-05T00:00:00",
          "2020-01-06T00:00:00",
          "2020-01-07T00:00:00",
          "2020-01-08T00:00:00",
          "2020-01-09T00:00:00",
          "2020-01-10T00:00:00",
          "2020-01-11T00:00:00",
          "2020-01-12T00:00:00",
          "2020-01-13T00:00:00",
          "2020-01-14T00:00:00",
          "2020-01-15T00:00:00",
          "2020-01-16T00:00:00",
          "2020-01-17T00:00:00",
          "2020-01-18T00:00:00",
          "2020-01-19T00:00:00",
          "2020-01-20T00:00:00",
          "2020-01-21T00:00:00",
          "2020-01-22T00:00:00",
          "2020-01-23T00:00:00",
          "2020-01-24T00:00:00",
          "2020-01-25T00:00:00",
          "2020-01-26T00:00:00",
          "2020-01-27T00:00:00",
          "2020-01-28T00:00:00",
          "2020-01-29T00:00:00",
          "2020-01-30T00:00:00",
          "2020-01-31T00:00:00",
          "2020-02-01T00:00:00",
          "2020-02-02T00:00:00",
          "2020-02-03T00:00:00",
          "2020-02-04T00:00:00",
          "2020-02-05T00:00:00",
          "2020-02-06T00:00:00",
          "2020-02-07T00:00:00",
          "2020-02-08T00:00:00",
          "2020-02-09T00:00:00",
          "2020-02-10T00:00:00",
          "2020-02-11T00:00:00",
          "2020-02-12T00:00:00",
          "2020-02-13T00:00:00",
          "2020-02-14T00:00:00",
          "2020-02-15T00:00:00",
          "2020-02-16T00:00:00",
          "2020-02-17T00:00:00",
          "2020-02-18T00:00:00",
          "2020-02-19T00:00:00",
          "2020-02-20T00:00:00",
          "2020-02-21T00:00:00",
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          "2020-02-29T00:00:00",
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
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          "2020-03-22T00:00:00",
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          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
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Realtor.com: Housing Indicators

Metrics

Monthly residential housing inventory and listing statistics for US counties and states.

The table below contains example metrics. The full list of metrics is available for download in this Microsoft Excel document.

See more detail about the definition of inventory indicators here.

Metric Description
Realtor_AvgMedianListingPrice Average of the monthly median listing price within the specified geography.
Realtor_AvgMedianListingPricePerSquareFeet Average of the monthly median listing price per square foot within the specified geography.
Realtor_ActiveListingCount Number of active listings within the specified geography.
Realtor_NewListingCount Number of new listings added to the market within the specified geography.
Realtor_PriceIncreasedCount Number of listings which have had their price increased within the specified geography.
Realtor_PriceReducedCount Number of listings which have had their price reduced within the specified geography.
Realtor_PendingListingCount Number of pending listings within the specified geography during the specified month, if a pending definition is available for that geography.
Realtor_TotalListingCount Total number of both active listings and pending listings within the specified geography. Only available at county level.
Realtor_AvgMedianDaysOnMarket Average of the monthly median number of days property listings spend on the market within the specified geography.
Realtor_AvgMedianSquareFeet Average of the monthly median listing square feet within the specified geography.
Realtor_AvgPendingRatio Average of the monthly ratio of the pending listing count to the active listing count within the specified geography. Only available at county level.
Realtor_AverageListingPrice Average listing price within the specified geography.
Realtor_AvgPercentChangeMedianListingPriceMm Average percentage change in the median listing price from the previous month.
Realtor_AvgPercentChangeMedianListingPriceYy Average percentage change in the median listing price from the same month in the previous year.

Examples

Example: Number of real-estate listings with price increases and decreases in Arizona from January, 2019 to May, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["Arizona_UnitedStates"],
    "expressions": [
      "Realtor_PriceIncreasedCount",
      "Realtor_PriceReducedCount"
    ],
    "start": "2019-01-01",
    "end": "2020-06-01",
    "interval":"MONTH"
  }
}



Response JSON:

{
  "result": {
    "Arizona_UnitedStates": {
      "Realtor_PriceReducedCount": {
        "type": "MaterializedTimeseriesDouble",
        "count": 17,
        "dates": [
          "2019-01-01T00:00:00",
          "2019-02-01T00:00:00",
          "2019-03-01T00:00:00",
          "2019-04-01T00:00:00",
          "2019-05-01T00:00:00",
          "2019-06-01T00:00:00",
          "2019-07-01T00:00:00",
          "2019-08-01T00:00:00",
          "2019-09-01T00:00:00",
          "2019-10-01T00:00:00",
          "2019-11-01T00:00:00",
          "2019-12-01T00:00:00",
          "2020-01-01T00:00:00",
          "2020-02-01T00:00:00",
          "2020-03-01T00:00:00",
          "2020-04-01T00:00:00",
          "2020-05-01T00:00:00"
        ],
        "data": [
          11216.0,
          12016.0,
          11448.0,
          10992.0,
          10540.0,
          10112.0,
          8872.0,
          8736.0,
          8780.0,
          9052.0,
          8736.0,
          5112.0,
          6132.0,
          6100.0,
          6072.0,
          6480.0,
          6576.0
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "unit": {
          "id": "single"
        },
        "timeZone": "NONE",
        "interval": "MONTH",
        "start": "2019-01-01T00:00:00",
        "end": "2020-06-01T00:00:00"
      },
      "Realtor_PriceIncreasedCount": {
        "type": "MaterializedTimeseriesDouble",
        "count": 17,
        "dates": [
          "2019-01-01T00:00:00",
          "2019-02-01T00:00:00",
          "2019-03-01T00:00:00",
          "2019-04-01T00:00:00",
          "2019-05-01T00:00:00",
          "2019-06-01T00:00:00",
          "2019-07-01T00:00:00",
          "2019-08-01T00:00:00",
          "2019-09-01T00:00:00",
          "2019-10-01T00:00:00",
          "2019-11-01T00:00:00",
          "2019-12-01T00:00:00",
          "2020-01-01T00:00:00",
          "2020-02-01T00:00:00",
          "2020-03-01T00:00:00",
          "2020-04-01T00:00:00",
          "2020-05-01T00:00:00"
        ],
        "data": [
          880.0,
          912.0,
          836.0,
          832.0,
          892.0,
          1124.0,
          896.0,
          1012.0,
          904.0,
          712.0,
          768.0,
          764.0,
          896.0,
          1004.0,
          896.0,
          544.0,
          708.0
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "unit": {
          "id": "single"
        },
        "timeZone": "NONE",
        "interval": "MONTH",
        "start": "2019-01-01T00:00:00",
        "end": "2020-06-01T00:00:00"
      }
    }
  }
}

Apple: COVID-19 Mobility Trends

Metrics

Mobility data available daily at a country, province, and city level globally. See more detail about the collection of mobility data and definition of mobility index here.

Metric Description
Apple_DrivingMobility Normalized driving routing requests, where 100 indicates number of requests on January 13, 2020.
Apple_WalkingMobility Normalized walking routing requests, where 100 indicates number of requests on January 13, 2020.
Apple_TransitMobility Normalized transit routing requests, where 100 indicates number of requests on January 13, 2020.

Examples

Example: Walking, driving, parks, and residential mobility trends for Washington, DC, United States in March, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["DistrictofColumbia_UnitedStates"],
    "expressions": [
      "Apple_WalkingMobility",
      "Apple_DrivingMobility",
      "Google_ParksMobility",
      "Google_ResidentialMobility"
    ],
    "start": "2020-03-01",
    "end": "2020-04-01",
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
    "DistrictofColumbia_UnitedStates": {
      "Apple_WalkingMobility": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00"
        ],
        "data": [
          113.18,
          104.74,
          113.76,
          120.47,
          116.91,
          130.96,
          165.53,
          125.01,
          118.95,
          106.91,
          101.09,
          93.05,
          104.24,
          96.93,
          57.97,
          57.09,
          54.55,
          50.56,
          54.55,
          58.62,
          53.16,
          43.48,
          31.99,
          39.32,
          31.24,
          43.64,
          46.98,
          38.3,
          41.06,
          43.29,
          31.73
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-03-01T00:00:00",
        "end": "2020-04-01T00:00:00"
      },
      "Apple_DrivingMobility": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00"
        ],
        "data": [
          96.56,
          106.99,
          110.98,
          112.23,
          115.66,
          130.54,
          133.53,
          100.47,
          108.19,
          106.25,
          105.11,
          101.89,
          107.94,
          93.77,
          64.94,
          74.91,
          68.14,
          64.23,
          63.93,
          69.48,
          59.31,
          45.78,
          47.58,
          50.66,
          45.96,
          52.99,
          58.72,
          47.84,
          42.06,
          50.94,
          42.38
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
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          0,
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          0,
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          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-03-01T00:00:00",
        "end": "2020-04-01T00:00:00"
      },
      "Google_ResidentialMobility": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00"
        ],
        "data": [
          98.0,
          100.0,
          99.0,
          99.0,
          100.0,
          101.0,
          99.0,
          99.0,
          101.0,
          100.0,
          102.0,
          103.0,
          105.0,
          104.0,
          106.0,
          116.0,
          119.0,
          121.0,
          122.0,
          122.0,
          113.0,
          112.0,
          127.0,
          125.0,
          128.0,
          127.0,
          127.0,
          117.0,
          114.0,
          126.0,
          128.0
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
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          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-03-01T00:00:00",
        "end": "2020-04-01T00:00:00"
      },
      "Google_ParksMobility": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00"
        ],
        "data": [
          100.0,
          99.0,
          102.0,
          108.0,
          106.0,
          94.0,
          108.0,
          133.0,
          131.0,
          113.0,
          105.0,
          100.0,
          95.0,
          74.0,
          57.0,
          65.0,
          78.0,
          68.0,
          82.0,
          84.0,
          81.0,
          75.0,
          34.0,
          54.0,
          32.0,
          58.0,
          55.0,
          31.0,
          59.0,
          53.0,
          35.0
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
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          0,
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          0,
          0,
          0
        ],
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-03-01T00:00:00",
        "end": "2020-04-01T00:00:00"
      }
    }
  }
}

Google: COVID-19 Community Mobility Reports

Metrics

Mobility data available daily at a country, province, and city level globally. The value for each day is normalized by day of the week, where 100 indicates the median value for that day of the week from January 3, 2020 through February 6, 2020.
See more detail about the collection of mobility data and definition of mobility index here.

Metric Description
Google_GroceryMobility Normalized mobility trends for places including grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.
Google_ParksMobility Normalized mobility trends for places including local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens.
Google_TransitStationsMobility Normalized mobility trends for places including public transit hubs, subway stations, bus stations, and train stations.
Google_RetailMobility Normalized mobility trends for places including restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
Google_ResidentialMobility Normalized mobility trends for residences.
Google_WorkplacesMobility Normalized mobility trends for workplaces.

PlaceIQ Exposure Indices

Metrics

Device count and device exposure index (DEX) data available daily at the county and state level in the United States.

The device exposure index (DEX) of a state or county location refers to the average number of distinct devices that visited any of the commercial venues that a particular smartphone at this location visited on a given day.

Device counts and DEX of a particular demographic group (based on education, income, or ethnicity) is computed by inferring the user’s permanent block of residence and using 2014-2018 ACS statistics of the neighborhood. Education level (1-4) is classified based on which quartile the neighborhood’s college share resides. Income level (1-4) is classified based on which quartile the neighborhood’s income resides.

See more detailed description of the methodology and index definition here.

The full list of metrics is available for download in this Microsoft Excel document.

Metric Description
PlaceIQ_DeviceCount Number of devices residing in a location.
PlaceIQ_DeviceExposure Device exposure index (DEX): average number of distinct devices that also visited any of the commercial venues that a particular device visited.
PlaceIQ_DeviceCount_Adjusted Number of devices at this location, adjusted to counteract sample bias caused by shelter-in-place.
PlaceIQ_DeviceExposure_Adjusted Device exposure index (DEX), adjusted to counteract sample bias caused by shelter-in-place.
PlaceIQ_DeviceCount_Education1 Device count for lowest education group, of four (4).
PlaceIQ_DeviceExposure_Education1 DEX for lowest education group, of four (4).
PlaceIQ_DeviceCount_Education1_Adjusted Adjusted device count for lowest education group, of four (4).
PlaceIQ_DeviceExposure_Education1_Adjusted Adjusted DEX for lowest education group, of four (4).
PlaceIQ_DeviceCount_Income2 Device count for second-lowest income group, of four (4).
PlaceIQ_DeviceExposure_RaceAsian DEX for Asian ethnic group.
PlaceIQ_DeviceCount_RaceBlack_Adjusted Adjusted device count for Black ethnic group.
PlaceIQ_DeviceExposure_RaceWhite_Adjusted Adjusted DEX for White ethnic group.

Examples

Example: Device Exposure (adjusted) for Santa Clara county, California

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec":{
    "ids":["SantaClara_California_UnitedStates"],
    "expressions":[ "PlaceIQ_DeviceExposure", "PlaceIQ_DeviceExposure_Adjusted"],
    "start": "2020-05-01",
    "end": "2020-05-15",
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
    "SantaClara_California_UnitedStates": {
      "PlaceIQ_DeviceExposure": {
        "type": "MaterializedTimeseriesDouble",
        "count": 14,
        "dates": [
          "2020-05-01T00:00:00",
          "2020-05-02T00:00:00",
          "2020-05-03T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
          "2020-05-07T00:00:00",
          "2020-05-08T00:00:00",
          "2020-05-09T00:00:00",
          "2020-05-10T00:00:00",
          "2020-05-11T00:00:00",
          "2020-05-12T00:00:00",
          "2020-05-13T00:00:00",
          "2020-05-14T00:00:00"
        ],
        "data": [
          26.0217,
          43.9594,
          32.8597,
          21.2203,
          21.8976,
          21.8725,
          21.6751,
          27.0366,
          56.5068,
          40.1078,
          19.8933,
          20.2918,
          20.6562,
          21.388
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "unit": {
          "id": "exposureIndex"
        },
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-05-01T00:00:00",
        "end": "2020-05-15T00:00:00"
      },
      "PlaceIQ_DeviceExposure_Adjusted": {
        "type": "MaterializedTimeseriesDouble",
        "count": 14,
        "dates": [
          "2020-05-01T00:00:00",
          "2020-05-02T00:00:00",
          "2020-05-03T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
          "2020-05-07T00:00:00",
          "2020-05-08T00:00:00",
          "2020-05-09T00:00:00",
          "2020-05-10T00:00:00",
          "2020-05-11T00:00:00",
          "2020-05-12T00:00:00",
          "2020-05-13T00:00:00",
          "2020-05-14T00:00:00"
        ],
        "data": [
          16.7024,
          31.1769,
          22.8112,
          13.8873,
          14.6107,
          14.6811,
          14.7436,
          17.9238,
          41.3063,
          29.3504,
          13.2251,
          13.7582,
          13.8765,
          14.6086
        ],
        "missing": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "unit": {
          "id": "exposureIndex"
        },
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-05-01T00:00:00",
        "end": "2020-05-15T00:00:00"
      }
    }
  }
}

University of Oxford: Coronavirus Government Response Tracker

Metrics

Policy indices are aggregates of individual policy indicators (see more detail in PolicyDetail) and show the level of government response along a certain policy dimension. For example, the Containment and Health index is an average of all policy values of C- and H-type policies for a location on a given day.

The full list of metrics is available for download in this Microsoft Excel document.

See more detailed description of policy types and indices here.

Metric Description
OxCGRT_Policy_C1_Flag Flag value (whether the policy is targets at a specific region or applies to the whole country) of C1 policy.
OxCGRT_Policy_C1_SchoolClosing C1 policy index: level of the governments response along C1 policy dimension.
OxCGRT_Policy_C8_InternationalTravelControls C8 policy index: level of the governments response along C8 policy dimension.
OxCGRT_Policy_E1_IncomeSupport E1 policy index: level of the governments response along E1 policy dimension.
OxCGRT_Policy_ConfirmedCases Confirmed case count.
OxCGRT_Policy_ConfirmedDeaths Confirmed death count.
OxCGRT_Policy_StringencyIndex Level of the governments response along stringency dimension (all C indicators and H1).
OxCGRT_Policy_StringencyIndexForDisplay Stringency index with extrapolation and smoothing.
OxCGRT_Policy_StringencyLegacyIndex Legacy index value of governmennt response stringency.
OxCGRT_Policy_StringencyLegacyIndexForDisplay Legacy stringency index with extrapolation and smoothing.
OxCGRT_Policy_GovernmentResponseIndex Level of overall governments response, taking account of all indicators.
OxCGRT_Policy_GovernmentResponseIndexForDisplay Government response index with extrapolation and smoothing.
OxCGRT_Policy_ContainmentHealthIndex Level of the governments response along containment and health dimension (all C and H indicators).
OxCGRT_Policy_ContainmentHealthIndexForDisplay Containment and health index with extrapolation and smoothing.
OxCGRT_Policy_EconomicSupportIndex Level of the governments response along economic support dimension (all E indicators).
OxCGRT_Policy_EconomicSupportIndexForDisplay Economic support index with extrapolation and smoothing.

Examples

Example: Level of government response in Containment and Health over time in the United States and Canada

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["UnitedStates", "Canada"],
    "expressions": ["OxCGRT_Policy_ContainmentHealthIndex"],
    "start": "2020-02-01",
    "end": "2020-05-01",
    "interval":"DAY"
  }
}



Response JSON:

{
  "result": {
    "Canada": {
      "OxCGRT_Policy_ContainmentHealthIndex": {
        "type": "MaterializedTimeseriesDouble",
        "count": 90,
        "dates": [
          "2020-02-01T00:00:00",
          "2020-02-02T00:00:00",
          "2020-02-03T00:00:00",
          "2020-02-04T00:00:00",
          "2020-02-05T00:00:00",
          "2020-02-06T00:00:00",
          "2020-02-07T00:00:00",
          "2020-02-08T00:00:00",
          "2020-02-09T00:00:00",
          "2020-02-10T00:00:00",
          "2020-02-11T00:00:00",
          "2020-02-12T00:00:00",
          "2020-02-13T00:00:00",
          "2020-02-14T00:00:00",
          "2020-02-15T00:00:00",
          "2020-02-16T00:00:00",
          "2020-02-17T00:00:00",
          "2020-02-18T00:00:00",
          "2020-02-19T00:00:00",
          "2020-02-20T00:00:00",
          "2020-02-21T00:00:00",
          "2020-02-22T00:00:00",
          "2020-02-23T00:00:00",
          "2020-02-24T00:00:00",
          "2020-02-25T00:00:00",
          "2020-02-26T00:00:00",
          "2020-02-27T00:00:00",
          "2020-02-28T00:00:00",
          "2020-02-29T00:00:00",
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00",
          "2020-04-01T00:00:00",
          "2020-04-02T00:00:00",
          "2020-04-03T00:00:00",
          "2020-04-04T00:00:00",
          "2020-04-05T00:00:00",
          "2020-04-06T00:00:00",
          "2020-04-07T00:00:00",
          "2020-04-08T00:00:00",
          "2020-04-09T00:00:00",
          "2020-04-10T00:00:00",
          "2020-04-11T00:00:00",
          "2020-04-12T00:00:00",
          "2020-04-13T00:00:00",
          "2020-04-14T00:00:00",
          "2020-04-15T00:00:00",
          "2020-04-16T00:00:00",
          "2020-04-17T00:00:00",
          "2020-04-18T00:00:00",
          "2020-04-19T00:00:00",
          "2020-04-20T00:00:00",
          "2020-04-21T00:00:00",
          "2020-04-22T00:00:00",
          "2020-04-23T00:00:00",
          "2020-04-24T00:00:00",
          "2020-04-25T00:00:00",
          "2020-04-26T00:00:00",
          "2020-04-27T00:00:00",
          "2020-04-28T00:00:00",
          "2020-04-29T00:00:00",
          "2020-04-30T00:00:00"
        ],
        "data": [
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-02-01T00:00:00",
        "end": "2020-05-01T00:00:00"
      }
    },
    "UnitedStates": {
      "OxCGRT_Policy_ContainmentHealthIndex": {
        "type": "MaterializedTimeseriesDouble",
        "count": 90,
        "dates": [
          "2020-02-01T00:00:00",
          "2020-02-02T00:00:00",
          "2020-02-03T00:00:00",
          "2020-02-04T00:00:00",
          "2020-02-05T00:00:00",
          "2020-02-06T00:00:00",
          "2020-02-07T00:00:00",
          "2020-02-08T00:00:00",
          "2020-02-09T00:00:00",
          "2020-02-10T00:00:00",
          "2020-02-11T00:00:00",
          "2020-02-12T00:00:00",
          "2020-02-13T00:00:00",
          "2020-02-14T00:00:00",
          "2020-02-15T00:00:00",
          "2020-02-16T00:00:00",
          "2020-02-17T00:00:00",
          "2020-02-18T00:00:00",
          "2020-02-19T00:00:00",
          "2020-02-20T00:00:00",
          "2020-02-21T00:00:00",
          "2020-02-22T00:00:00",
          "2020-02-23T00:00:00",
          "2020-02-24T00:00:00",
          "2020-02-25T00:00:00",
          "2020-02-26T00:00:00",
          "2020-02-27T00:00:00",
          "2020-02-28T00:00:00",
          "2020-02-29T00:00:00",
          "2020-03-01T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-07T00:00:00",
          "2020-03-08T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-14T00:00:00",
          "2020-03-15T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-21T00:00:00",
          "2020-03-22T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-28T00:00:00",
          "2020-03-29T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00",
          "2020-04-01T00:00:00",
          "2020-04-02T00:00:00",
          "2020-04-03T00:00:00",
          "2020-04-04T00:00:00",
          "2020-04-05T00:00:00",
          "2020-04-06T00:00:00",
          "2020-04-07T00:00:00",
          "2020-04-08T00:00:00",
          "2020-04-09T00:00:00",
          "2020-04-10T00:00:00",
          "2020-04-11T00:00:00",
          "2020-04-12T00:00:00",
          "2020-04-13T00:00:00",
          "2020-04-14T00:00:00",
          "2020-04-15T00:00:00",
          "2020-04-16T00:00:00",
          "2020-04-17T00:00:00",
          "2020-04-18T00:00:00",
          "2020-04-19T00:00:00",
          "2020-04-20T00:00:00",
          "2020-04-21T00:00:00",
          "2020-04-22T00:00:00",
          "2020-04-23T00:00:00",
          "2020-04-24T00:00:00",
          "2020-04-25T00:00:00",
          "2020-04-26T00:00:00",
          "2020-04-27T00:00:00",
          "2020-04-28T00:00:00",
          "2020-04-29T00:00:00",
          "2020-04-30T00:00:00"
        ],
        "data": [
          4.55,
          9.09,
          9.09,
          9.09,
          9.09,
          9.09,
          9.09,
          9.09,
          9.09,
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          12.12,
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          16.67,
          16.67,
          19.7,
          27.27,
          27.27,
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          68.56,
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          73.11,
          73.11,
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          73.11,
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        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-02-01T00:00:00",
        "end": "2020-05-01T00:00:00"
      }
    }
  }
}

IBM: Weather Company Data

Metrics

Historical weather data are available at a country, province, and county level globally beginning in mid-2018 up to the most recent available date. Predicted AverageDailyTemperature is additionally available for two weeks from the current date.

Metric Description
AverageDailyTemperature Average daily temperature in Fahrenheit.
AverageDewPoint Average dew point temperature in Fahrenheit.
AverageRelativeHumidity Average relative humidity in percentage.
AverageSurfaceAirPressure Surface air pressure in inches of mercury.
AveragePrecipitation Average hourly precipitation in inches.
AverageWindSpeed Average wind speed in miles per hour.
AverageWindDirection Average wind direction in degrees (1-360).
AverageHorizontalVisibility Horizontal visibility at the observation point in miles.
AverageWindGustSpeed Average wind gust speed in miles per hour.
AverageSnow Average hourly snowfall in inches.
AveragePrecipitationTotal Precipitation amount in the last 24 hours in inches.
AveragePressureTendency Change in the barometric pressure reading over the last hour: 0 = Steady, 1 = Rising or Rapidly Rising, 2 = Falling or Rapidly Falling

Examples

Example: Daily temperature in Santa Clara, California in May, 2020

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request JSON:

{
  "spec": {
    "ids": ["SantaClara_California_UnitedStates"],
    "expressions":["AverageDailyTemperature"],
    "interval":"DAY",
    "start":"2020-05-01",
    "end":"2020-06-01"
  }
}



Response JSON:

{
  "result": {
    "SantaClara_California_UnitedStates": {
      "AverageDailyTemperature": {
        "type": "MaterializedTimeseriesDouble",
        "count": 31,
        "dates": [
          "2020-05-01T00:00:00",
          "2020-05-02T00:00:00",
          "2020-05-03T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
          "2020-05-07T00:00:00",
          "2020-05-08T00:00:00",
          "2020-05-09T00:00:00",
          "2020-05-10T00:00:00",
          "2020-05-11T00:00:00",
          "2020-05-12T00:00:00",
          "2020-05-13T00:00:00",
          "2020-05-14T00:00:00",
          "2020-05-15T00:00:00",
          "2020-05-16T00:00:00",
          "2020-05-17T00:00:00",
          "2020-05-18T00:00:00",
          "2020-05-19T00:00:00",
          "2020-05-20T00:00:00",
          "2020-05-21T00:00:00",
          "2020-05-22T00:00:00",
          "2020-05-23T00:00:00",
          "2020-05-24T00:00:00",
          "2020-05-25T00:00:00",
          "2020-05-26T00:00:00",
          "2020-05-27T00:00:00",
          "2020-05-28T00:00:00",
          "2020-05-29T00:00:00",
          "2020-05-30T00:00:00",
          "2020-05-31T00:00:00"
        ],
        "data": [
          63.666666666666664,
          64.33333333333333,
          63.395833333333336,
          63.895833333333336,
          63.770833333333336,
          66.4375,
          72.79166666666667,
          78.08333333333333,
          70.1875,
          64.58333333333333,
          66.125,
          62.333333333333336,
          61.1875,
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          64.04166666666667,
          65.79166666666667,
          64.91666666666667,
          62.57638888888889,
          60.729166666666664,
          61.770833333333336,
          65.52083333333333,
          64.4375,
          67.60416666666667,
          73.0,
          80.3125,
          86.0625,
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          74.0,
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        "missing": [
          0,
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        ],
        "unit": {
          "id": "degrees_fahrenheit"
        },
        "timeZone": "NONE",
        "interval": "DAY",
        "start": "2020-05-01T00:00:00",
        "end": "2020-06-01T00:00:00"
      }
    }
  }
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of time series evaluation attributes

ids
Array of strings

Unique identifiers (IDs) for the sources to evaluate. Either IDs or a filter is required for evaluating a metric. For example, ["Hubei_China", "NewYork_US"].

expressions
Array of strings

The expressions to evaluate. For example: ["JHU_ConfirmedCases", "JHU_ConfirmedDeaths", "JHU_ConfirmedRecoveries"].

interval
string

Interval of the data to be returned. For example: 'DAY'.

start
string

Start datetime of the time range. For example: '2020-03-01'.

end
string

End datetime of the time range. For example: '2020-03-30'. IMPORTANT: This end date field acts as an open interval. That is, if end is set to “2020-04-04”, then only the data upto and including April 3rd is returned. If you need the data for April 4th, then you must set end date to “2020-04-05".

Responses

200

OK. The request has succeeded.

Response Schema: application/json
result
object

Returned object containing time series data.

id
object

Container of time series evaluation attributes.

expression
object
type
string

Name of the metric.

count
integer

Number of data elements in the returned array.

dates
Array of strings

Array of timestamps corresponding to the returned time series data.

start
string

Timestamp indicating the start of the returned time series data.

end
string

Timestamp indicating the start of the returned time series data.

data
Array of numbers

Array of time series data indicating the value of the metric at each timestamp in dates.

missing
Array of integers

Array of values indicating the percentage of data that is missing for each timestamp in dates. A value of 100 indicates that no data were present for the corresponding timestamp, and a value of 0 indicates that data were fully present for the corresponding timestamp.

interval
string

Interval of the data to be returned. Same as it is in the request. For example: "DAY".

timeZone
string

Time zone where the returned time series data was originally recorded.

post /api/1/outbreaklocation/evalmetrics
https://api.c3.ai/covid/api/1/outbreaklocation/evalmetrics

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "result":
    {
    }
}

GetProjectionHistory

This API returns a collection of time series, where each time series represents projections for a specific metric made at a specific point in time. GetProjectionHistory allows the comparison of different versions of these projections over time.

The following tables show the available time series metrics from each data source that can be evaluated using GetProjectionHistory. Use the expressions from the Metric column in the metric field of the request JSON of the getprojectionhistory API. For example, "metric": "UniversityOfWashington_AdmisMean_Hist".

University of Washington's Institute for Health Metrics and Evaluation: COVID-19 Projections

Metrics

Projections of hospital resource use and COVID-19 deaths available daily at country and province level globally.

NOTE: Most recent projections available are from June 13, 2020.

Metric Description
UniversityOfWashington_AdmisMean_Hist Mean number of hospital admissions per day
UniversityOfWashington_AdmisLower_Hist Lower uncertainy bound of number of hospital admissions per day
UniversityOfWashington_AdmisUpper_Hist Upper uncertainy bound of number of hospital admissions per day
UniversityOfWashington_AllbedMean_Hist Mean number of COVID-19 hospital beds needed per day
UniversityOfWashington_AllbedLower_Hist Lower uncertainy bound of number of COVID-19 hospital beds needed per day
UniversityOfWashington_AllbedUpper_Hist Upper uncertainy bound of number of COVID-19 hospital beds needed per day
UniversityOfWashington_BedoverMean_Hist Mean BedOver, computed as (Number of COVID-19 hospital beds needed per day - Total hospital bed capacity - Average hospital bed use)
UniversityOfWashington_BedoverLower_Hist Lower uncertainty bound of BedOver
UniversityOfWashington_BedoverUpper_Hist Upper uncertainty bound of BedOver
UniversityOfWashington_IcubedMean_Hist Mean number of COVID-19 ICU beds needed per day
UniversityOfWashington_IcubedLower_Hist Lower uncertainty bound of number of COVID-19 ICU beds needed per day
UniversityOfWashington_IcubedUpper_Hist Upper uncertainty bound of number of COVID-19 ICU beds needed per day
UniversityOfWashington_IcuoverMean_Hist Mean ICUOver, computed as (Number of COVID-19 ICU beds needed per day - Total ICU bed capacity - Average ICU bed use)
UniversityOfWashington_IcuoverLower_Hist Lower uncertainy bound of ICUOver
UniversityOfWashington_IcuoverUpper_Hist Upper uncertainy bound of ICUOver
UniversityOfWashington_InvvenMean_Hist Mean number of invasive ventilation procedures needed per day
UniversityOfWashington_InvvenLower_Hist Lower uncertainty bound of number of invasive ventilation procedures needed per day
UniversityOfWashington_InvvenUpper_Hist Upper uncertainty bound of number of invasive ventilation procedures needed per day
UniversityOfWashington_NewicuMean_Hist Mean number of new ICU admissions per day
UniversityOfWashington_NewicuLower_Hist Lower uncertainty bound of new ICU admissions per day
UniversityOfWashington_NewicuUpper_Hist Upper uncertainty bound of new ICU admissions per day
UniversityOfWashington_DeathsMean_Hist Mean number of COVID-19 deaths per day
UniversityOfWashington_DeathsLower_Hist Lower uncertainy bound of number of COVID-19 deaths per day
UniversityOfWashington_DeathsUpper_Hist Upper uncertainy bound of number of COVID-19 deaths per day
UniversityOfWashington_TotdeaMean_Hist Mean number of cumulative COVID-19 deaths
UniversityOfWashington_TotdeaLower_Hist Lower uncertainty bound of number of cumulative COVID-19 deaths
UniversityOfWashington_TotdeaUpper_Hist Upper uncertainty bound of number of cumulative COVID-19 deaths

Examples (Click on the arrows to expand)

The following examples show how to use the GetProjectionHistory API.

Example 1: Retrieve projections made between April 13 and May 1 of mean total cumulative deaths in Spain from April 13 to May 13

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/getprojectionhistory

Request JSON:

{
  "outbreakLocation": "Spain",
  "metric": "UniversityOfWashington_TotdeaMean_Hist",
  "metricStart": "2020-04-13",
  "metricEnd": "2020-05-13",
  "observationPeriodStart": "2020-04-13",
  "observationPeriodEnd": "2020-05-01"
}



Response JSON:

{
  "0": {
    "type": "map<string, any>",
    "value": {
      "Spain": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_TotdeaMean_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 30,
            "dates": [
              "2020-04-13T00:00:00",
              "2020-04-14T00:00:00",
              "2020-04-15T00:00:00",
              "2020-04-16T00:00:00",
              "2020-04-17T00:00:00",
              "2020-04-18T00:00:00",
              "2020-04-19T00:00:00",
              "2020-04-20T00:00:00",
              "2020-04-21T00:00:00",
              "2020-04-22T00:00:00",
              "2020-04-23T00:00:00",
              "2020-04-24T00:00:00",
              "2020-04-25T00:00:00",
              "2020-04-26T00:00:00",
              "2020-04-27T00:00:00",
              "2020-04-28T00:00:00",
              "2020-04-29T00:00:00",
              "2020-04-30T00:00:00",
              "2020-05-01T00:00:00",
              "2020-05-02T00:00:00",
              "2020-05-03T00:00:00",
              "2020-05-04T00:00:00",
              "2020-05-05T00:00:00",
              "2020-05-06T00:00:00",
              "2020-05-07T00:00:00",
              "2020-05-08T00:00:00",
              "2020-05-09T00:00:00",
              "2020-05-10T00:00:00",
              "2020-05-11T00:00:00",
              "2020-05-12T00:00:00"
            ],
            "data": [
              17454.13,
              17637.801,
              17796.099,
              17932.818,
              18051.048,
              18153.307,
              18241.716,
              18318.078999999998,
              18384.188000000002,
              18441.301,
              18490.142,
              18531.572,
              18566.665,
              18596.027,
              18620.422,
              18640.715,
              18657.511000000002,
              18671.181,
              18681.874,
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              18701.035,
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              18711.735,
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            ],
            "missing": [
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            ],
            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-04-13T00:00:00",
            "end": "2020-05-13T00:00:00",
            "expr": "TotdeaMean Projections as of 2020-04-13"
          }
        }
      }
    }
  },
  "1": {
    "type": "map<string, any>",
    "value": {
      "Spain": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_TotdeaMean_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 30,
            "dates": [
              "2020-04-13T00:00:00",
              "2020-04-14T00:00:00",
              "2020-04-15T00:00:00",
              "2020-04-16T00:00:00",
              "2020-04-17T00:00:00",
              "2020-04-18T00:00:00",
              "2020-04-19T00:00:00",
              "2020-04-20T00:00:00",
              "2020-04-21T00:00:00",
              "2020-04-22T00:00:00",
              "2020-04-23T00:00:00",
              "2020-04-24T00:00:00",
              "2020-04-25T00:00:00",
              "2020-04-26T00:00:00",
              "2020-04-27T00:00:00",
              "2020-04-28T00:00:00",
              "2020-04-29T00:00:00",
              "2020-04-30T00:00:00",
              "2020-05-01T00:00:00",
              "2020-05-02T00:00:00",
              "2020-05-03T00:00:00",
              "2020-05-04T00:00:00",
              "2020-05-05T00:00:00",
              "2020-05-06T00:00:00",
              "2020-05-07T00:00:00",
              "2020-05-08T00:00:00",
              "2020-05-09T00:00:00",
              "2020-05-10T00:00:00",
              "2020-05-11T00:00:00",
              "2020-05-12T00:00:00"
            ],
            "data": [
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              23276.096,
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            ],
            "missing": [
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            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-04-13T00:00:00",
            "end": "2020-05-13T00:00:00",
            "expr": "TotdeaMean Projections as of 2020-04-17"
          }
        }
      }
    }
  },
  "2": {
    "type": "map<string, any>",
    "value": {
      "Spain": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_TotdeaMean_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 30,
            "dates": [
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              "2020-04-14T00:00:00",
              "2020-04-15T00:00:00",
              "2020-04-16T00:00:00",
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              "2020-04-19T00:00:00",
              "2020-04-20T00:00:00",
              "2020-04-21T00:00:00",
              "2020-04-22T00:00:00",
              "2020-04-23T00:00:00",
              "2020-04-24T00:00:00",
              "2020-04-25T00:00:00",
              "2020-04-26T00:00:00",
              "2020-04-27T00:00:00",
              "2020-04-28T00:00:00",
              "2020-04-29T00:00:00",
              "2020-04-30T00:00:00",
              "2020-05-01T00:00:00",
              "2020-05-02T00:00:00",
              "2020-05-03T00:00:00",
              "2020-05-04T00:00:00",
              "2020-05-05T00:00:00",
              "2020-05-06T00:00:00",
              "2020-05-07T00:00:00",
              "2020-05-08T00:00:00",
              "2020-05-09T00:00:00",
              "2020-05-10T00:00:00",
              "2020-05-11T00:00:00",
              "2020-05-12T00:00:00"
            ],
            "data": [
              18056.0,
              18579.0,
              19130.0,
              19782.0,
              20287.0,
              20856.0,
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              21875.457000000002,
              22287.144,
              22654.369,
              22978.206000000002,
              23260.472,
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              24153.311,
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              24396.831000000002,
              24448.23,
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              24564.965,
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              24599.471,
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            ],
            "missing": [
              0,
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            ],
            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-04-13T00:00:00",
            "end": "2020-05-13T00:00:00",
            "expr": "TotdeaMean Projections as of 2020-04-21"
          }
        }
      }
    }
  },
  "3": {
    "type": "map<string, any>",
    "value": {
      "Spain": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_TotdeaMean_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 30,
            "dates": [
              "2020-04-13T00:00:00",
              "2020-04-14T00:00:00",
              "2020-04-15T00:00:00",
              "2020-04-16T00:00:00",
              "2020-04-17T00:00:00",
              "2020-04-18T00:00:00",
              "2020-04-19T00:00:00",
              "2020-04-20T00:00:00",
              "2020-04-21T00:00:00",
              "2020-04-22T00:00:00",
              "2020-04-23T00:00:00",
              "2020-04-24T00:00:00",
              "2020-04-25T00:00:00",
              "2020-04-26T00:00:00",
              "2020-04-27T00:00:00",
              "2020-04-28T00:00:00",
              "2020-04-29T00:00:00",
              "2020-04-30T00:00:00",
              "2020-05-01T00:00:00",
              "2020-05-02T00:00:00",
              "2020-05-03T00:00:00",
              "2020-05-04T00:00:00",
              "2020-05-05T00:00:00",
              "2020-05-06T00:00:00",
              "2020-05-07T00:00:00",
              "2020-05-08T00:00:00",
              "2020-05-09T00:00:00",
              "2020-05-10T00:00:00",
              "2020-05-11T00:00:00",
              "2020-05-12T00:00:00"
            ],
            "data": [
              18056.0,
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              19130.0,
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              20856.0,
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              23532.103,
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              25025.802000000003,
              25044.491,
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              25069.838,
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              25084.58
            ],
            "missing": [
              0,
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            ],
            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-04-13T00:00:00",
            "end": "2020-05-13T00:00:00",
            "expr": "TotdeaMean Projections as of 2020-04-22"
          }
        }
      }
    }
  },
  "4": {
    "type": "map<string, any>",
    "value": {
      "Spain": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_TotdeaMean_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 30,
            "dates": [
              "2020-04-13T00:00:00",
              "2020-04-14T00:00:00",
              "2020-04-15T00:00:00",
              "2020-04-16T00:00:00",
              "2020-04-17T00:00:00",
              "2020-04-18T00:00:00",
              "2020-04-19T00:00:00",
              "2020-04-20T00:00:00",
              "2020-04-21T00:00:00",
              "2020-04-22T00:00:00",
              "2020-04-23T00:00:00",
              "2020-04-24T00:00:00",
              "2020-04-25T00:00:00",
              "2020-04-26T00:00:00",
              "2020-04-27T00:00:00",
              "2020-04-28T00:00:00",
              "2020-04-29T00:00:00",
              "2020-04-30T00:00:00",
              "2020-05-01T00:00:00",
              "2020-05-02T00:00:00",
              "2020-05-03T00:00:00",
              "2020-05-04T00:00:00",
              "2020-05-05T00:00:00",
              "2020-05-06T00:00:00",
              "2020-05-07T00:00:00",
              "2020-05-08T00:00:00",
              "2020-05-09T00:00:00",
              "2020-05-10T00:00:00",
              "2020-05-11T00:00:00",
              "2020-05-12T00:00:00"
            ],
            "data": [
              18056.0,
              18579.0,
              19130.0,
              19581.0,
              20146.0,
              20556.0,
              20955.0,
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              21820.0,
              22260.0,
              22627.0,
              23005.0,
              23293.0,
              23624.0,
              23889.678,
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              24327.327,
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              25051.373,
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              25252.49,
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            ],
            "missing": [
              0,
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            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-04-13T00:00:00",
            "end": "2020-05-13T00:00:00",
            "expr": "TotdeaMean Projections as of 2020-04-28"
          }
        }
      }
    }
  }
}
Example 2: Retrieve projections made between March 23 and April 2 of the upper bound of ICU beds needed over capacity in New York from March 23 to April 15

HTTP URL: https://api.c3.ai/covid/api/1/outbreaklocation/getprojectionhistory

Request JSON:

{
  "outbreakLocation": "NewYork_UnitedStates",
  "metric": "UniversityOfWashington_IcuoverUpper_Hist",
  "metricStart": "2020-03-23",
  "metricEnd": "2020-04-15",
  "observationPeriodStart": "2020-03-23",
  "observationPeriodEnd": "2020-04-02"
}



Response JSON:

{
  "0": {
    "type": "map<string, any>",
    "value": {
      "NewYork_UnitedStates": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_IcuoverUpper_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 23,
            "dates": [
              "2020-03-23T00:00:00",
              "2020-03-24T00:00:00",
              "2020-03-25T00:00:00",
              "2020-03-26T00:00:00",
              "2020-03-27T00:00:00",
              "2020-03-28T00:00:00",
              "2020-03-29T00:00:00",
              "2020-03-30T00:00:00",
              "2020-03-31T00:00:00",
              "2020-04-01T00:00:00",
              "2020-04-02T00:00:00",
              "2020-04-03T00:00:00",
              "2020-04-04T00:00:00",
              "2020-04-05T00:00:00",
              "2020-04-06T00:00:00",
              "2020-04-07T00:00:00",
              "2020-04-08T00:00:00",
              "2020-04-09T00:00:00",
              "2020-04-10T00:00:00",
              "2020-04-11T00:00:00",
              "2020-04-12T00:00:00",
              "2020-04-13T00:00:00",
              "2020-04-14T00:00:00"
            ],
            "data": [
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            "missing": [
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            ],
            "timeZone": "NONE",
            "interval": "DAY",
            "start": "2020-03-23T00:00:00",
            "end": "2020-04-15T00:00:00",
            "expr": "IcuoverUpper Projections as of 2020-03-25"
          }
        }
      }
    }
  },
  "1": {
    "type": "map<string, any>",
    "value": {
      "NewYork_UnitedStates": {
        "type": "map<string, any>",
        "value": {
          "UniversityOfWashington_IcuoverUpper_Hist": {
            "type": "UniversityOfWashingtonProjectionResult",
            "count": 23,
            "dates": [
              "2020-03-23T00:00:00",
              "2020-03-24T00:00:00",
              "2020-03-25T00:00:00",
              "2020-03-26T00:00:00",
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              "2020-04-05T00:00:00",
              "2020-04-06T00:00:00",
              "2020-04-07T00:00:00",
              "2020-04-08T00:00:00",
              "2020-04-09T00:00:00",
              "2020-04-10T00:00:00",
              "2020-04-11T00:00:00",
              "2020-04-12T00:00:00",
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            ],
            "data": [
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header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
outbreakLocation
string

Unique ID of the OutbreakLocation for which the metric should be evaluated, e.g. "California_UnitedStates"

metric
string

Metric to be evaluated, e.g. "UniversityOfWashington_AdmisLower_Hist"

metricStart
string

Start datetime over which the metric should be evaluated. E.g. '2020-04-01' indicates that you want to view the value of the metric beginning at '2020-04-01'.

metricEnd
string

End datetime over which the metric should be evaluated. E.g. '2020-04-30' indicates that you want to view the value of the metric ending at '2020-04-30'.

observationPeriodStart
string

Start datetime for which projection metrics to view. E.g. '2020-04-01' indicates that you want to view projections calculated after '2020-04-01'.

observationPeriodEnd
string

(Optional) End datetime for which projection metrics to view. E.g. '2020-04-30' indicates that you want to view projections calculated before '2020-04-30'.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
id
object
type
string

map<string, any>

value
object
post /api/1/outbreaklocation/getprojectionhistory
https://api.c3.ai/covid/api/1/outbreaklocation/getprojectionhistory

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "outbreakLocation": "string",
  • "metric": "string",
  • "metricStart": "string",
  • "metricEnd": "string",
  • "observationPeriodStart": "string",
  • "observationPeriodEnd": "string"
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "id":
    {
    }
}

LocationExposure

LocationExposure stores information based on the movement of people's mobile devices across locations over time. It stores the following:

  • Location exposure index (LEX) for a pair of locations (locationTarget, locationVisited): the fraction of mobile devices that pinged in locationTarget on a date that also pinged in locationVisited at least once during the previous 14 days. The pair (locationTarget, locationVisited) can be two county locations or two state locations.
  • Device count: the number of distinct mobile devices that pinged at locationTarget on the date.

See more detailed description of the methodology and index definition here.

GetLocationExposures

This API returns:

  • LocationExposure objects in JSON format, each of which represents a LEX data point for a pair of locations on a given day;
  • Daily device counts of the locationTarget field specified in the parameter.

To retrieve LEX values at the state level, all fields are optional. To retrieve LEX values at the county level, the locationTarget and locationVisited fields are required.

LEX values are available daily from January 20, 2020 through August 19, 2020 and for Wednesdays and Saturdays after August 19, 2020.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Get location exposure indices between San Francisco County, California and Clark County, Nevada and device counts for San Francisco County, California.

HTTP URL: https://api.c3.ai/covid/api/1/locationexposure/getlocationexposures

Request JSON:

{
  "spec": {
    "locationTarget": "SanFrancisco_California_UnitedStates",
    "locationVisited": "Clark_Nevada_UnitedStates",
    "start": "2020-01-20",
    "end": "2020-01-25"
  }
}

Response JSON:

{
  "locationExposures": {
    "type": "json",
    "value": [
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "locationVisited": "Clark_Nevada_UnitedStates",
        "timestamp": "2020-01-20T00:00:00.000Z",
        "value": 0.014275999999999999
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "locationVisited": "Clark_Nevada_UnitedStates",
        "timestamp": "2020-01-21T00:00:00.000Z",
        "value": 0.015028999999999999
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "locationVisited": "Clark_Nevada_UnitedStates",
        "timestamp": "2020-01-22T00:00:00.000Z",
        "value": 0.014157
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "locationVisited": "Clark_Nevada_UnitedStates",
        "timestamp": "2020-01-23T00:00:00.000Z",
        "value": 0.013359000000000001
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "locationVisited": "Clark_Nevada_UnitedStates",
        "timestamp": "2020-01-24T00:00:00.000Z",
        "value": 0.014417
      }
    ]
  },
  "deviceCounts": {
    "type": "json",
    "value": [
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "timestamp": "2020-01-20T00:00:00.000Z",
        "value": 27738.0
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "timestamp": "2020-01-21T00:00:00.000Z",
        "value": 34867.0
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "timestamp": "2020-01-22T00:00:00.000Z",
        "value": 35460.0
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "timestamp": "2020-01-23T00:00:00.000Z",
        "value": 35033.0
      },
      {
        "locationTarget": "SanFrancisco_California_UnitedStates",
        "timestamp": "2020-01-24T00:00:00.000Z",
        "value": 34472.0
      }
    ]
  }
}
Example 2: Get location exposure indices between California and all other states for a single date.

HTTP URL: https://api.c3.ai/covid/api/1/locationexposure/getlocationexposures

Request JSON:

{
  "spec": {
    "locationTarget": "California_UnitedStates",
    "start": "2020-06-01",
    "end": "2020-06-02"
  }
}

Response JSON:

{
  "locationExposures": {
    "type": "json",
    "value": [
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Arizona_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.021019
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "RhodeIsland_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 7.099999999999999E-5
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Hawaii_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 1.96E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Nevada_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.018249
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Delaware_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 1.0800000000000001E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Connecticut_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 2.8700000000000004E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "DistrictofColumbia_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 8.1E-5
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Idaho_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001966
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "NorthDakota_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 1.82E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Vermont_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 3.6E-5
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Oregon_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.0061979999999999995
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Virginia_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001052
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Arkansas_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.0015
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Indiana_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001734
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Montana_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 5.8E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "California_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.9972110000000001
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Illinois_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.002739
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Florida_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001376
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Wyoming_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001715
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "SouthDakota_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 3.28E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Pennsylvania_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001351
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Iowa_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001347
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Wisconsin_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 6.65E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Louisiana_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 9.98E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Michigan_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 6.24E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Minnesota_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 6.76E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Tennessee_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.0018629999999999999
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Nebraska_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.0015539999999999998
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Oklahoma_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.002665
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "SouthCarolina_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 6.95E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Mississippi_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 9.480000000000001E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Maryland_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 5.56E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "NewYork_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 8.97E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "NewJersey_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 8.730000000000001E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Missouri_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.002226
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "WestVirginia_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 3.09E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "NewMexico_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.004255
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Alabama_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001059
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "NorthCarolina_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001403
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Washington_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.002957
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Maine_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 6.8E-5
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Massachusetts_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 3.49E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Kansas_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001132
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "NewHampshire_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 1.1100000000000001E-4
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Georgia_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001556
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Utah_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.005227000000000001
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Ohio_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.001568
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Texas_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.006821
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Colorado_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 0.003041
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Alaska_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 6.8E-5
      },
      {
        "locationTarget": "California_UnitedStates",
        "locationVisited": "Kentucky_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 8.69E-4
      }
    ]
  },
  "deviceCounts": {
    "type": "json",
    "value": [
      {
        "locationTarget": "California_UnitedStates",
        "timestamp": "2020-06-01T00:00:00.000Z",
        "value": 1302856.0
      }
    ]
  }
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object
locationTarget
string

ID of the OutbreakLocation for locationTarget, where mobile devices in question were present on the date (optional for state level). For example: California_UnitedStates

locationVisited
string

ID of the OutbreakLocation for locationVisited, where mobile devices visited in the past 14 days (optional for state level).

start
string

Start datetime of the time range. For example: '2020-03-01' (optional).

end
string

End datetime of the time range. For example: '2020-03-30' (optional).

Responses

200

OK. The request has succeeded.

Response Schema: application/json
result
object

Returned object containing LEX and device counts.

deviceCounts
Array of objects (DeviceCounts)

An array of DeviceCount objects representing the number of distinct devices that pinged at locationTarget on a particular day.

locationExposures
Array of objects (LocationExposures)

An array of LocationExposure objects that each represents the fraction of devices which pinged in locationTarget on a certain day that also pinged in locationVisited at least once in the previous 14 days.

post /api/1/locationexposure/getlocationexposures
https://api.c3.ai/covid/api/1/locationexposure/getlocationexposures

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "result":
    {
    }
}

PopulationData

PopulationData stores historical population measurements and estimates by age and gender for countries (using World Bank data and US Census Bureau: International Census), US counties and states (using US Census Bureau data). It also includes country-level population projections from US Census Bureau: International Census.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
parent OutbreakLocation C3.ai Type OutbreakLocation affiliated with this population value.
year int Year population estimate was taken, between 2010-2019 for US Census data, and between 1960-2018 for World Bank data.
populationAge string Age range for the population estimate. Allowed values: Total, Median, <5, 5 - 9, 5 - 13, 10 - 14, 14 -17, 15 - 19, 15 - 44, 18 - 24, 18 - 64, 20 - 24, 25 - 29, 25 - 44, 30 - 34, 35 - 39, 40 - 44, 45 - 49, 45 - 64, 50 - 54, 55 - 59, 60 -64, 65 - 69, 70 - 74, 75 - 79, 80 - 84, >=16, >=18, >=65, >=85 Also allowed for US Census Bureau: International Census: 0, 1, 2, ..., 100.
gender string Gender of the population estimate. Allowed values: Male/Female, Male, Female.
race string Race characteristic of the population estimate. Allowed values:
White alone,
Black or African American alone,
American Indian and Alaska Native alone,
Asian alone,
Native Hawaiian and Other Pacific Islander alone,
Two or More Races,
White alone or in combination,
Black or African American alone or in combination,
American Indian and Alaska Native alone or in combination,
Asian alone or in combination,
Native Hawaiian and Other Pacific Islander alone or in combination,
Any race.
ethnicity string Ethnicity characteristic of the population estimate. Allowed values: Hispanic, Not Hispanic, Hispanic/Not Hispanic.
estimate boolean True if the population value is an estimate based on the American Housing Survey, false otherwise.
median boolean True if the value is the median age of all individuals in the location, false if the value is a population count.
value double Population count, or median age if median is true.
minAge int Lower limit of the population age range.
maxAge int Upper limit of the population age range.
origin string The source of the population data. Allowed values: International Census Bureau, United States Census, World Bank

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch the number of seniors and total population using the 2010 US Census

HTTP URL: https://api.c3.ai/covid/api/1/populationdata/fetch

Request JSON:

{
  "spec": {
    "filter": "contains(parent, 'UnitedStates') && (populationAge == '>=65' || populationAge == 'Total') && gender == 'Male/Female' && year == 2010 && estimate == 'False'",
    "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
      "year": 2010,
      "gender": "Male/Female",
      "populationAge": ">=65",
      "estimate": false,
      "median": false,
      "value": 4203,
      "minAge": 65,
      "id": "2010_Abbeville_SouthCarolina_UnitedStates_Male/Female_>=65_Census_PopulationCount",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-30T01:45:45Z",
        "createdBy": "dataloader",
        "updated": "2020-04-30T01:45:45Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-04-30T02:23:25Z",
        "sourceFile": "flattenedCensusData.csv",
        "fetchInclude": "[]",
        "fetchType": "PopulationData"
      },
      "version": 1,
      "parent": {
        "id": "Abbeville_SouthCarolina_UnitedStates"
      },
      "timestamp": "2010-01-01T00:00:00Z"
    },
    {
      "year": 2010,
      "gender": "Male/Female",
      "populationAge": "Total",
      "estimate": false,
      "median": false,
      "value": 25417,
      "id": "2010_Abbeville_SouthCarolina_UnitedStates_Male/Female_Total_Census_PopulationCount",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-30T01:45:45Z",
        "createdBy": "dataloader",
        "updated": "2020-04-30T01:45:45Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-04-30T02:23:25Z",
        "sourceFile": "flattenedCensusData.csv",
        "fetchInclude": "[]",
        "fetchType": "PopulationData"
      },
      "version": 1,
      "parent": {
        "id": "Abbeville_SouthCarolina_UnitedStates"
      },
      "timestamp": "2010-01-01T00:00:00Z"
    },
    {
      "year": 2010,
      "gender": "Male/Female",
      "populationAge": ">=65",
      "estimate": false,
      "median": false,
      "value": 7886,
      "minAge": 65,
      "id": "2010_Acadia_Louisiana_UnitedStates_Male/Female_>=65_Census_PopulationCount",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-30T01:45:45Z",
        "createdBy": "dataloader",
        "updated": "2020-04-30T01:45:45Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-04-30T02:10:34Z",
        "sourceFile": "flattenedCensusData.csv",
        "fetchInclude": "[]",
        "fetchType": "PopulationData"
      },
      "version": 1,
      "parent": {
        "id": "Acadia_Louisiana_UnitedStates"
      },
      "timestamp": "2010-01-01T00:00:00Z"
    },

    ...

  ],
  "count": 6470,
  "hasMore": false
}
Example 2: Fetch the projected Canadian female population in the age group 75-79 in 2040 from US Census Bureau: International Census.

HTTP URL: https://api.c3.ai/covid/api/1/populationdata/fetch

Request JSON:

{
  "spec": {
    "filter": "contains(parent, 'Canada') && (populationAge == '75-79') && gender == 'Female' && year(timestamp) == 2040 && origin == 'International Census Bureau'",
    "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
      "gender": "Female",
      "populationAge": "75-79",
      "estimate": false,
      "percent": false,
      "value": 1206279.0,
      "minAge": 75,
      "maxAge": 79,
      "origin": "International Census Bureau",
      "id": "2040_Canada_75To79_MidyearFemalePopulation",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-04T04:37:54Z",
        "createdBy": "dataloader",
        "updated": "2020-06-04T04:37:54Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-04T06:59:22Z",
        "sourceFile": "midyear_populations_5yr_age_sex.csv",
        "fetchInclude": "[]",
        "fetchType": "PopulationData"
      },
      "version": 1,
      "parent": {
        "id": "Canada"
      },
      "timestamp": "2040-06-01T00:00:00Z"
    }
  ],
  "count": 1,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/populationdata/fetch
https://api.c3.ai/covid/api/1/populationdata/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

LaborDetail

LaborDetail stores historical monthly labor force and employment data for the US counties and states from US Bureau of Labor Statistics.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
parent OutbreakLocation C3.ai Type OutbreakLocation affiliated with this employment statistic.
year int Year the labor statistics was taken, between 2000 to 2020.
month int Month the labor statistics was taken.
laborForce int Civilian labor force population.
employedPopulation int Civilian noninstitutional population aged 16 years and over that is employed.
unemployedPopulation int Number of civilians aged 16 years or older who: had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment sometime during the 4-week period ending with the reference week.
unemploymentRate double Unemployed population divided by labor force population, in percent.
origin string The source of the labor data. Allowed values: BLS.

Examples (Click on the arrows to expand)

The following example shows how to use this API.

Example: Fetch the unemployment rates of counties in California in March, 2020

HTTP URL: https://api.c3.ai/covid/api/1/labordetail/fetch

Request JSON:

{
  "spec": {
    "filter": "year == 2020 && month == 3 && contains(parent, 'California_UnitedStates')",
    "include": "unemploymentRate",
    "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
      "unemploymentRate": 3.91542181365508,
      "id": "2020_03_Alameda_California_UnitedStates",
      "meta": {
        "fetchInclude": "[unemploymentRate,id,version]",
        "fetchType": "LaborDetail"
      },
      "version": 1
    },
    {
      "unemploymentRate": 7.3584905660377355,
      "id": "2020_03_Alpine_California_UnitedStates",
      "meta": {
        "fetchInclude": "[unemploymentRate,id,version]",
        "fetchType": "LaborDetail"
      },
      "version": 1
    },
    {
      "unemploymentRate": 5.476673427991886,
      "id": "2020_03_Amador_California_UnitedStates",
      "meta": {
        "fetchInclude": "[unemploymentRate,id,version]",
        "fetchType": "LaborDetail"
      },
      "version": 1
    },
    {
      "unemploymentRate": 6.83884902251693,
      "id": "2020_03_Butte_California_UnitedStates",
      "meta": {
        "fetchInclude": "[unemploymentRate,id,version]",
        "fetchType": "LaborDetail"
      },
      "version": 1
    },
    {
      "unemploymentRate": 5.496396186933271,
      "id": "2020_03_Calaveras_California_UnitedStates",
      "meta": {
        "fetchInclude": "[unemploymentRate,id,version]",
        "fetchType": "LaborDetail"
      },
      "version": 1
    },
    ...
  ],
  "count": 116,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/labordetail/fetch
https://api.c3.ai/covid/api/1/labordetail/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

LineListRecord

LineListRecord stores individual-level crowdsourced information from laboratory-confirmed COVID-19 patients. Information includes gender, age, symptoms, travel history, location, reported onset, confirmation dates, and discharge status.

NOTE: LineListRecord data are available through April 30, 2020.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
location OutbreakLocation C3.ai Type OutbreakLocation.
locationType string Specific location where the individual was assessed. For example: Yokohama Port, National Centre for Infectious Diseases.
isGroundZero boolean Is the patient located in Wuhan? Allowed values: true, false.
livesInGroundZero boolean Does the patient live in Wuhan? Allowed values: true, false.
traveledToGroundZero boolean Did the patient travel to Wuhan? Allowed values: true, false.
relevantTravelHistoryLocation string Details of patient travel history including locations travelled.
recordSource string Public source from which this patient information collected.
gender string Gender of the patient. Allowed values: male, female, other.
age int Age of the patient.
ageRange string Age range, if age is provided as age range.
groundZeroExposure string Description regarding whether the individual had exposure to the Wuhan marketplace.
chronicDisease string Medical history of chronic disease symptoms.
symptomStartDate datetime Date COVID-19 symptoms appeared.
exposureStartDate datetime Date COVID-19 exposure started.
exposureEndDate datetime Date COVID-19 exposure ended.
caseConfirmationDate datetime Date the case was confirmed.
symptoms string Description of the symptoms.
caseConfirmationDate datetime Date of the confirmation of the case.
caseInCountry int Ordinal number indicating whether this case is, e.g., 20th or 32nd, in that country.
hospitalAdmissionDate datetime Date admitted to hospital.
relevantTravelHistoryDates string Dates when the patient travelled in recent past.
outcome string Outcome of the treatment. Allowed values: stable, discharge, death, "" (null value).
didDie boolean Did patient die? Allowed values: true, false.
didRecover boolean Did patient recover? Allowed values: true, false.
outcomeDate datetime Date of the outcome.
traveler boolean Whether the patient is a local resident or a traveler to the location.
internationTraveler boolean Whether the patient is an international traveler.
domesticTraveler boolean Whether the patient is an domestic traveler.
notes string Clinical notes.
infectedBy LineListRecord C3.ai Type LineListRecord that represents the record of the patient who infected this patient. Available for LineListRecord data from the Data Science for COVID-19: South Korea Dataset.
contactNumber int The number of the patient's contacts with people. Available for LineListRecord data from the Data Science for COVID-19: South Korea Dataset.
hospitalReleaseDate datetime Date the patient is released from the hospital. Available for LineListRecord data from the Data Science for COVID-19: South Korea Dataset.
deceasedDate datetime Date of death. Available for LineListRecord data from the Data Science for COVID-19: South Korea Dataset.
infectionOrder int Order of infection. Possible values: 1: patient infected in Wuhan; 2: patient infected was from the one whose infection order is 1; 3: patient infected was from the one whose infection order is 2.
travelHistory PatientRoute List of C3.ai Type PatientRoute objects associated with this patient record.
hasTravelHistory boolean Whether the patient has any associated PatientRoute data.
lineListSource string Data source for the crowdsourced line list records. Allowed values: OPEN (nCoV2019 Data Working Group), DXY (MOBS Lab).

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch all Line List Records of males of age under 20 with travel history to Wuhan, tracked by the nCoV-2019 Data Working Group

HTTP URL: https://api.c3.ai/covid/api/1/linelistrecord/fetch

Request JSON:

{
  "spec": {
      "filter": "gender == 'male' && lineListSource == 'OPEN' && age <= 20 && contains(relevantTravelHistoryLocation,'Wuhan')"
 }
}



Response JSON:

{
  "objs": [
    {
        "location": {
            "id": "GuidingCountyQiannanPrefecture_Guizhou_China"
        },
        "age": 20,
        "gender": "male",
        "symptoms": "discomfort",
        "symptomStartDate": "2020-01-18T00:00:00Z",
        "hospitalAdmissionDate": "2020-01-18T00:00:00Z",
        "caseConfirmationDate": "2020-01-22T00:00:00Z",
        "isGroundZero": false,
        "livesInGroundZero": false,
        "traveledToGroundZero": false,
        "relevantTravelHistoryLocation": "Wuhan",
        "notes": "student in a college in Wuhan",
        "lineListSource": "OPEN",
        "id": "0017f762-239b-440b-94db-e66bd08d516f",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-03T11:46:38Z",
            "createdBy": "dataloader",
            "updated": "2020-04-03T11:46:38Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-03T11:53:40Z",
            "sourceFile": "COVID19CanonicalOpenLineList.csv",
            "fetchInclude": "[]",
            "fetchType": "LineListRecord"
        },
        "version": 1
    },
...

}
Example 2: Fetch first two thousand (2000) LineList records (request only)

HTTP URL: https://api.c3.ai/covid/api/1/linelistrecord/fetch

Request JSON:

{ // By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
 }
}
Example 3: Fetch the first two thousand (2000) Line List Records tracked in the nCoV-2019 Data Working Group (request only)

HTTP URL: https://api.c3.ai/covid/api/1/linelistrecord/fetch

Request JSON:

{ // See the `lineListSource` field for allowed values in the table of fields. By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
      "filter": "lineListSource == 'OPEN'"
  }
}
Example 4: Fetch the first two thousand (2000) Line List Records tracked by MOBS Lab (request only)

HTTP URL: https://api.c3.ai/covid/api/1/linelistrecord/fetch

Request JSON:

{ // See the `lineListSource` field for allowed values in the table of fields. By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
      "filter": "lineListSource == 'DXY'"
  }
}
Example 5: Fetch the first two thousand (2000) male-patient Line List Records tracked by MOBS Lab (request only)

HTTP URL: https://api.c3.ai/covid/api/1/linelistrecord/fetch

Request JSON:

{ // See the `lineListSource` field for allowed values in the table of fields. By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
      "filter": "gender == 'male' && lineListSource == 'DXY'"
  }
}
Example 6: Python example using "offset" parameter 
import requests
import pandas as pd

def read_data(url, body):
    has_more = True
    body['spec']['offset'] = 0
    df = pd.DataFrame()
    while has_more:
        print(body['spec']['offset'])
        response = requests.post(
            url,
            json = body,
            headers = {'Accept' : 'application/json'}
        )
        new_df = pd.json_normalize(response.json()['objs'])
        df = df.append(new_df)
        has_more = response.json()['hasMore']
        body['spec']['offset'] += new_df.shape[0]
    return df

url = 'https://api.c3.ai/covid/api/1/linelistrecord/fetch'
payload = {
    "spec": {
        "filter": "exists(hospitalAdmissionDate)",
        "include": "caseConfirmationDate, outcomeDate, hospitalAdmissionDate, age"
    }
}
read_data(url, payload)
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/linelistrecord/fetch
https://api.c3.ai/covid/api/1/linelistrecord/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

PatientRoute

PatientRoute records all locations (e.g. hospitals, stores, restaurants) visited by COVID-19 patients in South Korea over time.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
parent LineListRecord C3.ai Type LineListRecord representing the patient.
location OutbreakLocation C3.ai Type OutbreakLocation of the patient visit.
locationType string Type of location visited by the patient. Examples include hospital, airport, bakery, and post_office.
coordinate LatLong C3.ai Type LatLong representing latitude and longitude of location.
timestamp datetime Date when the patient visited the location.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch all locations visited by monitored patients in Seoul

HTTP URL: https://api.c3.ai/covid/api/1/patientroute/fetch

Request JSON:

{
  "spec": {
    "filter": "contains(location, 'Seoul_KoreaSouth')",
    "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Gangbuk-gu_Seoul_KoreaSouth"
      },
      "locationType": "etc",
      "coordinates": {
        "latitude": 37.63886,
        "longitude": 127.0231577
      },
      "id": "00091b82-3916-4915-81d4-6d71c30642c5",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-05T04:37:57Z",
        "createdBy": "dataloader",
        "updated": "2020-05-05T04:37:57Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-05T04:54:46Z",
        "fetchInclude": "[]",
        "fetchType": "PatientRoute"
      },
      "version": 1,
      "parent": {
        "id": "1000000111"
      },
      "timestamp": "2020-02-22T00:00:00Z"
    },
    ...
  ],
  "count": 1384,
  "hasMore": false
}
Example 2: Fetch all locations visited by monitored patients in a specific coordinate range

HTTP URL: https://api.c3.ai/covid/api/1/patientroute/fetch

Request JSON:

{
  "spec": {
    "filter": "coordinates.latitude > 37.58 && coordinates.latitude < 37.59 && coordinates.longitude > 127.05 && coordinates.latitude < 127.07",
    "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Dongdaemun-gu_Seoul_KoreaSouth"
      },
      "locationType": "hospital",
      "coordinates": {
        "latitude": 37.5878949,
        "longitude": 127.0653215
      },
      "id": "0b492523-f776-4021-9739-3cdd86b95cae",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-05T04:37:57Z",
        "createdBy": "dataloader",
        "updated": "2020-05-05T04:37:57Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-05T04:54:46Z",
        "fetchInclude": "[]",
        "fetchType": "PatientRoute"
      },
      "version": 1,
      "parent": {
        "id": "1000000040"
      },
      "timestamp": "2020-02-25T00:00:00Z"
    },
    ...
  ],
  "count": 15,
  "hasMore": false
}
Example 3: Fetch all stores visited by a specific patient, along with their outcome

HTTP URL: https://api.c3.ai/covid/api/1/patientroute/fetch

Request JSON:

{
  "spec": {
    "filter": "parent.id == '1000000231' && locationType == 'store'",
    "include": "this, parent.outcome"
  }
}



Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Yeongdong-gun_Chungcheongbuk-do_KoreaSouth"
      },
      "locationType": "store",
      "coordinates": {
        "latitude": 36.1727531,
        "longitude": 127.7737169
      },
      "id": "08461a81-6589-44d7-9544-9a557217128d",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-05T04:37:57Z",
        "createdBy": "dataloader",
        "updated": "2020-05-05T04:37:57Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-05T04:54:46Z",
        "fetchInclude": "[this,{parent:[outcome,id]}]",
        "fetchType": "PatientRoute"
      },
      "version": 1,
      "parent": {
        "outcome": "released",
        "id": "1000000231"
      },
      "timestamp": "2020-02-27T00:00:00Z"
    },
    ...
  ],
  "count": 11,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/patientroute/fetch
https://api.c3.ai/covid/api/1/patientroute/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

BiologicalAsset

BiologicalAsset stores the metadata of the genome sequences collected from SARS-CoV-2 samples in the National Center for Biotechnology Information Virus Database. See also Sequence.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string Genome sequence ID.
assetType string Biological molecule type. Allowed values: protein sequence, nucleotide sequence.
genus string Genus. Allowed values: Betacoronavirus.
family string Family. Allowed values: Coronaviridae.
species string The species that the BiologicalAsset relates to.
authors string Individuals cited as source of genome sequences.
genBankTitle string Sequence description in the National Center for Biotechnology Information Virus Database.
publications string Public source from which this patient information collected.
name string Name of the patient.
location OutbreakLocation C3.ai Type OutbreakLocation.
sequence Sequence C3.ai Type Sequence.
nucleotideCompleteness string The completeness of thie BiologicalAsset, available if the asset is a nucleotide.
sequenceType string The type of sequence this represents. Possible values: GenBank and RefSeq.
bioSample string Name of the sample from which this asset is found.
host string The host organism from which the sample was taken. Allowed values: Homo sapiens.
isolationSource string Source from which the sample was taken. Allowed values: oronasopharynx, blood, feces, lung, swab, lung, oronasopharynx.
collectionDate datetime Date when samples were collected.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch metadata for all BiologicalAsset sequences (request only)

HTTP URL: https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request JSON:

{
  "spec": {
    "limit": -1
  }
}
Example 2: Fetch metadata for all BiologicalAsset protein sequences (request only)

HTTP URL: https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request JSON:

{
  "spec": {
      "filter": "assetType == 'protein sequence'",
      "limit": -1
  }
}
Example 3: Fetch metadata for all BiologicalAsset nucleotide sequences (request only)

HTTP URL: https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request JSON:

{
  "spec": {
      "filter": "assetType == 'nucleotide sequence'",
      "limit": -1
  }
}
Example 4: Fetch metadata for all BiologicalAsset sequences sampled from blood (request only)

HTTP URL: https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request JSON:

{
  "spec": {
      "filter": "isolationSource == 'blood'",
      "limit": -1
  }
}
Example 5: Fetch metadata for all BiologicalAsset sequences taken in Japan and sampled from feces

HTTP URL: https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request JSON:

{
  "spec": {
      "filter": "isolationSource == 'feces' && location == 'Japan'",
      "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
        "location": {
            "id": "Japan"
        },
        "sequence": {
            "id": "AB889995"
        },
        "assetType": "nucleotide sequence",
        "host": "Rhinolophus cornutus",
        "species": "Severe acute respiratory syndrome-related coronavirus",
        "genus": "Betacoronavirus",
        "family": "Coronaviridae",
        "isolationSource": "feces",
        "authors": "Suzuki,J., Sato,R., Kobayashi,T., Aoi,T., Harasawa,R.",
        "genBankTitle": "SARS bat coronavirus RdRp gene for RNA dependent RNA polymerase, partial cds, strain: Is39",
        "collectionDate": "2013-01-01T00:00:00Z",
        "id": "AB889995",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-02T03:39:44Z",
            "createdBy": "dataloader",
            "updated": "2020-04-02T03:39:44Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-02T03:39:50Z",
            "sourceFile": "nucleotide_sequence_metadata.csv",
            "fetchInclude": "[]",
            "fetchType": "BiologicalAsset"
        },
...

}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/biologicalasset/fetch
https://api.c3.ai/covid/api/1/biologicalasset/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

Sequence

Sequence stores the genome sequences collected from SARS-CoV-2 samples in the National Center for Biotechnology Information Virus Database. See also BiologicalAsset.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string Genome sequence ID.
parent BiologicalAsset C3.ai Type BiologicalAsset.
sequence string Actual genomic sequence. Should be in uppercase.
sequenceType string Biological molecule type. Allowed values: protein, complete, partial, GenBank.
length int Length of the sequence, as in length(sequence).

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch all sequences from Sequence (request only)

HTTP URL: https://api.c3.ai/covid/api/1/sequence/fetch

Request JSON:

{
  "spec": {
    "limit": -1
  }
}
Example 2: Fetch all protein sequences (request only)

HTTP URL: https://api.c3.ai/covid/api/1/sequence/fetch

Request JSON:

{
  "spec": {
      "filter": "sequenceType == 'protein'",
      "limit": -1
  }
}
Example 3: Fetch all complete genome sequences (request only)

HTTP URL: https://api.c3.ai/covid/api/1/sequence/fetch

Request JSON:

{
  "spec": {
      "filter": "sequenceType == 'complete'",
      "limit": -1
  }
}
Example 4: Fetch all Sequences with <= 100 Base Pairs (or Amino Acids) (request only)

HTTP URL: https://api.c3.ai/covid/api/1/sequence/fetch

Request JSON:

{
  "spec": {
      "filter": "length <= 100",
      "limit": -1
  }
}
Example 5: Fetch sequence 5R7Y_A

HTTP URL: https://api.c3.ai/covid/api/1/sequence/fetch

Request JSON:

{
  "spec": {
      "filter": "id == '5R7Y_A'"
  }
}



Response JSON:

{
  "objs": [
    {
        "parent": {
            "id": "5R7Y_A"
        },
        "sequence": "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQ",
        "sequenceType": "protein",
        "length": 306,
        "id": "5R7Y_A",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-02T00:37:16Z",
            "createdBy": "dataloader",
            "updated": "2020-04-02T00:37:16Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-02T16:54:53Z",
            "sourceFile": "protein_sequences.csv",
            "fetchInclude": "[]",
            "fetchType": "Sequence"
        },
        "version": 65537
    }
],
"count": 1,
"hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/sequence/fetch
https://api.c3.ai/covid/api/1/sequence/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

Subsequence

Subsequence stores indices of critical segments within a nucleotide or amino acid sequence, e.g., introns, exons, and proteins. Indices are 1-indexed.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
parent Sequence The Sequence this Subsequence references.
startIndex int The position where the value of this Subsequence begins in relation to the parent's sequence.
endIndex int The position where the second value of this Subsequence ends in relation to the parent's sequence.
secondStartIndex int The position where the second value of this Subsequence begins in relation to the parent's sequence, for the second subsequence. Available only if this Subsequence is a join of two substrings.
secondEndIndex int The position where the second value of this Subsequence ends in relation to the parent's sequence, for the second subsequence. Available only if this Subsequence is a join of two substrings.
value string The value of this Subsequence code substring. If this Subsequence is a join of two substrings, then this field is the concatenation of both substrings.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch all Subsequences (request only)

HTTP URL: https://api.c3.ai/covid/api/1/subsequence/fetch

Request JSON:

{
  "spec": {
    "limit": -1
  }
}
Example 2: Fetch all subsequences of genome MN975266 (request only)

HTTP URL: https://api.c3.ai/covid/api/1/subsequence/fetch

Request JSON:

{
  "spec": {
    "filter": "parent == 'MN975266'"
  }
}



Response JSON:

{
  "objs": [
    {
        "parent": {
            "id": "MN975266"
        },
        "startIndex": 1,
        "endIndex": 107,
        "id": "1-107",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-03T15:27:09Z",
            "createdBy": "dataloader",
            "updated": "2020-04-09T03:15:03Z",
            "updatedBy": "dataloader",
            "timestamp": "2020-04-09T03:15:06Z",
            "sourceFile": "sequences_coding_region.csv",
            "fetchInclude": "[]",
            "fetchType": "Subsequence"
        },
        "version": 14
    }
],
"count": 1,
"hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/subsequence/fetch
https://api.c3.ai/covid/api/1/subsequence/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

AminoAcidLookup

AminoAcidLookup contains the lookup table to map the IUPAC Amino Acid Codes to their full names and abbreviations.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string The IUPAC Code.
name string Full name of the amino acid.
abbreviation string Three-letter abbreviation of the amino acid.

Example (Click on the arrow to expand)

The following example shows how to use this API.

Fetch all AminoAcids

HTTP URL: https://api.c3.ai/covid/api/1/aminoacidlookup/fetch

Request JSON:

{
  "spec": {

  }
}



Response JSON:

{
  "objs": [
    {
        "abbreviation": "ala",
        "id": "A",
        "name": "alanine",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-03-31T16:05:46Z",
            "createdBy": "provisioner",
            "updated": "2020-03-31T16:05:46Z",
            "updatedBy": "provisioner",
            "timestamp": "2020-03-31T16:05:46Z",
            "fetchInclude": "[]",
            "fetchType": "AminoAcidLookup"
        },
        "version": 1
    },
  ...
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/aminoacidlookup/fetch
https://api.c3.ai/covid/api/1/aminoacidlookup/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

NucleotideLookup

NucleotideLookup contains the lookup table to map the IUPAC Nucleotide Codes to their full names and abbreviations.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string The IUPAC Code.
name string Full name of the nucleotide.
abbreviation string Three-letter abbreviation of the nucleotide.

Example (Click on the arrow to expand)

The following example shows how to use this API.

Fetch all Nucleotides

HTTP URL: https://api.c3.ai/covid/api/1/nucleotidelookup/fetch

Request JSON:

{
  "spec": {

  }
}



Response JSON:

{
  "objs": [
    {
        "id": "A",
        "name": "adenine",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-03-31T16:05:46Z",
            "createdBy": "provisioner",
            "updated": "2020-03-31T16:05:46Z",
            "updatedBy": "provisioner",
            "timestamp": "2020-03-31T16:05:46Z",
            "fetchInclude": "[]",
            "fetchType": "NucleotideLookup"
        },
        "version": 1
    },
...
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/nucleotidelookup/fetch
https://api.c3.ai/covid/api/1/nucleotidelookup/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

BiblioEntry

BiblioEntry stores the metadata about the journal articles in the CORD-19 Dataset.

The fetch API provides tabular journal article data, while the getarticlemetadata API provides full-text articles in JSON.

NOTE: Journal articles from CORD-19 are available through April 8, 2020. Additional journal articles will be made available soon.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string Journal article ID.
sha string ID that links the BiblioEntry to a JSON file containing the journal article's full text.
source string The database from which the journal article is sourced. Allowed values: Elsevier, biorxiv, CZI, medrxiv, PMC, WHO.
title string Title of the journal article.
doi datetime Journal article's BioRxiv/MedRxiv ID.
pmcid string Journal article's PMC ID.
pubmedId string Journal article's PubMed ID.
license string Journal article's license. Allowed values: biorxiv, els-covid, medrvix, cc-by, cc-by-nc, cc-by-nc-nd, cc-by-nc-sa, cc-by-nd, cc-by-sa, cc0, pd.
abstractText string Journal article's abstract text.
publishTime datetime Date the journal article was published.
authors string List of journal article's authors.
journal string Journal which published the article.
url string The URL of the article.
idMsftPaper string Journal article's Microsoft Academic Paper ID.
whoCovidence string Journal article's WHO ID.
hasFullText boolean Is the JournalArticle's full text available in the dataset? Allowed values: true, false.
fullTextFile string The type of the journal article. Allowed values: custom_license, comm_use_subset, biorxiv_medrxiv, noncomm_use_subset.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Fetch metadata for the first two thousand (2000) BiblioEntry journal articles (request only)

HTTP URL: https://api.c3.ai/covid/api/1/biblioentry/fetch

Request JSON:

{ // By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
  }
}
Fetch metadata for the first two thousand (2000) full text BiblioEntry journal articles

HTTP URL: https://api.c3.ai/covid/api/1/biblioentry/fetch

Request JSON:

{ // By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
    "filter": "hasFullText == true"
  }
}
Fetch metadata for the first two thousand (2000) BiblioEntry journal articles approved for commercial use

HTTP URL: https://api.c3.ai/covid/api/1/biblioentry/fetch

Request JSON:

{ // By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
    "filter": "fullTextFile == 'comm_use_subset'"
  }
}



Response JSON:

{
  "objs": [
    {
        "sha": "3442b139e80c8351c89a9398709090db63edb8fe",
        "source": "PMC",
        "title": "Seroprevalence of Rodent Pathogens in Wild Rats from the Island of St. Kitts, West Indies",
        "doi": "10.3390/ani9050228",
        "pmcid": "PMC6562389",
        "pubmedId": "31083284.0",
        "license": "cc-by",
        "abstractText": "SIMPLE SUMMARY: The role of rodents in the transmission of many diseases is widely known. Wild rats abundant in urban environments may transmit diseases to humans and other animals, including laboratory rodents used for biomedical research in research facilities, possibly compromising research data. In order to gather information about the various diseases present around such facilities, it is important to conduct routine surveillance of wild rodents in the area. In this pilot study, we surveyed 22 captured wild rats (Rattus norvegicus and Rattus rattus) from the Caribbean island of St. Kitts for 19 microorganisms. Information gained from such surveillance data would be beneficial in assessing regional public health risks and when implementing routine laboratory rodent health monitoring protocols. ABSTRACT: A pilot seroprevalence study was conducted to document exposure to selected pathogens in wild rats inhabiting the Caribbean island of St. Kitts. Serum samples collected from 22 captured wild rats (Rattus norvegicus and Rattus rattus) were tested for the presence of antibodies to various rodent pathogens using a rat MFI2 serology panel. The samples were positive for cilia-associated respiratory bacillus (13/22; 59.1%), Clostridium piliforme (4/22; 18.2%), Mycoplasma pulmonis (4/22; 18.2%), Pneumocystis carinii (1/22; 4.5%), mouse adenovirus type 2 (16/22; 72.7%), Kilham rat virus (15/22; 68.2%), reovirus type 3 (9/22; 40.9%), rat parvovirus (4/22; 18.2%), rat minute virus (4/22; 18.2%), rat theilovirus (2/22; 9.1%), and infectious diarrhea of infant rats strain of group B rotavirus (rat rotavirus) (1/22; 4.5%). This study provides the first evidence of exposure to various rodent pathogens in wild rats on the island of St. Kitts. Periodic pathogen surveillance in the wild rat population would be beneficial in assessing potential regional zoonotic risks as well as in enhancing the current knowledge when implementing routine animal health monitoring protocols in facilities with laboratory rodent colonies.",
        "publishTime": "2019-05-10T00:00:00",
        "authors": "Boey, Kenneth; Shiokawa, Kanae; Avsaroglu, Harutyun; Rajeev, Sreekumari",
        "journal": "Animals (Basel)",
        "hasFullText": true,
        "fullTextFile": "comm_use_subset",
        "url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562389/",
        "id": "000q5l5n",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T08:10:47Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T08:10:47Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T08:10:47Z",
            "fetchInclude": "[]",
            "fetchType": "BiblioEntry"
        },
        "version": 1
    },
  ...
}
Fetch metadata for the first two thousand (2000) full text PMC journal articles with full text cc-by license (request only)

HTTP URL: https://api.c3.ai/covid/api/1/biblioentry/fetch

Request JSON:

{ // By default the first 2000 records are returned if no "limit" is specified.
  "spec": {
    "filter": "source == 'PMC' && license == 'cc-by' && hasFullText == true"
  }
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/biblioentry/fetch
https://api.c3.ai/covid/api/1/biblioentry/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

GetArticleMetadata

This API returns the full-text, in JSON, of journal articles from the CORD-19 dataset.

Examples (Click on the arrow to expand)

The following examples show how to use this API.

Get the full-text, in JSON, of a journal article from the CORD-19 dataset

HTTP URL: https://api.c3.ai/covid/api/1/biblioentry/getarticlemetadata

Request JSON:

{
  "ids": ["02lsd9p6"] // list of "id" fields in BiblioEntry C3.ai Type.
}



Response JSON:

{
  "value": {
    "value": [
      {
        "paper_id": "638a64df0b44b94562271c191fa0e45933459000",
        "metadata": {
          "title": "Eff ects of smoking and solid-fuel use on COPD, lung cancer, and tuberculosis in China: a time-based, multiple risk factor, modelling study",
          "authors": [
            {
              "first": "Hsien-Ho",
              "middle": [],
              "last": "Lin",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Megan",
              "middle": [],
              "last": "Murray",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Ted",
              "middle": [],
              "last": "Cohen",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Caroline",
              "middle": [],
              "last": "Colijn",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Majid",
              "middle": [],
              "last": "Ezzati",
              "suffix": "",
              "affiliation": {},
              "email": ""
            }
          ]
        },
        "abstract": [
          {
            "text": "Background Chronic obstructive pulmonary disease (COPD), lung cancer, and tuberculosis are three leading causes of death in China, where prevalences of smoking and solid-fuel use are also high. We aimed to predict the eff ects of risk-factor trends on COPD, lung cancer, and tuberculosis.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Abstract"
          },
          {
            "text": "We used representative data sources to estimate past trends in smoking and household solid-fuel use and to construct a range of future scenarios. We obtained the aetiological eff ects of risk factors on diseases from meta-analyses of epidemiological studies and from large studies in China. We modelled future COPD and lung cancer mortality and tuberculosis incidence, taking into account the accumulation of hazardous eff ects of risk factors on COPD and lung cancer over time, and dependency of the risk of tuberculosis infection on the prevalence of disease. We quantifi ed the sensitivity of our results to methods and data choices.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Abstract"
          },
          {
            "text": "Complete cessation of smoking and solid-fuel use by 2033 would reduce the projected annual tuberculosis incidence in 2033 by 14-52% if 80% DOTS coverage is sustained, 27-62% if 50% coverage is sustained, or 33-71% if 20% coverage is sustained.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Abstract"
          },
          {
            "text": "Interpretation Reducing smoking and solid-fuel use can substantially lower predictions of COPD and lung cancer burden and would contribute to eff ective tuberculosis control in China.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Abstract"
          }
        ],
        "body_text": [
          {
            "text": "Chronic obstructive pulmonary disease (COPD), lung cancer, and tuberculosis are the second, sixth, and eighth leading causes of death in China, accounting for almost 2 million deaths in 2002 (20·5% of all deaths in China). 1 A half of Chinese men smoke and more than 70% of Chinese households use solid fuels, such as wood, crop residues, and coal for heating and cooking. 2 Tobacco smoking and indoor air pollution from solid-fuel use are the most important global risk factors for COPD and lung cancer and account for a signifi cant proportion of deaths from these diseases in developing countries. 3, 4 Without interventions, the annual numbers of COPD and lung cancer deaths in China are predicted to double over the next 30 years. 1 Systematic reviews have concluded that smoking is also an independent risk factor for tuberculosis [5] [6] [7] and suggested a positive association between indoor air pollution and the disease. 5 Integrated programmes that incorporate multiple risk factor and therapeutic interventions are a potentially eff ective way to target the cluster of respiratory diseases that share common risks. 8 Planning integrated programmes for respiratory diseases and risk factors requires quantitative estimates of how future COPD, lung cancer, and tuberculosis will be aff ected by trends in smoking and indoor air pollution. Estimating the eff ects of smoking and indoor air pollution on COPD and lung cancer requires incorporation of how fast risk accumulates after initiation, or reverses after cessation. Smoking and indoor air pollution also infl uence the dynamics of tuberculosis, both via direct eff ect in exposed individuals, and indirect eff ect in unexposed individuals through infectious source cases.",
            "cite_spans": [
              {
                "start": 601,
                "end": 603,
                "text": "3,",
                "ref_id": null
              },
              {
                "start": 604,
                "end": 605,
                "text": "4",
                "ref_id": null
              },
              {
                "start": 837,
                "end": 840,
                "text": "[5]",
                "ref_id": "BIBREF1"
              },
              {
                "start": 841,
                "end": 844,
                "text": "[6]",
                "ref_id": "BIBREF2"
              },
              {
                "start": 845,
                "end": 848,
                "text": "[7]",
                "ref_id": "BIBREF3"
              },
              {
                "start": 932,
                "end": 933,
                "text": "5",
                "ref_id": "BIBREF1"
              },
              {
                "start": 1128,
                "end": 1129,
                "text": "8",
                "ref_id": "BIBREF4"
              }
            ],
            "ref_spans": [],
            "section": "Introduction"
          },
          {
            "text": "We provide a systematic assessment of the future trends of these three leading communicable and non-communicable respiratory diseases in China that share smoking and indoor air pollution as risk factors. These results quantify the potential benefi ts of programmes that target one or more of these risk factors or disease outcomes.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Introduction"
          },
          {
            "text": "To estimate the proportional eff ects of future smoking and solid-fuel use on projected lung cancer and COPD mortality, we used a generalised version of the population attributable fraction (PAF) relationship: P t,i =proportion of population in the i th exposure category at time t in one future exposure scenario. We used the scenario of constant exposure as the baseline to which all other scenarios were compared. P´t ,i =proportion of population in the i th exposure category at time t in an alternative future scenario. RR t,i =relative risk of disease-specifi c mortality for the i th exposure category at time t. n=number of exposure categories. The exposure categories include continuously exposed, non-exposed, as well as those whose exposure began or stopped at diff erent times during the analysis.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Analytical models"
          },
          {
            "text": "PAF estimates the proportional reduction in disease or death that would occur if the observed exposure to a risk factor (P) were modifi ed to an alternative exposure scenario (P´). 9 The generalised version of the PAF relationship allows both exposure and relative risk to vary over time. Relative risk at time (t) depends on time since exposure initiation or cessation, because the eff ects of smoking and indoor air pollution accumulate gradually after exposure begins (eg, smoking initiation) and reverse gradually after exposure stops (eg, smoking cessation). 10, 11 We did separate analyses for lung cancer and COPD and for men and women, because they have diff erent relative risks. For both lung cancer and COPD, we fi rst computed PAFs separately for smoking and solid-fuel use. COPD and lung cancer are caused by multiple risk factors acting simultaneously, and hence a proportion of deaths might be prevented by reducing exposure to either factor. 9, 12, 13 For example, some COPD deaths among smokers who cook with solid fuels may be prevented by smoking prevention or by use of clean fuels. As a result of multicausality, the PAFs for multiple risk factors overlap, and cannot be combined by simple addition. We estimated the PAFs for the combined eff ects of smoking and solid fuel, accounting for multicausality and avoiding double-counting the overlap of multiple risk factors: 9, [12] [13] [14] PAF i =PAF of the i th risk factor n=number of risk factors Three conditions must hold when using the above equation. First, exposures to risks should be uncorrelated. The cross-province correlation coeffi cient between solidfuel use and smoking, from the data sources described in webappendix 1, was 0·04 (95% CI -0·32 to 0·40, p=0·82) for men and -0·11 (-0·45 to 0·26, p=0·56) for women, indicating small and non-signifi cant correlation. We also examined individual-level correlation with the 2006 panel from The China Health and Nutrition Survey (a multistage, random cluster survey, including 9788 adults in nine provinces throughout China with signifi cant variation in socioeconomic and health status). 15 61% of men and 4% of women had ever smoked in homes that did not use solid fuels and 63% of men and 4% of women had ever smoked in homes that did. The odds ratios for smoking, comparing solid-fuel users to non-users, was 1·07 (0·95-1·20) for men and 1·02 (0·76-1·36) for women, showing uncorrelated exposures. The second condition is that the hazardous eff ects of one risk are not mediated through other risks. Smoking and solid-fuel combustion are both sources of respirable pollutants, but the eff ects of pollutants from one source are not mediated through exposure to the other. The third condition is that the proportional eff ects of one risk do not depend on exposure to the other risk factor. The proportional mortality study of smoking in China showed that the relative risks of lung cancer and respiratory-disease mortality among Chinese smokers were not modifi ed by background disease levels, 16",
            "cite_spans": [
              {
                "start": 181,
                "end": 182,
                "text": "9",
                "ref_id": "BIBREF5"
              },
              {
                "start": 564,
                "end": 567,
                "text": "10,",
                "ref_id": "BIBREF6"
              },
              {
                "start": 568,
                "end": 570,
                "text": "11",
                "ref_id": "BIBREF7"
              },
              {
                "start": 958,
                "end": 960,
                "text": "9,",
                "ref_id": "BIBREF5"
              },
              {
                "start": 961,
                "end": 964,
                "text": "12,",
                "ref_id": "BIBREF8"
              },
              {
                "start": 965,
                "end": 967,
                "text": "13",
                "ref_id": "BIBREF9"
              },
              {
                "start": 1393,
                "end": 1395,
                "text": "9,",
                "ref_id": "BIBREF5"
              },
              {
                "start": 1396,
                "end": 1400,
                "text": "[12]",
                "ref_id": "BIBREF8"
              },
              {
                "start": 1401,
                "end": 1405,
                "text": "[13]",
                "ref_id": "BIBREF9"
              },
              {
                "start": 1406,
                "end": 1410,
                "text": "[14]",
                "ref_id": "BIBREF10"
              }
            ],
            "ref_spans": [],
            "section": "Analytical models"
          },
          {
            "text": "When susceptible (S) individuals are infected, they enter a state of fast latency (L) from which they may experience primary progression to the infectious (I) state. If not progressed within 5 years of infection, patients enter slow latency, where they may progress to the infectious state via endogenous reactivation at a greatly reduced rate. Individuals in the infectious state can be treated and enter the recovered (R) state from which they remain at risk of relapse to active disease. Individuals in the slowly progressive latent state or the recovered state are at risk of reinfection, although prior infection confers partial immunity. Individuals in any state can die, and new individuals enter the system via the susceptible compartment. ",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Figure 1: Compartmental susceptible-latent-infectious-recovered (SLIR) model of tuberculosis infection"
          },
          {
            "text": "See Online for webappendix 1 which is consistent with no eff ect modifi cation of relative risks between these two risks.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Figure 1: Compartmental susceptible-latent-infectious-recovered (SLIR) model of tuberculosis infection"
          },
          {
            "text": "We calculated the annual number of lung cancer and COPD deaths avoidable by reducing exposure to smoking and solid fuels, individually as well as combined, by multiplying the corresponding PAF by the projected total disease-specifi c deaths for the year of analysis.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Figure 1: Compartmental susceptible-latent-infectious-recovered (SLIR) model of tuberculosis infection"
          },
          {
            "text": "The incidence of tuberculosis depends on risk-factor exposure and population prevalence of infectious cases. We used a dynamic tuberculosis transmission model, specifi cally the deterministic compartmental susceptiblelatent-infectious-recovered model (fi gure 1), described in detail elsewhere. 17, 18 The susceptible-latent-infectious-recovered model parameters were based on previous epidemiological studies on the natural history of tuberculosis and risk factors, China-specifi c demographic parameters, and calibration to epidemiology in China (webtable 1). We introduced smoking and indoor air pollution into the model by stratifying the model population into the four possible combinations of exposure to these risk factors, proportional to their actual (time-varying) prevalence in each province. We fi tted a time-varying transmission parameter, defi ned as the rate at which infectious individuals are able to infect people who are susceptible. Available data and previous modelling suggested that the transmission parameter declined almost linearly in England and Wales from about 20 in 1900 to about 1 at the end of the 20th century. 19 We set the value of the transmission parameter to 20 in 1900, and it declined linearly to an empirically-fi tted lower bound; the rate of decline and the lower bound were fi tted to minimise the root mean square of the diff erences between the modelled and observed tuberculosis prevalence by province. The fi tted lower bound, which determines tuberculosis incidence after 2000, was not sensitive to the initial transmission parameter in 1900. Model fi tting also considered that chemotherapy was unavailable before 1960 20 and that the mean duration of disease is 2 years without treatment, 0·8 years under eff ective DOTS (directly observed treatment, short course), and 1·5 years for programmes other than eff ective DOTS. 21 We applied the calibrated model to estimate future tuberculosis incidence under diff erent scenarios of smoking, solid-fuel use, and DOTS coverage.",
            "cite_spans": [
              {
                "start": 295,
                "end": 298,
                "text": "17,",
                "ref_id": "BIBREF12"
              },
              {
                "start": 299,
                "end": 301,
                "text": "18",
                "ref_id": "BIBREF13"
              },
              {
                "start": 1145,
                "end": 1147,
                "text": "19",
                "ref_id": "BIBREF14"
              },
              {
                "start": 1875,
                "end": 1877,
                "text": "21",
                "ref_id": "BIBREF16"
              }
            ],
            "ref_spans": [],
            "section": "Figure 1: Compartmental susceptible-latent-infectious-recovered (SLIR) model of tuberculosis infection"
          },
          {
            "text": "To account for heterogeneity across provinces, we did tuberculsosis analyses at the provincial level. We present results for provinces that span a range of characteristics: Jiangsu, a province with moderate historical prevalence of tuberculosis and representative historical trend in prevalence compared to other provinces and Guizhou and Shanghai which have prevalences and trends that diff er from other provinces (webfi gure 1).",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Figure 1: Compartmental susceptible-latent-infectious-recovered (SLIR) model of tuberculosis infection"
          },
          {
            "text": "We detail the methods and sources for estimating past trends in smoking, 22 Smoking prevalence in Chinese men has recently declined; 33 many high-income and some middleincome countries (eg, South Africa, Poland, Thailand) have reduced tobacco smoking from its peak through a combination of taxation, regulation (eg, public smoking or advertising ban), and health education; 34-37 the fi nal smoking prevalence of 30% is selected to be slightly higher than those in high-income countries, such as the UK and the USA, where more eff ective tobacco control policies and programmes have been implemented 38 Aggressive control",
            "cite_spans": [
              {
                "start": 73,
                "end": 75,
                "text": "22",
                "ref_id": "BIBREF17"
              },
              {
                "start": 600,
                "end": 602,
                "text": "38",
                "ref_id": null
              }
            ],
            "ref_spans": [],
            "section": "Risk-factor exposure and DOTS data sources and scenarios"
          },
          {
            "text": "Male smoking prevalence declines more rapidly between 2003 and 2033, reaching 15% in each province in 2033",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Risk-factor exposure and DOTS data sources and scenarios"
          },
          {
            "text": "Nationally, Singapore, Australia, and Canada have successfully reduced smoking prevalence to below 20% through eff ective tobacco control policies and interventions; 38-40 below 20% prevalence was also achieved through the Massachusetts Tobacco Control Program; 41 a prevalence of 15% is therefore feasible through eff ective tobacco control mechanisms",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Risk-factor exposure and DOTS data sources and scenarios"
          },
          {
            "text": "To zero Male smoking prevalence declines to zero in 2033",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Risk-factor exposure and DOTS data sources and scenarios"
          },
          {
            "text": "This is an ideal scenario, included to provide a theoretical upper-bound on the benefi ts of gradual smoking reduction over the projection period",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Risk-factor exposure and DOTS data sources and scenarios"
          },
          {
            "text": "Large rise Female smoking prevalence rises to reach 30% in each province in 2033",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Women"
          },
          {
            "text": "The worst-case scenario for female smoking, in which the female smoking epidemic succeeds that of males, as seen in high-income, central European, and Latin American countries, 37, 38 where female smoking prevalence typically peaked at levels lower that of men, 37,38 hence set to 30%",
            "cite_spans": [
              {
                "start": 177,
                "end": 180,
                "text": "37,",
                "ref_id": "BIBREF31"
              },
              {
                "start": 181,
                "end": 183,
                "text": "38",
                "ref_id": null
              }
            ],
            "ref_spans": [],
            "section": "Women"
          },
          {
            "text": "Female smoking prevalence rises to reach 15% in each province in 2033",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Moderate rise"
          },
          {
            "text": "Similar to large rise in female smoking with a lower peak, as seen for example in Japan; 38 This is an ideal scenario, included to provide a theoretical upper-bound on the benefi ts of gradual smoking reduction over the projection period See webfi gure 2 for prevalence over time. We used China Data Online 31 for data on provincial populations. The future projections of the national population came from the Population Division of the Department of Economic and Social Aff airs of the UN Secretariat 32 and those of the disease-specifi c mortality from the global burden of disease study conducted by the WHO. Global burden of disease forecasts disease-specifi c mortality on the basis of projections of economic and social development, smoking, obesity, and selected other disease determinants. 1 We constructed scenarios that represent the range of policies and programmes that can reduce smoking and solid-fuel use on the basis of experiences and trends in other countries and some parts of China (tables 1 and 2 and webfi gure 2). All scenarios covered a period of 30 years (2003-33). We constructed prevalence of risk factors over time in the scenarios for each province with a linear time trend, and aggregated the provincial prevalences to the national level. More than 99% of the tobacco market share in China is controlled by the Chinese National Tobacco Corporation. Tobacco prices and taxes are low and there are only a few tobacco control measures in place. 38 However, China ratifi ed the Framework Convention on Tobacco Control in October 2005, which came into eff ect in January, 2006. Future smoking prevalence will largely depend on the extent to which economic and regulatory approaches to tobacco control are pursued. Although male and female smoking prevalences probably have common policy determinants, the analyses of lung cancer and chronic disease mortality were done separately for men and women and included all sex-specifi c scenarios. The analysis for tuberculosis was done for men and women combined because relative risks were for both sexes combined and because there are indirect eff ects of smoking on tuberculosis between sexes. National adult smoking prevalence was 49·6% for men and 3·0% for women in 2003 according to the National Health Service Survey. Another National Tobacco Prevalence Survey in 2002 reported current smoking prevalence of 57·4% for men and 2·6% for women. The two surveys had slightly diff erent defi nitions of current smoking. 22, 33 Tobacco consumption per person in China peaked around 1990 and has declined slowly since then. 38 Data from three National Health Service Surveys (1993, 1998, 2003) show that smoking prevalence declined from 60·3% to 49·6% among men (standardised to the 2003 population); smoking in women declined from 4·7% to 3·0% in this period.",
            "cite_spans": [
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                "start": 89,
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                "text": "38",
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                "start": 2643,
                "end": 2649,
                "text": "(1993,",
                "ref_id": null
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                "start": 2650,
                "end": 2655,
                "text": "1998,",
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                "start": 2656,
                "end": 2661,
                "text": "2003)",
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            ],
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            "section": "Moderate rise"
          },
          {
            "text": "Solid fuels comprise biomass (wood, crop residues, animal dung, and charcoal) and coal and are used for cooking and heating. Only coal smoke raises the risk of lung cancer in epidemiological studies although wood smoke contains small amounts of carcinogens. 47 Non-solid fuels, which have substantially lower emissions of health-damaging pollutants, include liquid and gaseous fuels and electricity. 47 Solid-fuel scenarios only encompass fuel changes and did not take into account alternative stoves. China has implemented an ambitious programme to disseminate improved cooking stoves, 54, 55 primarily to improve effi ciency and reduce fuel use. The",
            "cite_spans": [
              {
                "start": 258,
                "end": 260,
                "text": "47",
                "ref_id": "BIBREF41"
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                "start": 400,
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                "text": "47",
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                "start": 587,
                "end": 590,
                "text": "54,",
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                "start": 591,
                "end": 593,
                "text": "55",
                "ref_id": "BIBREF49"
              }
            ],
            "ref_spans": [],
            "section": "Moderate rise"
          },
          {
            "text": "Reason for scenario use",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Defi nition"
          },
          {
            "text": "Unchanged Proportion of households using solid fuels remains at its 2003 level in every province",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Total solid fuels"
          },
          {
            "text": "Evidence indicates that few households revert back to solid fuels once they have transitioned to cleaner fuels, with the possible exception of the poorest countries (eg, some in sub-Saharan Africa) during international or national energy or economic crises; 42 therefore, stabilisation at current prevalence is the likely upper bound for household solid fuel use",
            "cite_spans": [
              {
                "start": 258,
                "end": 260,
                "text": "42",
                "ref_id": "BIBREF36"
              }
            ],
            "ref_spans": [],
            "section": "Total solid fuels"
          },
          {
            "text": "Half current Percent of households using solid fuels declines to one half of its 2003 level in each province in 2033",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Total solid fuels"
          },
          {
            "text": "In China, rapid economic growth has contributed to near-universal access to electricity for lighting and for services such as television, but solid-fuel use for cooking and heating has persisted, with 72% of Chinese households continuing to use solid fuels according the 2000 National Census; [43] [44] [45] [46] this scenario is based on the experience of middle-income countries (eg, those in Latin America) that have reduced household solid-fuel use through active policy interventions [47] [48] [49] Urban-rural Percent of households using solid fuels declines to zero in urban populations and to one half of its 2003 level in rural populations in each province in 2033",
            "cite_spans": [
              {
                "start": 293,
                "end": 297,
                "text": "[43]",
                "ref_id": "BIBREF37"
              },
              {
                "start": 298,
                "end": 302,
                "text": "[44]",
                "ref_id": null
              },
              {
                "start": 303,
                "end": 307,
                "text": "[45]",
                "ref_id": "BIBREF39"
              },
              {
                "start": 308,
                "end": 312,
                "text": "[46]",
                "ref_id": "BIBREF40"
              },
              {
                "start": 489,
                "end": 493,
                "text": "[47]",
                "ref_id": "BIBREF41"
              },
              {
                "start": 494,
                "end": 498,
                "text": "[48]",
                "ref_id": "BIBREF42"
              },
              {
                "start": 499,
                "end": 503,
                "text": "[49]",
                "ref_id": "BIBREF43"
              }
            ],
            "ref_spans": [],
            "section": "Total solid fuels"
          },
          {
            "text": "This scenario modifi es the previous scenario of \"decline to half the current level\" to acknowledge that there are better-developed supply chains in urban populations, even at the same income level; 8, 42, 50 in China, increased attention to urban air quality may also motivate faster and more extensive transitions in urban areas as has been done in other cities [51] [52] [53] To zero Percent of households using solid fuels declines to zero in 2033",
            "cite_spans": [
              {
                "start": 199,
                "end": 201,
                "text": "8,",
                "ref_id": "BIBREF4"
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              {
                "start": 202,
                "end": 205,
                "text": "42,",
                "ref_id": "BIBREF36"
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              {
                "start": 206,
                "end": 208,
                "text": "50",
                "ref_id": "BIBREF44"
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              {
                "start": 364,
                "end": 368,
                "text": "[51]",
                "ref_id": "BIBREF45"
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                "start": 369,
                "end": 373,
                "text": "[52]",
                "ref_id": "BIBREF46"
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              {
                "start": 374,
                "end": 378,
                "text": "[53]",
                "ref_id": "BIBREF47"
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            ],
            "ref_spans": [],
            "section": "Total solid fuels"
          },
          {
            "text": "This is an ideal scenario which provides an upper-bound on the benefi ts of clean fuels over the projection period",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Total solid fuels"
          },
          {
            "text": "All biomass converted to coal",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Share of solid fuels from coal"
          },
          {
            "text": "The coal share of solid fuel increases to 100% in each province by 2033",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Share of solid fuels from coal"
          },
          {
            "text": "Although until the 1980s and 1990s biomass was the dominant source of household energy in China, deforestation and policies to reduce and reverse it have compelled many rural residents to switch to from biomass to coal 43, 54 Unchanged coal share of solid fuels",
            "cite_spans": [
              {
                "start": 219,
                "end": 222,
                "text": "43,",
                "ref_id": "BIBREF37"
              },
              {
                "start": 223,
                "end": 225,
                "text": "54",
                "ref_id": "BIBREF48"
              }
            ],
            "ref_spans": [],
            "section": "Share of solid fuels from coal"
          },
          {
            "text": "The coal share of solid fuel remains at its 2003 level Given the past trends in biomass to coal conversion, and its policy drivers, this is the lowest bound for coal share of solid fuels See webfi gure 2 for fuel-use over time. 54 If alternative stove designs reduce exposure signifi cantly, they can be treated as equivalent to fuel change; partial exposure reduction can be modelled with eff ect sizes between those for solid fuels and clean fuels.",
            "cite_spans": [
              {
                "start": 228,
                "end": 230,
                "text": "54",
                "ref_id": "BIBREF48"
              }
            ],
            "ref_spans": [],
            "section": "Share of solid fuels from coal"
          },
          {
            "text": "The same solid-fuel scenarios were used for men and women but the relative risk is higher for women (table 3) 32 The national change in relative proportion of urban and rural population over time was applied to all provinces. The proportion of diff erent categories of solid-fuel use in future scenarios were calculated such that transitions from coal and biomass to clean fuels in any year were proportional to their prevalence in the year of transition.",
            "cite_spans": [
              {
                "start": 110,
                "end": 112,
                "text": "32",
                "ref_id": "BIBREF26"
              }
            ],
            "ref_spans": [
              {
                "start": 100,
                "end": 109,
                "text": "(table 3)",
                "ref_id": "TABREF4"
              }
            ],
            "section": "Share of solid fuels from coal"
          },
          {
            "text": "China made signifi cant progress in DOTS expansion between 1990 and 2000 in 13 provinces, municipalities, and autonomous regions. After the outbreak of severe acute respiratory syndrome, China further expanded DOTS coverage so that nationally an estimated 80% of smear-positive cases of tuberculosis were detected and treated in 2005. 56 We used time-varying treatment coverage in each province and municipality based on province-specifi c implementation before 2002 and a range of possible scenarios from 2003 (webtable 2).",
            "cite_spans": [
              {
                "start": 335,
                "end": 337,
                "text": "56",
                "ref_id": "BIBREF50"
              }
            ],
            "ref_spans": [],
            "section": "Share of solid fuels from coal"
          },
          {
            "text": "We used relative risks for the eff ects of smoking on lung cancer and COPD from the retrospective proportional mortality study of smoking in China 16 (table 3) , and from meta-analyses of Chinese cohort studies in a sensitivity analysis (webappendix 2). The relations between smoking or indoor air pollution from solid-fuel use and chronic diseases have a dose-response relationship; therefore risks might diff er for men and women.",
            "cite_spans": [
              {
                "start": 147,
                "end": 149,
                "text": "16",
                "ref_id": "BIBREF11"
              }
            ],
            "ref_spans": [
              {
                "start": 150,
                "end": 159,
                "text": "(table 3)",
                "ref_id": "TABREF4"
              }
            ],
            "section": "Data sources for risk-factor eff ect sizes"
          },
          {
            "text": "Because the deaths in the proportional mortality study occurred in 1986-88, around the peak of the smoking epidemic in China, 16, 38 relative risks for COPD and lung cancer were substantially lower than in populations in Europe and the USA, which have smoked longer. Low relative risks have also been observed in other Asian populations, which likewise began smoking late. 57 Studies in European and American cohorts at diff erent stages of the tobacco epidemic have shown that relative risks for chronic diseases rise for decades after the peak of the smoking epidemic. 10, 58 To account for the accumulation of risk, we allowed COPD and lung cancer relative risks from the proportional mortality study to increase to those from the American Cancer Society Cancer Prevention Study, phase II (CPS-II), which was done primarily in lifelong smokers. 58 In sensitivity analyses, we allowed relative risks to remain at their 1986-88 levels over the complete analysis period, representing the possibility that smoking may be associated with smaller relative risks in Asian populations than among others.",
            "cite_spans": [
              {
                "start": 126,
                "end": 129,
                "text": "16,",
                "ref_id": "BIBREF11"
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                "start": 130,
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                "text": "38",
                "ref_id": null
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                "start": 373,
                "end": 375,
                "text": "57",
                "ref_id": "BIBREF51"
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                "start": 571,
                "end": 574,
                "text": "10,",
                "ref_id": "BIBREF6"
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                "start": 575,
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                "text": "58",
                "ref_id": "BIBREF52"
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                "ref_id": "BIBREF52"
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            ],
            "ref_spans": [],
            "section": "Data sources for risk-factor eff ect sizes"
          },
          {
            "text": "We used relative risks for the eff ects of indoor air pollution from solid-fuel use on COPD (all solid fuels) and lung cancer (coal only) from a recent systematic review and meta-analysis of cohort, case-control, and cross-sectional studies. 47 Because the Chinese population and those in the epidemiological studies have used solid fuels for a long time, relative risks are expected to be at their peak. The relative risks for the eff ects of smoking and solid-fuel use on tuberculosis were from a recent meta-analysis. 5 The relative risks of smoking and indoor air pollution for COPD and lung cancer decline gradually after exposure stops. We used the change after exposure cessation estimated with data from CPS-II (webfi gure 3). The CPS-II fi ndings are consistent with studies on how indoor air pollution interventions reduce the COPD and lung cancer relative risks over time 59, 60 but provide data in fi ner time intervals. We used a symmetric pattern for the increase in relative risk after exposure begins.",
            "cite_spans": [
              {
                "start": 242,
                "end": 244,
                "text": "47",
                "ref_id": "BIBREF41"
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                "start": 521,
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                "text": "5",
                "ref_id": "BIBREF1"
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                "text": "59,",
                "ref_id": "BIBREF53"
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                "ref_id": "BIBREF54"
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            ],
            "ref_spans": [],
            "section": "Data sources for risk-factor eff ect sizes"
          },
          {
            "text": "Smoking might increase the risk of latent tuberculosis infection or the risk of progression to active disease from latent infection. For smoking and latent infection, we used the pooled estimate from a recent meta-analysis, 5 which had relative risks for disease similar to other published meta-analyses. 6, 7 The relative risk for progression to active tuberculosis was obtained by dividing that for smoking and active tuberculosis from the only cohort study (2·87) 61 relative risk in the only study of smoking and active disease among people who were latently infected. 62 We separated the relative risk for the eff ects of solid-fuel use on active tuberculosis into the eff ects on latent infection and progression to active diseases using the same proportions as for smoking.",
            "cite_spans": [
              {
                "start": 224,
                "end": 225,
                "text": "5",
                "ref_id": "BIBREF1"
              },
              {
                "start": 305,
                "end": 307,
                "text": "6,",
                "ref_id": "BIBREF2"
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              {
                "start": 308,
                "end": 309,
                "text": "7",
                "ref_id": "BIBREF3"
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              {
                "start": 467,
                "end": 469,
                "text": "61",
                "ref_id": "BIBREF55"
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                "start": 573,
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                "text": "62",
                "ref_id": "BIBREF56"
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            ],
            "ref_spans": [],
            "section": "Data sources for risk-factor eff ect sizes"
          },
          {
            "text": "The sponsor of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author had full access to all data in the study and had fi nal responsibility for the decision to submit for publication.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Role of the funding source"
          },
          {
            "text": "If tobacco smoking and solid-fuel use remain at their current levels, between 2003 and 2033 an estimated 65 million people will die of COPD and 18 million of lung cancer in China, accounting for 19% and 5%, respectively, of all deaths over this period. 1 82% of the COPD deaths (53 million) and 75% of lung cancer deaths (14 million) will be attributable to the combined eff ects of smoking and solid-fuel use (fi gure 2). 52% of COPD deaths and 82% of lung cancer deaths attributable to these risks will be among men. Of the 67 million COPD and lung cancer deaths attributable to smoking and solid-fuel use, over 10 million (8·1 million from COPD and 2·0 million from lung cancer) are unavoidable, even if all smokers had quit and all households had begun to use clean fuels in 2003, because the eff ect of the exposures on chronic diseases persist even after exposure ends. Men who stop smoking in 2003 are expected to lower their absolute risks of COPD by 56% and lung cancer by 60% after 5 years relative to those who continue smoking (the reductions for women are 63% for COPD and 75% for lung cancer; webfi gure 3). Risks will be lowered by 83-84% (COPD) and 83-86% (lung cancer) after 10 years and 91-92% (COPD) and 88-91% (lung cancer) after 20 years (webfi gure 3). At the population level, with moderate tobacco control, the sum of the annual avoided COPD and lung cancer deaths among men would be an estimated 4·6 and 1·9 million, respectively, 15% of all projected deaths of men from these diseases (webfi gure 4). If tobacco control is aggressive, with smoking in men declining to 15% by 2033, 8·0 million COPD deaths and 3·3 million lung cancer deaths would be averted.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Results"
          },
          {
            "text": "If female smoking gradually declines to zero by 2033, the sum of the annual avoided COPD and lung cancer deaths would be 4·9 and 0·76 million, averting 14% and 13% all projected female deaths from these diseases (webfi gure 4). Conversely, if female smoking rises to 30% by 2033, an additional 2·2 million COPD deaths and 0·60 million lung cancer deaths are expected among Chinese women. This asymmetry of benefi ts and harms occurs because the relative risks seem to rise slowly among new smokers but fall steeply in former smokers (webfi gure 3).",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Results"
          },
          {
            "text": "Halving solid-fuel use by 2033 would lower the sum of the annual number of COPD and lung cancer deaths, respectively, by 2·2 million and 0·30 million in men (7% and 2% of deaths from these causes) and by 4·3 million and 0·27 million in women (12% and 5% of deaths from these causes; webfi gure 5). Proportionally, more deaths are prevented among women because they are closer to the pollution source during cooking, have higher exposure to pollutants, and hence higher relative risks. The benefi ts would be twice as large with a complete transition to clean fuels.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Results"
          },
          {
            "text": "After accounting for the overlap between the eff ects of the two risk factors, if smoking and solid-fuel use are If there is sustained 80% coverage of eff ective DOTS (webtable 2), the annual incidence of infectious tuberculosis in the three provinces presented here is estimated to decline after 2003, even if smoking and solid-fuel use remain at their current levels (fi gure 4). Nonetheless, reducing smoking and solid fuels would further reduce incidence of tuberculosis from projected levels. The estimated reductions in 2033, under diff erent scenarios of smoking and solid-fuel use, range from 10% to 23% of the projected levels in Jiangsu, 35% to 52% in Guizhou (where prevalence has increased), and 5% to 14% in Shanghai (where prevalence has been lowest; fi gure 4).",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Results"
          },
          {
            "text": "Incidence of tuberculosis is projected to decline even when DOTS implementation is less eff ective. The decline in incidence in these three provinces will be 4-28% less under moderate DOTS than under optimum DOTS, and 11-79% smaller under minimum DOTS. This slower decline in tuberculosis incidence would, in turn, lead to smoking and solid-fuel interventions having larger relative and absolute benefi cial eff ects on trends in tuberculosis (fi gure 4). For example, in Jiangsu province, the relative reduction in projected incidence in 2033, under diff erent scenarios of smoking and ",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Results"
          },
          {
            "text": "Reductions in smoking and solid-fuel use can signifi cantly reduce the burden of COPD and lung cancer. Moderate, aggressive, and complete reduction of these exposures over the next three decades, through mechanisms such as tobacco taxation, advertising ban, and fuel pricing, can lead to 7-38% reduction in deaths from these two diseases, which have few other eff ective treatments.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "We observed a diff erence in how risk-factor interventions aff ect population-level trends in tuberculosis compared with those of COPD and lung cancer: in the absence of new technologies for early detection and treatment, the burden of COPD and lung cancer will remain high as long as smoking and indoor air pollution persist because these two risk factors are the most important causes of chronic respiratory disease incidence and mortality in China. But tuberculosis incidence can decline signifi cantly because treatment reduces the risk of infection through reducing the duration of infectiousness. Nonetheless, smoking and solid-fuel interventions should be components of tuberculosis control for at least three reasons. First, irrespective of coverage and eff ectiveness of DOTS, risk-factor interventions can further reduce incidence of tuberculosis. The complementary eff ects of risk-factor interventions occur through reducing the risk of latent infection and progression from latent infection to infectious disease. Second, reducing smoking in patients with tuberculosis might further strengthen DOTS eff ectiveness because smoking has been associated with delayed response to treatment. 63 Third, reducing smoking may itself lower the risk of drug resistance as recent studies have suggested a link between smoking and drug-resistant tuberculosis. 64, 65 Our quantitative analysis of the eff ects of the two leading environmental and lifestyle sources of respirable pollutants on a common set of infectious and chronic diseases used models that incorporated the aetiological risk factors in disease incidence and mortality over time. Specifi cally, our models and data sources accounted for the fact that eff ects on lung cancer and COPD accumulate or reverse gradually after exposure begins or stops and for dependency of tuberculosis incidence on prevalent infectious cases. We used several large sources of data to reconstruct consistent and comparable trends of smoking, solid-fuel use, and tuberculosis in China's provinces. Scenarios were constructed on the basis of policy experiences in China and other countries, and can provide programme targets and benchmarks. Current and projected mortality from COPD and lung cancer from the global burden of disease analysis used Chinese mortality data sources and accounted for demographic and socioeconomic development. 1 The relative risks and how they change over time were based on meta-analyses and large high-quality epidemiological studies in China and elsewhere.",
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                "start": 1199,
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            "section": "Discussion"
          },
          {
            "text": "Population-level analyses of mortality eff ects of risk factors, including ours, may also be aff ected by some limitations and uncertainties. We quantifi ed selected major sources of uncertainty in sensitivity analyses (webappendices 2 and 3, webfi gures 6 and 7, and webtables 3-5). These analyses show that the largest source of uncertainty for COPD and lung cancer is whether the relative risks of smoking related diseases in China will reach those observed in western populations. One recent study in China is consistent with increasing relative risks, 66 but the possibility of ethnic diff erences cannot be ruled out. 67 Benefi ts of tobacco control in diff erent scenarios would be 23-81% smaller for men and 84-94% smaller for women if the relative risks for eff ects of smoking on COPD and lung cancer do not rise signifi cantly after the peak of the smoking epidemic (webfi gure 6). The eff ects of all other sensitivity analyses on the number of averted deaths from COPD and lung cancer were less than 11% for the combined eff ects of smoking and solid-fuel use, and less than 18% for the eff ects of individual risk factors.",
            "cite_spans": [
              {
                "start": 557,
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                "ref_id": "BIBREF60"
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            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "The predictions for tuberculsosis are subject to uncertainties of model parameters, including how smoking and indoor air pollution aff ect the risk of diff erent stages of tuberculosis natural history (latent infection versus progression) and the values of the transmission and progression parameters (webappendix 3, webfi gure 8, and webtable 5). These analyses show that the largest source of uncertainty for the tuberculosis model is the future trend of transmission parameter, which depends on how sociodemographic change aff ects the patterns of contact and other determinants of transmission in China. If factors such as higher population density and increased travel increase the number of contacts that lead to tuberculosis transmission, the transmission parameter might not continue its expected decline. In this case, reducing smoking and indoor air pollution will have an even greater eff ect on tuberculosis control than estimated. For example, if the transmission parameter in Jiangsu doubles over the next 30 years, the benefi ts of the diff erent scenarios of smoking, solid-fuel use would be 58-77% greater than the original analysis in fi gure 4. Multiparameter uncertainty analysis showed the interquartile range of the estimated eff ects was within 14-33% of its central value in all provinces and in all scenarios of DOTS, smoking, and solid-fuel use. Finally, the causal link between smoking and tuberculosis is based on a larger number of epidemiological studies than that of indoor air pollution from solid-fuel use. If the causal eff ect on tuberculosis were only from smoking, the benefi ts of reducing exposure would be smaller than those in fi gure 4, by 19-37% for diff erent scenarios in Jiangsu, by 23-32% in Guizhou, and by 9-13% in Shanghai. In addition to the above sources of uncertainty, fundamentally new technologies for early detection and treatment of chronic diseases could signifi cantly aff ect future burden. Discovery and large-scale use of such technologies over the duration of our analysis are less likely for COPD, but cannot be ruled out for lung cancer and tuberculosis. Finally, relative risks from meta-analyses may not be generalisable to population-level eff ects; nevertheless, such estimation is necessary and indispensable to inform policy making.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "In addition to smoking and solid-fuel use, respiratory diseases are aff ected by other sources of inhaled pollutants, including passive smoking, air pollution from transport and industrial sources, and occupational exposures. In China, in 2003 passive smoking was responsible for an estimated 17 000 and 7000 deaths from COPD and lung cancer, respectively, among men (compared with 412 000 and 170 000 from direct smoking) and 126 000 and 25 000 deaths among women (compared with 113 000 and 16 000) because substantially more women are non-smokers and exposed to passive smoking 33 (webappendix 2). The quantitative evidence on the eff ects of passive smoking on tuberculosis is more limited, although a causal eff ect is likely. 5 Ambient urban air pollution, which in China might be partly due to household solid-fuel use, was responsible for an estimated 32 000 lung cancer deaths and 317 000 cardiopulmonary deaths in the Western Pacifi c region in 2000 (no independent eff ects on COPD were estimated for ambient urban air pollution) and selected occupational exposures for an estimated 34 000 lung cancer deaths and 161 000 COPD deaths in the same region. 4 The eff ect of ambient air pollution on tuberculosis has not been investigated in epidemiological studies.",
            "cite_spans": [
              {
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                "end": 732,
                "text": "5",
                "ref_id": "BIBREF1"
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            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "Infectious and chronic respiratory diseases, smoking, and indoor air pollution are either already concentrated or increasingly prevalent in developing countries. In addition to their disease burden, these diseases have large and inequitable household and societal economic burden associated with health-care costs, reduced labour-market participation, and decreased accumulation of human capital, described in detail elsewhere for China and for other developing countries. [68] [69] [70] [71] [72] Our results show that reducing common risk factors could have a substantial eff ect on future burden of COPD and lung cancer, and be an important contributor to tuberculosis control. Our fi ndings have several potential policy and programme implications at the national, community, household, or individual levels. At the national level, our results on the projected burden of disease from COPD and lung cancer alone reinforce the need for regulatory and economic policies that reduce smoking and promote clean household energy sources in China. Tuberculosis control however cannot rely on risk-factor reduction as its main intervention, and the core of national or regional tuberculosis control programmes should remain interventions that can reduce and eventually interrupt transmission under generalisable circumstances, currently DOTS. Nonetheless, at the community level, some interventions could increase the coverage or community eff ectiveness of others. For example, case detection and treatment completion under DOTS in China have been constrained by limitations of physical and human infrastructure, fi nancial factors, and compliance, especially in rural areas and marginalised social groups. [73] [74] [75] [76] Low-income or marginalised communities could be enrolled in programmes that provide cleaner fuels or stoves and nutritional supplements, conditional on periodic tuberculosis tests and DOTS treatment completion. At the individual level, tobacco cessation can be added to tuberculosis treatment, possibly with fi nancial incentives. 77, 78 If multiple interventions are implemented, the management structure of DOTS, including its standardised approach to registration, recording, and reporting of enrolled individuals (or households) and their status in receiving intervention, can strengthen planning and implementation of interventions for indoor air pollution and smoking. 79 Tobacco taxes might also be used to subsidise DOTS, clean energy technology, and nutrition for low-income households that take part in integrated programmes. An important next step would be a policy dialogue between the diff erent economic, energy, and health-sector agencies in China as well as community-based intervention studies that assess the eff ectiveness of combining risk factor interventions with tuberculosis case fi nding and treatment under actual conditions of implementation.",
            "cite_spans": [
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              {
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              {
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              {
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            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "HHL, MM, and ME designed the study and the analyses. HHL and ME collected data and wrote the report, with input from other authors. HHL conducted data analysis. ME contributed to analysis for chronic diseases. MM, TC, and CC contributed to analysis for TB. All authors reviewed and approved the fi nal report.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Contributors"
          },
          {
            "text": "We declare that we have no confl ict of interest.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Confl ict of interest"
          }
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        "ref_entries": {
          "FIGREF1": {
            "text": ", 47 possibly because they spend more time near the stove. The latest fuel data were from the national census for the year 2000. To be consistent with the smoking and tuberculosis data, we used the 2000 data for 2003, the fi rst year of analysis. The proportion of China's population in urban areas is increasing. To incorporate this eff ect in the solid-fuel scenarios, we used the current and projected urban and rural population share from the Population Division of the Department of Economic and Social Aff airs of the United Nations Secretariat.",
            "latex": null,
            "type": "figure"
          },
          "FIGREF2": {
            "text": "by the pooled estimate for smoking and latent infection (1·90). The estimated relative risk of 1·5(table 3)is within the 95% CI of the See Online for webtable 2 The two numbers show relative risks for the beginning and end of analysis period in the main analysis to account for the delayed smoking epidemic in China. We used the same relative risks for the current and future eff ects of smoking on tuberculosis, because the studies used in the meta-analysis were from populations with various durations of past exposure and because risk accumulation may be diff erent from chronic diseases like COPD and lung cancer.",
            "latex": null,
            "type": "figure"
          },
          "FIGREF3": {
            "text": "Sum of annual deaths 2003-33 for both sexes if exposure for both sexes to smoking and solid fuel use are reduced to zero by 2033 See Online for webfi gure Annual mortality in men from COPD (A) and lung cancer (B) and in women from COPD (C) and lung cancer (D) under combined scenarios of smoking and solid fuel use Not avoidable deaths are those if risk-factor exposures were reduced to zero in 2003. See webfi gures 4 and 5 for separate results for smoking and solid fuel use. See Online for webfi gure 4 gradually eliminated between 2003 and 2033, an estimated 26 million COPD deaths (40% of all projected COPD deaths) and 6·3 million lung cancer deaths (34% of all projected lung cancer deaths) would be avoided (fi gures 2 and 3). The intermediate scenarios have the potential to reduce mortality from these diseases by an estimated 17-34% among men and 18-29% among women. The prevalence of active tuberculosis declined between 1979 and 2000 in most provinces in China (webfi gure 1).",
            "latex": null,
            "type": "figure"
          },
          "FIGREF4": {
            "text": "Annual incidence of infectious tuberculosis under combined eff ects of smoking and indoor air pollution scenarios by municipality and DOTS eff ectivenessDecreases in incidence with optimum, moderate, or minimum DOTS in Jiangsu (A, B, C, respectively) and Guizhou (D, E, F) and for Shanghai (G), which already has eff ective DOTS so non-optimum scenarios not shown.solid-fuel use, would be 10-23% under optimum DOTS, 12-27% under moderate DOTS, and 15-33% under minimum DOTS.",
            "latex": null,
            "type": "figure"
          },
          "TABREF0": {
            "text": "See Online for webfi gure 1",
            "latex": null,
            "type": "table"
          },
          "TABREF1": {
            "text": "a lower peak than in other populations may result from sociocultural factors Unchanged Female smoking prevalence remains at its 2003 low level in each provinceSociocultural factors might prevent female smoking prevalence to rise further with economic development, as evidenced by relative long-term stability of female smoking",
            "latex": null,
            "type": "table"
          },
          "TABREF2": {
            "text": "Smoking scenarios",
            "latex": null,
            "type": "table"
          },
          "TABREF3": {
            "text": "Scenarios of household solid-fuel use",
            "latex": null,
            "type": "table"
          },
          "TABREF4": {
            "text": "Relative risks of smoking and solid-fuel use on COPD, lung cancer, and tuberculosis",
            "latex": null,
            "type": "table"
          }
        },
        "back_matter": [
          {
            "text": "This research was funded by the International Union Against Tuberculosis and Lung Disease, through a grant from the World Bank. We thank Cheng-Yuan Chiang, Donald Enarson, Enis Baris, and Ziad Obermeyer for valuable discussion on methods and models, Colin Mathers for mortality projections, Zheng Zhou for help with data on smoking and fuel use, Jonathan Sinton for discussions on fuels use trends in China, and the Chinese Ministry of Health for tuberculosis prevalence data. We thank the China Health and Nutrition Survey, Jill Boreham, Rachel Huxley, and Zhengming Chen for valuable information and data on risk factor exposure and eff ects.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Acknowledgments"
          }
        ]
      }
    ]
  }
}
Get the full-text, in JSON-format, of multiple journal articles from the CORD-19 dataset

HTTP URL: https://api.c3.ai/covid/api/1/biblioentry/getarticlemetadata

Request JSON:

{
  "ids" : ["000q5l5n", "000tfenb"] // list of "id" fields in BiblioEntry C3.ai Type.
}



Response JSON:

{
  "value": {
    "value": [
      {
        "paper_id": "3442b139e80c8351c89a9398709090db63edb8fe",
        "metadata": {
          "title": "Seroprevalence of Rodent Pathogens in Wild Rats from the Island of St. Kitts, West Indies",
          "authors": [
            {
              "first": "Kenneth",
              "middle": [],
              "last": "Boey",
              "suffix": "",
              "affiliation": {},
              "email": "boey_kenneth@hotmail.comk.b."
            },
            {
              "first": "Kanae",
              "middle": [],
              "last": "Shiokawa",
              "suffix": "",
              "affiliation": {},
              "email": "kshiokawa@yahoo.comk.s."
            },
            {
              "first": "Harutyun",
              "middle": [],
              "last": "Avsaroglu",
              "suffix": "",
              "affiliation": {},
              "email": "havsaroglu@rossvet.edu.knh.a.*correspondence:sree63rajeev@gmail.com"
            },
            {
              "first": "Sreekumari",
              "middle": [],
              "last": "Rajeev",
              "suffix": "",
              "affiliation": {},
              "email": ""
            }
          ]
        },
        "abstract": [
          {
            "text": "The role of rodents in the transmission of many diseases is widely known. Wild rats abundant in urban environments may transmit diseases to humans and other animals, including laboratory rodents used for biomedical research in research facilities, possibly compromising research data. In order to gather information about the various diseases present around such facilities, it is important to conduct routine surveillance of wild rodents in the area. In this pilot study, we surveyed 22 captured wild rats (Rattus norvegicus and Rattus rattus) from the Caribbean island of St. Kitts for 19 microorganisms. Information gained from such surveillance data would be beneficial in assessing regional public health risks and when implementing routine laboratory rodent health monitoring protocols.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Abstract"
          },
          {
            "text": "Abstract: A pilot seroprevalence study was conducted to document exposure to selected pathogens in wild rats inhabiting the Caribbean island of St. Kitts. Serum samples collected from 22 captured wild rats (Rattus norvegicus and Rattus rattus) were tested for the presence of antibodies to various rodent pathogens using a rat MFI2 serology panel. The samples were positive for cilia-associated respiratory bacillus (13/22; 59.1%), Clostridium piliforme (4/22; 18.2%), Mycoplasma pulmonis (4/22; 18.2%), Pneumocystis carinii (1/22; 4.5%), mouse adenovirus type 2 (16/22; 72.7%), Kilham rat virus (15/22; 68.2%), reovirus type 3 (9/22; 40.9%), rat parvovirus (4/22; 18.2%), rat minute virus (4/22; 18.2%), rat theilovirus (2/22; 9.1%), and infectious diarrhea of infant rats strain of group B rotavirus (rat rotavirus) (1/22; 4.5%). This study provides the first evidence of exposure to various rodent pathogens in wild rats on the island of St. Kitts. Periodic pathogen surveillance in the wild rat population would be beneficial in assessing potential regional zoonotic risks as well as in enhancing the current knowledge when implementing routine animal health monitoring protocols in facilities with laboratory rodent colonies.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Abstract"
          }
        ],
        "body_text": [
          {
            "text": "Wild and peridomestic rats, especially the Norway rat (Rattus norvegicus) and black rat (Rattus rattus), are known reservoirs of a number of rodent and zoonotic pathogens [1] . They are ubiquitous in urban and rural environments and are major pests of public health significance, as they carry and transmit pathogens that can cause significant mortality in humans and animals [2] . Wild rats may pose an animal biosecurity risk to laboratory rodent colonies due to inadvertent transmission, possibly causing significant complications in biomedical research [3] , in addition to zoonotic risks to laboratory animal caretakers and other personnel.",
            "cite_spans": [
              {
                "start": 171,
                "end": 174,
                "text": "[1]",
                "ref_id": "BIBREF0"
              },
              {
                "start": 376,
                "end": 379,
                "text": "[2]",
                "ref_id": "BIBREF1"
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              {
                "start": 557,
                "end": 560,
                "text": "[3]",
                "ref_id": "BIBREF2"
              }
            ],
            "ref_spans": [],
            "section": "Introduction"
          },
          {
            "text": "To gather pertinent information and document evidence of the exposure to common rodent pathogens in wild rats inhabiting the island of St. Kitts, West Indies, we conducted a pilot seroprevalence study and screened wild rats from the island for selected rodent pathogens.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Introduction"
          },
          {
            "text": "This study was conducted at Ross University School of Veterinary Medicine (RUSVM), Basseterre, St. Kitts, West Indies, adhering to a protocol approved by the Institutional Animal Care and Use Committee (protocol no. 17-01-04).",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Materials and Methods"
          },
          {
            "text": "Wild rats were captured with live traps (Tomahawk Trap Co, Tomahawk, WI, USA) around three main areas on the island of St. Kitts (Figure 1 ), from March to April 2017. All trapping locations were in urban areas where rats were expected to be passing. Traps were set overnight and checked the following morning. The captured rats were then immediately transported to the RUSVM necropsy facility. ",
            "cite_spans": [],
            "ref_spans": [
              {
                "start": 129,
                "end": 138,
                "text": "(Figure 1",
                "ref_id": "FIGREF0"
              }
            ],
            "section": "Wild Rat Trapping"
          },
          {
            "text": "Captured rats were euthanized using carbon dioxide gas (CO 2 ) and cervical dislocation. This was performed by placing traps containing the rats in a leak-proof plastic bag and filling it with CO 2 . Once the rats were visibly unconscious, they were taken out of the plastic bag and cervical dislocation was performed as secondary euthanasia. Subsequently, blood was collected by open cardiac puncture. We recorded the weight, sex, and body length of each rat. Serum was obtained after centrifugation of whole blood, and stored at −80 • C until used for serological analysis. The rat species was determined by amplification and sequencing of the mitochondrial cytochrome b gene [5] . Sequences were aligned by Molecular Evolutionary Genetic Analysis version 7.0 (MEGA7) [6] and a search of homologous sequences was performed using Basic Local Alignment Search Tool (BLAST) [7] .",
            "cite_spans": [
              {
                "start": 678,
                "end": 681,
                "text": "[5]",
                "ref_id": "BIBREF4"
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                "start": 770,
                "end": 773,
                "text": "[6]",
                "ref_id": "BIBREF5"
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                "ref_id": "BIBREF6"
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            "ref_spans": [],
            "section": "Sample Collection and Rat Identification"
          },
          {
            "text": "Serum samples were submitted to a commercial laboratory (IDEXX BioAnalytics, Columbia, MO, USA) to detect antibodies against the following agents using a multiplex fluorescent immunoassay ",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Serological Analysis"
          },
          {
            "text": "A total of 29 rats were collected from three areas around the island of St. Kitts, of which 22 were tested for antibodies against various rodent pathogens. For the other seven rats, a sufficient amount of serum was not able to be collected for this study. Of those 22 rats for which an adequate amount of serum was available, 12 (54.5%) were males and 10 (45.5%) were females. The rat species were identified as R. norvegicus (13/22; 59.1%) and R. rattus (9/22; 40.9%).",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Results"
          },
          {
            "text": "Exposure to 11 of 19 (57.9%) pathogens tested in the panel was detected, and 21 of the 22 (95.5%) rats sampled were positive for one or more pathogens tested (Table 1, Figure 2 Figure 2 ). No serological evidence of E. cuniculi, HTNV, LCMV, THV, MAV1, PVM, RCV/SDAV, and SeV was detected in any serum sample. Antibodies to significant zoonotic pathogens (HTNV and LCMV) were not detected in any of the samples. According to the serological results, one rat was negative for antibodies to all agents tested, and three rats were positive for antibodies to a single pathogen (two MAV2 and one RTV). Four rats were positive for antibodies to two tested pathogens, while 14 rats had antibodies against three or more of the pathogens tested. The highest number of pathogens detected was in a single rat with antibodies to seven pathogens. R. norvegicus had significantly higher prevalence of KRV (12/22; p ≤ 0.0066) and CARB (11/22; p ≤ 0.0073) compared to R. rattus (3/22 and 2/22, respectively). Fisher's exact test was the statistical method used due to the small sample size. There was no association identified between the presence of any pathogen and sex of the rat. ",
            "cite_spans": [],
            "ref_spans": [
              {
                "start": 168,
                "end": 176,
                "text": "Figure 2",
                "ref_id": "FIGREF2"
              },
              {
                "start": 177,
                "end": 185,
                "text": "Figure 2",
                "ref_id": "FIGREF2"
              }
            ],
            "section": "Results"
          },
          {
            "text": "This pilot seroprevalence study provides first evidence of the exposure to rodent pathogens in wild rats in St. Kitts, West Indies. Sampling the wild rodent population in St. Kitts for common pathogens known to affect laboratory rodent colonies would be substantially beneficial in enhancing the current knowledge when implementing routine animal health monitoring protocols with respect to the laboratory rodent colony at RUSVM, in addition to assessing for zoonotic risks to laboratory animal care personnel. ",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "This pilot seroprevalence study provides first evidence of the exposure to rodent pathogens in wild rats in St. Kitts, West Indies. Sampling the wild rodent population in St. Kitts for common pathogens known to affect laboratory rodent colonies would be substantially beneficial in enhancing the current knowledge when implementing routine animal health monitoring protocols with respect to the laboratory rodent colony at RUSVM, in addition to assessing for zoonotic risks to laboratory animal care personnel.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "In this study, antibodies against CARB, MAV2, and KRV were detected in over half of the rat serum samples tested. KRV, like H-1, RMV, and RPV, is a parvovirus that is frequently found in laboratory and wild rats that can persist in infected rats and the environment for long periods of time, and rats are a natural host for the virus. Unlike H-1, which has low significance in rats, KRV could tremendously interfere with biomedical research involving several body systems, especially if infection occurred during fetal development [3] . Parvoviruses H-1, RMV, and RPV are asymptomatic in naturally infected rats, compared to KRV, which, although rarely, causes clinical signs such as jaundice, ataxia, and scrotal cyanosis [3, 8] . Genetic and/or behavioral differences could be attributed to the increased seroprevalence of KRV in R. norvegicus.",
            "cite_spans": [
              {
                "start": 531,
                "end": 534,
                "text": "[3]",
                "ref_id": "BIBREF2"
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                "text": "[3,",
                "ref_id": "BIBREF2"
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                "start": 727,
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                "text": "8]",
                "ref_id": "BIBREF7"
              }
            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "The highest seroprevalence was to MAV2 (strain K87), while none of the samples were positive for MAV1 (strain FL). Mouse adenoviruses are rare and asymptomatic in mice and have minimal interference in biomedical research [3, 9] . The high seroprevalence observed in this study could be attributed to known seroconversion of a different virus to MAV2 in some rat colonies, and the cross-reactivity of MAV1 antiserum with MAV2 [9].",
            "cite_spans": [
              {
                "start": 221,
                "end": 224,
                "text": "[3,",
                "ref_id": "BIBREF2"
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                "text": "9]",
                "ref_id": null
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            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "Seroprevalence of CARB in this study was much higher than that of M. pulmonis, which contradicts previous findings of significant correlation and co-infection between the two pathogens [3, 10, 11] . In addition, we found a positive association between exposure to CARB and the species of rat (R. norvegicus), while it was not the case for M. pulmonis. Both pathogens are transmitted by aerosol and cause chronic infections in rodents, with dual infection involving both agents causing more severe pulmonary lesions [3] . In a previous study, the authors found that rats in the study had differences in susceptibility to M. pulmonis but not to CARB, despite similarities between the two infections [12] . This might be a reason for the large difference in seroprevalences of the two pathogens in our study.",
            "cite_spans": [
              {
                "start": 185,
                "end": 188,
                "text": "[3,",
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              {
                "start": 189,
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                "text": "10,",
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            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "REO3 also had a relatively high seroprevalence, though infected rats are usually asymptomatic and natural infection has not been proven to be linked specifically to interference with biomedical research [13] . Of the three serotypes of reoviruses (1, 2, and 3), type 3 is known to be the most pathogenic to laboratory rodents [3] .",
            "cite_spans": [
              {
                "start": 203,
                "end": 207,
                "text": "[13]",
                "ref_id": null
              },
              {
                "start": 326,
                "end": 329,
                "text": "[3]",
                "ref_id": "BIBREF2"
              }
            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "Antibodies to HTNV and LCMV were not detected in any of the tested rat serum samples. Both viruses are known to be significant zoonotic pathogens that would be a public health risk [8, 14] . Epidemiologically, the majority of HTNV infection is observed in Asia and in its reservoir host, the field mouse (Apodemus agrarius). Seoul virus, another hantavirus whose infection occurs worldwide, would be of more concern from a public health perspective, as R. rattus and R. norvegicus are the main reservoirs [15] . Further evaluation is needed to determine if the antigen used to detect HTNV antibodies in this panel cross-reacts with antibodies to Seoul virus. In addition, it would be essential to screen antibodies against other hantaviruses depending on the local epidemiology.",
            "cite_spans": [
              {
                "start": 181,
                "end": 184,
                "text": "[8,",
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              },
              {
                "start": 185,
                "end": 188,
                "text": "14]",
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              },
              {
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            ],
            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "Natural exposure and infections without any overt disease have been detected in laboratory rodent colonies, and the majority of these infections are caused by opportunists or commensals [3] . Even in the absence of pathogenic effects or clinical disease, colonization with these pathogens may alter biomedical research data [3, 8] . Using laboratory animals free from such pathogens contributes to the reliability and reproducibility of results in research studies [8] . In addition to the availability of specific pathogen-free rodents and individually ventilated caging, routine pathogen surveillance and health monitoring protocols in modern research facilities have resulted in lower pathogen prevalence in laboratory rodent colonies [16] .",
            "cite_spans": [
              {
                "start": 186,
                "end": 189,
                "text": "[3]",
                "ref_id": "BIBREF2"
              },
              {
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                "end": 327,
                "text": "[3,",
                "ref_id": "BIBREF2"
              },
              {
                "start": 328,
                "end": 330,
                "text": "8]",
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              },
              {
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            ],
            "ref_spans": [],
            "section": "Discussion"
          },
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            "text": "In this pilot seroprevalence study, we demonstrated evidence of exposure to rodent pathogens in wild rats in St. Kitts. Wild rats harbor a variety of rodent and zoonotic pathogens [1] that could be a source of contamination in laboratory rodent colonies, which may compromise biomedical research data, as well as jeopardize human and animal health [3, 8] . Periodic pathogen surveillance in the wild rat population would be beneficial in assessing potential regional zoonotic risks as well as in enhancing the current knowledge when implementing routine animal health monitoring protocols in research and breeding facilities with laboratory rodent colonies. Funding: This study received no external funding.",
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          "FIGREF0": {
            "text": "Map of St. Kitts, West Indies [4], showing the parishes and areas of rat capture sites (blue circles). A, around the campus of Ross University School of Veterinary Medicine in West Farm, and residential dwellings in Mattingley; B, near commercial establishments in downtown Basseterre and Port Zante; C, around the campus of the St. Kitts Biomedical Research Foundation (nonhuman primate facility) in Lower Bourryeau Estate.",
            "latex": null,
            "type": "figure"
          },
          "FIGREF1": {
            "text": "Map of St. Kitts, West Indies [4], showing the parishes and areas of rat capture sites (blue circles). A, around the campus of Ross University School of Veterinary Medicine in West Farm, and residential dwellings in Mattingley; B, near commercial establishments in downtown Basseterre and Port Zante; C, around the campus of the St. Kitts Biomedical Research Foundation (nonhuman primate facility) in Lower Bourryeau Estate.",
            "latex": null,
            "type": "figure"
          },
          "FIGREF2": {
            "text": "MFI2) panel (Rat Global Serology): cilia-associated respiratory bacillus (CARB), Clostridium piliforme, Mycoplasma pulmonis, Encephalitozoon cuniculi, Pneumocystis carinii, Toolan's H-1 virus (H-1), Hantaan virus (HTNV), infectious diarrhea of infant rats strain of group B rotavirus (IDIR strain of GBR; rat rotavirus), Kilham rat virus (KRV), lymphocytic choriomeningitis virus (LCMV), mouse adenovirus type 1 (MAV1), mouse adenovirus type 2 (MAV2), pneumonia virus of mice (PVM), rat coronavirus/sialodacryoadenitis virus (RCV/SDAV), reovirus type 3 (REO3), rat minute virus (RMV), rat parvovirus (RPV), rat theilovirus (RTV), and Sendai virus (SeV).",
            "latex": null,
            "type": "figure"
          },
          "FIGREF3": {
            "text": "Seroprevalence of selected rodent pathogens in wild rats from St. Kitts, West Indies. CARB, cilia-associated respiratory bacillus; H-1, Toolan's H-1 virus; HTNV, Hantaan virus; IDIR strain of GBR, infectious diarrhea of infant rats strain of group B rotavirus (rat rotavirus); KRV, Kilham rat virus; LCMV, lymphocytic choriomeningitis virus; MAV1, mouse adenovirus type 1 (strain FL); MAV2, mouse adenovirus type 2 (strain K87); PVM, pneumonia virus of mice; RCV/SDAV, rat coronavirus/sialodacryoadenitis virus; REO3, reovirus type 3; RMV, rat minute virus; RPV, rat parvovirus; RTV, rat theilovirus; SeV, Sendai virus.",
            "latex": null,
            "type": "figure"
          },
          "FIGREF4": {
            "text": "Contributions: Conceptualization: S.R.; Methodology: K.S. and S.R.; Validation: H.A. and S.R.; Formal Analysis: K.B. and K.S.; Investigation: K.S.; Resources: K.S.; Data Curation: K.B. and K.S.; Writing-Original Draft Preparation: K.B.; Writing-Review and Editing: K.B., K.S., H.A., and S.R.; Visualization: K.B.; Supervision: H.A. and S.R.; Project Administration: K.B. and K.S.; Funding Acquisition: S.R.",
            "latex": null,
            "type": "figure"
          },
          "TABREF0": {
            "text": "). Of the 22 tested serum samples, 72.7% (16/22; 95% confidence interval (CI): 54.1-91.3) were positive for MAV2, 68.2% (15/22; 95% CI: 48.7-87.7) for KRV, 59.1% (13/22; 95% CI: 38.6-79.6) for CARB, 40.9% (9/22; 95% CI: 20.4-61.4) for REO3, 18.2% (4/22; 95% CI: 2.1-34.3) for C. piliforme, M. pulmonis, RPV, and RMV, 9.1% (2/22; 95% CI: 0-21.1) for RTV, and 4.5% (1/22; 95% CI: 0-13.2) for P. carinii and IDIR strain of GBR(Table 1;",
            "latex": null,
            "type": "table"
          },
          "TABREF1": {
            "text": "Overall summary of rodent pathogens detected in wild rats from St. Kitts, West Indies. Area A, around the campus of Ross University School of Veterinary Medicine in West Farm, and residential dwellings in Mattingley; Area B, near commercial establishments in downtown Basseterre and Port Zante; Area C, around the campus of the St. Kitts Biomedical Research Foundation (nonhuman primate facility) in Lower Bourryeau Estate. Abbreviations: CARB, cilia-associated respiratory bacillus; H-1, Toolan's H-1 virus; HTNV, Hantaan virus; IDIR strain of GBR, infectious diarrhea of infant rats strain of group B rotavirus (rat rotavirus); KRV, Kilham rat virus; LCMV, lymphocytic choriomeningitis virus; MAV1, mouse adenovirus type 1 (strain FL); MAV2, mouse adenovirus type 2 (strain K87); PVM, pneumonia virus of mice; RCV/SDAV, rat coronavirus/sialodacryoadenitis virus; REO3, reovirus type 3; RMV, rat minute virus; RPV, rat parvovirus; RTV, rat theilovirus; SeV, Sendai virus.",
            "latex": null,
            "type": "table"
          }
        },
        "back_matter": [
          {
            "text": "We thank Ross University School of Veterinary Medicine, Center for One Health and Tropical Medicine for supporting this study.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Acknowledgments:"
          },
          {
            "text": "The authors declare no conflict of interest.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Conflicts of Interest:"
          }
        ]
      },
      {
        "paper_id": "c890cb0b691543c29b35b5a1351ff8c990739fe2",
        "metadata": {
          "title": "Prevalence and genetic diversity analysis of human coronaviruses among cross-border children",
          "authors": [
            {
              "first": "Peilin",
              "middle": [],
              "last": "Liu",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Lei",
              "middle": [],
              "last": "Shi",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Wei",
              "middle": [],
              "last": "Zhang",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Jianan",
              "middle": [],
              "last": "He",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Chunxiao",
              "middle": [],
              "last": "Liu",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Chunzhong",
              "middle": [],
              "last": "Zhao",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Siu",
              "middle": [
                "Kai"
              ],
              "last": "Kong",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Jacky",
              "middle": [],
              "last": "Fong",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Chuen",
              "middle": [],
              "last": "Loo",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Dayong",
              "middle": [],
              "last": "Gu",
              "suffix": "",
              "affiliation": {},
              "email": ""
            },
            {
              "first": "Longfei",
              "middle": [],
              "last": "Hu",
              "suffix": "",
              "affiliation": {},
              "email": ""
            }
          ]
        },
        "abstract": [
          {
            "text": "Background: More than a decade after the outbreak of human coronaviruses (HCoVs) SARS in Guangdong province and Hong Kong SAR of China in 2002, there is still no reoccurrence, but the evolution and recombination of the coronaviruses in this region are still unknown. Therefore, surveillance on the prevalence and the virus variation of HCoVs circulation in this region is conducted. Methods: A total of 3298 nasopharyngeal swabs samples were collected from cross-border children (<6 years, crossing border between Southern China and Hong Kong SAR) showing symptoms of respiratory tract infection, such as fever (body temperature > 37.5°C), from 2014 May to 2015 Dec. Viral nucleic acids were analyzed and sequenced to study the prevalence and genetic diversity of the four human coronaviruses. The statistical significance of the data was evaluated with Fisher chi-square test. Results: 78 (2.37%; 95%CI 1.8-2.8%) out of 3298 nasopharyngeal swabs specimens were found to be positive for OC43 (36;1.09%), HKU1 (34; 1.03%), NL63 (6; 0.18%) and 229E (2;0.01%). None of SARS or MERS was detected. The HCoVs predominant circulating season was in transition of winter to spring, especially January and February and NL63 detected only in summer and fall. Complex population with an abundant genetic diversity of coronaviruses was circulating and they shared homology with the published strains (99-100%). Besides, phylogenetic evolutionary analysis indicated that OC43 coronaviruses were clustered into three clades (B,D,E), HKU1 clustered into two clades(A,B) and NL63 clustered into two clades(A,B). Moreover, several novel mutations including nucleotides substitution and the insertion of spike of the glycoprotein on the viral surface were discovered.",
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            "section": "Abstract"
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        ],
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          {
            "text": "Conclusions: The detection rate and epidemic trend of coronaviruses were stable and no obvious fluctuations were found. The detected coronaviruses shared a conserved gene sequences in S and RdRp. However, mutants of the epidemic strains were detected, suggesting continuous monitoring of the human coronaviruses is in need among cross-border children, who are more likely to get infected and transmit the viruses across the border easily, in addition to the general public.",
            "cite_spans": [],
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            "section": "(Continued from previous page)"
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          {
            "text": "Keywords: Human coronaviruses, Cross-border children, Molecular epidemiology, Phylogenetic analysis, Genetic diversity Background Human coronaviruses (HCoVs) have been causing worldwide outbreak with cases of hospitalization [1] . Six types of coronaviruses (CoVs) are known to infect human: two α-CoVs, i.e. 229E and NL63, two β-CoVs group A, i.e. HKU1 and OC43, β-CoVs group B, i.e. Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and β-CoVs group C, i.e. Middle East Respiratory Syndrome Coronavirus (MERS-CoV). SARS-CoV and MERS-CoV, which are highly pathogenic to human lives and have caused serious diseases or death, causes about 10 and 36% mortality respectively. OC43, HKU1, NL63 and 229E are the most common four HCoVs in most regions, circulating worldwide with a detection rate ranging from 1.1 -8.5% and with variations in their predominantly circulating seasons and strains [2] [3] [4] [5] . HCoVs ranks the third in the detection rate of all 17 respiratory viruses in south of China (Guangzhou) and poses a heavy burden to the health care of children as it is associated with acute upper or lower respiratory tract infections, and cases of death have been reported [6] . Moreover, high mutation rates caused by the low fidelity of RNA-dependent RNA polymerase (RdRp) led to high diversity of HCoVs [7] . Several studies about the genetic diversity of human coronaviruses on hospitalized patients had been carried out previously. The new OC43 genotype D based on the recombination of B and C was discovered in 2005 [8] . Two additional recombinants: E (CH) and E (FR) were reported as homologous genome recombination in 2015 [9, 10] . The genetic features of NL63 were reported at least three distinct circulating genotypes (A, B and C) and one recombinant (cluster R) in the United States in 2011 [11] . Meanwhile, HKU1 strains were grouped into three clusters (A, B and C) due to natural recombination [12] . These previous reports focused on hospitalized patients, who have low mobility and seldom cross the border, while this study hereby firstly reports the analysis on crossborder children, mainly including \"cross-boundary students\", who are born and attend school in Hong Kong but reside in Mainland China [13, 14] . A border still exists between Shenzhen in Mainland China and Hong Kong (SZ-HK port) due to the colonial history, resulting in different health care and education systems [13] . Children had a high incidence of coronaviruses infection and \"cross-boundary students\" connecting closely Hong Kong and Mainland China will help us understand the epidemic characteristics of coronaviruses in the Pearl River Delta region. New occurrence of infectious coronaviruses and the known pan-coronavirus variation among this region are of our study interest because the coronaviruses have the potential to threaten global health system and no vaccine is currently available [15, 16] . Therefore, surveillance upon human coronaviruses among this region was carried in this study.",
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            "section": "(Continued from previous page)"
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          {
            "text": "This was a cross-sectional study in molecular epidemiology for coronaviruses infection, and the minimum sample size of this study was 1683 as determined by Z distribution. A total of 3298(>1683) nasopharyngeal swabs samples were collected from children (<6 years) who passed Shenzhen border, linking Southern China and Hong Kong SAR, from 2014 to 2015 and showed symptoms of respiratory tract infection, such as fever (body temperature > 37.5°C) and cough. Written informed consent was obtained from the guardians of all participants before the sample and data collection.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Clinical specimens collection"
          },
          {
            "text": "Briefly, nasopharyngeal swab was collected and stored in a sterile EP tube with 5 mL viral transport medium in Shenzhen border. All the samples collected were immediately refrigerated at 2-8°C and transported to the central laboratory of health quarantine of Shenzhen Entry-exit Inspection and Quarantine Bureau (SZCIQ) within the same day and stored at −80°C until analysis.",
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            "section": "Sample preparation"
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          {
            "text": "Viral nucleic acids were extracted from 200 μL respiratory samples using MagNA pure 96 DNA with Viral NA small volume kit (Roche) and EZ1 virus Mini kit V2.0 (Qiagen) according to the manufacturer's instructions. The viral nucleic acids were stored at −80°C until use. For the coronaviruses screening, a quantitative real-time polymerase chain reaction (qRT-PCR) was performed in triplicate using ABI 7500 qRT-PCR thermocycler. The specimens were firstly screened for influenza viruses according to the procedure previously published [17] . Samples of negative results on influenza were then tested for pan-coronavirus as well as 13 other common respiratory viruses. The qRT-PCR master mixture was performed according to the manufacturer's instructions of qRT-PCR Kit (Quant), mainly contained 20.0 μL buffer and 5.0 μL RNA. The thermal cycling conditions were set as follows: reverse transcription at 50°C for 10 min, initial 95°C for 3 min, 40 cycles of PCR amplification at 95°C for 15 s, annealing/elongation at 60°C for 45 s. The partial S (S1 subunit) and RdRp genes were detected in the positive samples after HCoVs screening with the forward (F) and reverse (R) primers listed in Table 1 . The PCR mixture (25 μL) contained 5.0 μL of RNA, PCR buffer mixed with Superscript ®III/PT Taq Kit (Invitrogen) containing 12.5 μl of 2× Rxn Mix,1 μL of forward and reverse primer (10 μM), 1.0 μL of MgSO 4 , 1.0 μL of BSA (0.1%),1.0 μl of Superscript ®III/PT Taq Enzyme, 0.5 μL of RNA Inhibitor, 2.0 μL of nuclease free water. The thermal cycling conditions were set as follows: reverse transcription at 50°C for 30 min, 35 cycles of PCR amplification at 94°C for 30 s, annealing at 50-54°C for 30 s, elongation at 68°C for 150-180 s, final elongation at 68°C for 5 min. Sanger sequencing (Sangon Biotech) of the PCR products of concentration ranging from 50 to 300 ng/μL was performed to study the homology and mutations of samples. Genetic sequence data have been submitted to a publicly available repository (Genbank) and the accessible sequence accession numbers (MF996589-MF996664) including features of the samples and sequences. (Fig. 1a) . The results of the clinical symptoms of these samples were shown in Table 2 .",
            "cite_spans": [
              {
                "start": 534,
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                "text": "[17]",
                "ref_id": "BIBREF16"
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                "text": "Table 1",
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                "start": 2134,
                "end": 2143,
                "text": "(Fig. 1a)",
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                "start": 2214,
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                "text": "Table 2",
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            ],
            "section": "Molecular screening of virus and amplification, sequencing of RdRp and S genes"
          },
          {
            "text": "Males and females shared a common detection rate of all the HCoVs studied and no significant difference was found among the detection rate of the four strains. Also, the p values of Fisher's chi-square test showed no significant difference in detection rates among different origins. The first three clinical symptoms of HCoVs infection were fever (p = 0.08), throat congestion (p = 0.58) and antiadoncus (p = 0.09). Yet, there was no significant difference between HCoVs infected and noninfected patients. For the age group distribution of four HCoVs infections, the infant age group (<1 year old) with weaker respiratory immunity was showed with the highest infection rate in total types of HCoVs infection (p = 0.049) and OC43 infection (p = 0.068) (Fig. 1b) .",
            "cite_spans": [],
            "ref_spans": [
              {
                "start": 752,
                "end": 761,
                "text": "(Fig. 1b)",
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            "section": "Molecular screening of virus and amplification, sequencing of RdRp and S genes"
          },
          {
            "text": "There was virus co-infection between human coronaviruses with other common respiratory diseases. Adenovirus(Adv) and Rhinovirus(RV) were the most common two viruses that concomitantly detected with HCoVs in children younger than 6 years old. A total of 40 RdRp genes, including 20 for OC43, 15 for HKU1, 4 for NL63 and 1 for 229E, and 36 S genes, including 16 for OC43, 16 for HKU1 and 4 for NL63, were sequenced to perform phylogenetic analysis. Since there is a high conservative in RdRp gene, phylogenetic tree was not shown here. Multiple alignments results of Fig. 2 Phylogenetic analysis based on partial S genes of OC43, HKU1 and NL63. (I) Phylogenetic tree of OC43 S genes (2.2 kb) constructed with maximum likelihood; (II) Phylogenetic tree of HKU1 S genes (2.4 kb) constructed with maximum likelihood; (III) Phylogenetic tree of NL63 S genes (4.0 kb) constructed with neighbour-joining. Our samples were indicated with a red spot and others were used as referenced strains from complete genomes in GenBank. The strains indicated with \"*\" were clustered into genotype E, recombinant of B, C and D. The OC43 and NL63 phylogenetic trees were constructed using BCoV and Bat CoV respectively as outgroup RdRp genes indicated that OC43 and HKU1 possessed 99-100% nt identities. Largest divergences were observed in HKU1 coronaviruses, which possessed 96 -100% nt identities, but sequences detected in this study were 99-100% homologous to the published strains (Table 3) . For the phylogenetic trees constructed based on 31 S genes with a genomic length over 2 kb of four HCoVs, there was a high level of genetic diversity among those HCoVs (Fig. 2) . The OC43 coronaviruses were clustered into clade B (5,41.7%), clade D (6,50%) and clade E(1,8.3%) while none of the strains of genotype A and C was detected (Fig. 2I) . Besides, there was one OC43 sequence (SW1502-30/2015/Shenzhen, China) being clustered with a new recombination genotype E (CH) (Genbank accession no: KP198611.1). Similarly, HKU1 strains in this study were clustered into clade A (7,46.7%) and clade B (8,53.3%) and related to the sequences detected in Beijing and Hong Kong SAR respectively, while no clade C was detected (Fig. 2 II) . NL63 strains in this study were clustered into clade A (1,25.0%) and clade B (3,75.0%), related to strains isolated from USA and Denmark, while no clade C were detected neither (Fig. 2 III) . Moreover, we found nucleotide mutations in some of the samples (Fig. 3) . Three out of 8 OC43 coronaviruses of genotype D had a total of 11 bases substitution in nucleotide position 25,059-25,112 of S genes (Genbank accession number of referenced strain: KF923904.1) (Fig. 3a) . Six out of 8 HKU1 coronaviruses of genotype B were found with an extra insertion in nucleotide position 24,465 of genome leading to an additional amino acid \"Threonine\" insertion in amino acid position 510 of Spike (Genbank accession of referenced strain: DQ415911.1) (Fig. 3b ).",
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                "start": 565,
                "end": 571,
                "text": "Fig. 2",
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                "text": "(Table 3)",
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                "text": "(Fig. 2)",
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                "text": "(Fig. 2I)",
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                "text": "(Fig. 2 II)",
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                "text": "(Fig. 2 III)",
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                "text": "(Fig. 3)",
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                "text": "(Fig. 3a)",
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            ],
            "section": "Molecular screening of virus and amplification, sequencing of RdRp and S genes"
          },
          {
            "text": "The detection rate of total HCoVs was 2.37% (95% CI: 1.8 to 2.8%) in this study was consistent with the previous studies. All the coronaviruses detected have been typed. OC43 was the most common coronaviruses in our study consistent with reports in Guangzhou, Hong Kong, USA and England [4, [18] [19] [20] Fig. 3 Mutation analysis on the S genes of OC43 and HKU1. a Bases substitution in S1 genes of OC43. b Extra insertion in putative RBD of HKU1 that the prevalence of NL63 was similar to or even higher than that of OC43 in Brazil, Kenya and Japan [3, [21] [22] [23] . 229E was detected in low levels throughout years as previous reports and thus the peak activity of 229E could not be determined. The HCoVs predominant circulating season was in transition of winter to spring, especially January and February. NL63 predominant circulating seasons were summer and fall, which were different from those reports of winter and spring in temperate countries, such as the USA and Netherlands [24, 25] . None of the infection was found in the 1-2 years old group, even though the number of sample of this group was higher than that of the infant age group. In summary, we had analyzed the prevalent and clinical characteristics of HCoVs infection in cross-border children in SZ-HK ports. Compared with previous reports, the detection rate and epidemic trend of coronaviruses were stable, and no obvious fluctuations were found. Yet, none of novel infectious coronaviruses, SARS and MERS were detected in this study.",
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            ],
            "ref_spans": [
              {
                "start": 306,
                "end": 312,
                "text": "Fig. 3",
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            ],
            "section": "Discussion"
          },
          {
            "text": "The coronaviruses detected from SZ-HK ports had a high homology with the published strains indicated a stable gene sequences in S and RdRp. However, there were great genetic diversity among these circulating strains. OC43 detected in this report cluster with genotype B, D and E strains, while none of genotypes A and C were detected, probably because genotype A strains had disappeared and genotype C strains were not included in this study [9] . We observed six OC43 coronaviruses were closely related to the genotype B detected from Beijing based on S genes. It possessed 99% nt identities and showed an incongruent phylogenetic relationship between RdRp and S genes. New Recombination genotypes led by high intra-specific diversity have been reported in studying OC43 coronaviruses circulating in France, where eight different recombinants were discovered and confirmed with in silico analysis of complete genomes available using partial genome sequencing [10] . At present, the base substitution and insertion in OC43 and HKU1 is novel and could not find any matches in either OC43 or HKU1 strains in Genbank library. More importantly, these amino acid sites are located in one of the putative regions of HKU1 receptor binding domain [26] . The protein structure and its related function, especially on the efficiency on human infection, need to be investigated in the future.",
            "cite_spans": [
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            "ref_spans": [],
            "section": "Discussion"
          },
          {
            "text": "The detection rate of coronaviruses were in line with previous reports, no novel infectious coronaviruses was detected, the epidemic trend of coronaviruses were stable and all the infectors showed normal respiratory infection symptoms. Besides there were great genetic diversity of coronaviruses detected from SZ-HK ports and all the strains had a high homology compared with the published strains. However, mutant of the epidemic strains detected during our surveillance are increasing, therefore continuous monitoring of the human coronaviruses is in need among cross-border children, who are more likely to get infected and transmit the viruses across the border easily, in addition to the general public. ",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Conclusions"
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        "ref_entries": {
          "FIGREF1": {
            "text": "Adv: Adenovirus; Bat CoV: Bat coronavirus; BCoV: Bovine coronavirus; Cox A6: Coxsackievirus A6; EV: Enterovirus; HBoV: Human Bocavirus; HCoVs: Human coronaviruses; MP: Mycoplasma pneumonia; qRT-PCR: Quantitative real-time polymerase chain reaction; RdRp: RNA-dependent RNA polymerase; RSV: Respiratory syncytial virus; RT-PCR: Reverse transcription polymerase chain reaction; RV: Rhinovirus; SZ-HK: Shenzhen-Hong Kong",
            "latex": null,
            "type": "figure"
          },
          "TABREF0": {
            "text": "PCR primers of RdRp, S genes of four HCoVs Fig. 1 Epidemiological characteristics of human coronaviruses infection among Cross-border children. a Distribution of the four HCoV infections based on Month group. b Distribution of the four HCoV infections based on Age group. Positive and co-infected cases were plotted on the left Y-axis and others were plotted on the right Y-axis. Different strains or total HCoVs were indicated according to the keyStatistical and sequence analysisThe statistical significance of the data was evaluated with SPSS 20.0. All the p-value determined by Fisher's Chisquare test and a p-value <0.05 was considered statistically significant. DNASTAR was used to analyze and illustrate the gene sequences compared with the sequences in NCBI Genbank for homology study.",
            "latex": null,
            "type": "table"
          },
          "TABREF1": {
            "text": "Statistics of closely related strains of HCoVs based on RdRp and S gene",
            "latex": null,
            "type": "table"
          },
          "TABREF2": {
            "text": ", but some studies demonstrated",
            "latex": null,
            "type": "table"
          }
        },
        "back_matter": [
          {
            "text": "Not applicable. Availability of data and materials Genetic sequence data have been submitted to a publicly available repository (Genbank) with the accessible sequence accession numbers (MF996589-MF996664).",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Acknowledgements"
          },
          {
            "text": "Authors' contributions LS, DG and LH designed the whole project. PL drafted the manuscript. WZ involved amplifying the genes. PL, LS and LH analyzed the data. JH, CL and CZ designed and participated in the virus detection experiment. JFCL and SKK participated in the analysis of coronaviruses sequencing. JFCL, DG and LH provided important guidance and revised the manuscript before submission. All authors read and approved the final manuscript.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "annex"
          },
          {
            "text": "This study was ethically approved by Shenzhen Entry-exit Inspection and Quarantine Bureau, Shenzhen, China. Written informed consent was obtained from the guardians of all participants before the sample and data collection.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Ethics approval and consent to participate"
          },
          {
            "text": "Not applicable.",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Consent for publication"
          },
          {
            "text": "The authors declare that they have no competing interests.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:",
            "cite_spans": [],
            "ref_spans": [],
            "section": "Competing interests"
          }
        ]
      }
    ]
  }
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
ids
Array of strings

List of BiblioEntry reference IDs.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
value
object (The Result Schema)
post /api/1/biblioentry/getarticlemetadata
https://api.c3.ai/covid/api/1/biblioentry/getarticlemetadata

Request samples

Content type
application/json
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{
  • "ids":
    [
    ]
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "value": { }
}

TherapeuticAsset

TherapeuticAsset stores details about the research and development (R&D) of coronavirus therapies, e.g., vaccines, diagnostics, and antibodies.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string Therapy ID.
productType string Therapy's drug or platform class.
links ExternalLink C3.ai Type ExternalLink.
description string Description of the therapy.
clinicalTrialsOtherDiseases string Other diseases or pathogens for which the therapy has undergone or is undergoing clinical development.
developer string Organization that developed the therapy.
stageOfDevelopment string The therapy's current phase of clinical development. Allowed values: Clinical, Pre-Clinical, Compassionate Use, Phase 1/2 (not yet recruiting), Phase I, Expanded access.
fundingSources string The organization funding the therapy's R&D.
nextSteps string Anticipated next steps for the therapy's clinical development.
therapyType string The type of therapy. Allowed values: Vaccine, Antibodies, Antivirals, Cell-based therapies, RNA-based therapies, Scanning compounds to repurpose, Dormant Discontinued, Other.
origin string The source of the data containing the therapy's R&D details. Allowed values: WHO, Milken.
target string The virus the therapy targets or treats. Allowed values: COVID-19, MERS, SARS.
sources string URLs of public sources from which the therapy's information was collected.
fdaApprovalStatus string Details about the status of the therapy's FDA approval.
clinicalTrialsCovid19 string Active clinical trials to evaluate the therapy's efficacy for treating COVID-19.
lastUpdatedNotes string Date on which a specific therapy was last updated.
publishedResults string Publications on the therapy's efficacy for treating COVID-19.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Fetch all therapies

HTTP URL: https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request JSON:

{
  "spec": {
    "limit": -1
  }
}
Fetch therapies tracked by Milken Institute (request only)

HTTP URL: https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request JSON:

{
  "spec": {
    "filter": "origin == 'Milken'"
  }
}
Fetch therapies tracked by WHO (request only)

HTTP URL: https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request JSON:

{
  "spec": {
    "filter": "origin == 'WHO'"
  }
}
Fetch vaccine therapies

HTTP URL: https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request JSON:

{
  "spec": {
    "filter": "therapyType == 'Vaccine'"
  }
}
Fetch pre-clinical DNA therapies, targeting COVID-19, tracked by WHO

HTTP URL: https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request JSON:

{
  "spec": {
    "filter": "contains(productType, 'DNA') && stageOfDevelopment == 'Pre-Clinical' && origin == 'WHO' && target == 'COVID-19'"
  }
}



Response JSON:

{
  "objs": [
    {
        "productType": "DNA",
        "description": "DNA with electroporation",
        "developer": "Karolinska Institute / Cobra Biologics (OPENCORONA Project)",
        "stageOfDevelopment": "Pre-Clinical",
        "therapyType": "Vaccine",
        "origin": "WHO",
        "target": "COVID-19",
        "id": "-3328432098485197346",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T09:58:36Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T09:58:36Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T09:58:36Z",
            "fetchInclude": "[]",
            "fetchType": "TherapeuticAsset"
        },
        "version": 1
    },
    {
        "productType": "DNA",
        "description": "DNA plasmid vaccine",
        "developer": "Zydus Cadila",
        "stageOfDevelopment": "Pre-Clinical",
        "therapyType": "Vaccine",
        "origin": "WHO",
        "target": "COVID-19",
        "id": "2177139788646478350",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T09:58:36Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T09:58:36Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T09:58:36Z",
            "fetchInclude": "[]",
            "fetchType": "TherapeuticAsset"
        },
        "version": 1
    },
    {
        "productType": "DNA",
        "description": "DNA",
        "developer": "Takis/Applied DNA Sciences/Evvivax",
        "stageOfDevelopment": "Pre-Clinical",
        "therapyType": "Vaccine",
        "origin": "WHO",
        "target": "COVID-19",
        "id": "2966580801280467198",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T09:58:36Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T09:58:36Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T09:58:36Z",
            "fetchInclude": "[]",
            "fetchType": "TherapeuticAsset"
        },
        "version": 1
    },
    {
        "productType": "DNA",
        "description": "DNA plasmid vaccine Electroporation device",
        "clinicalTrialsOtherDiseases": "Lassa, Nipah, HIV, Filovirus, HPV, Cancer indications, Zika, Hepatitis B",
        "developer": "Inovio Pharmaceuticals",
        "stageOfDevelopment": "Pre-Clinical",
        "therapyType": "Vaccine",
        "origin": "WHO",
        "target": "COVID-19",
        "id": "742688045948574216",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T09:58:36Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T09:58:36Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T09:58:36Z",
            "fetchInclude": "[]",
            "fetchType": "TherapeuticAsset"
        },
        "version": 1
    },
    {
        "productType": "DNA",
        "description": "DNA plasmid vaccine",
        "developer": "Osaka University/ AnGes/ Takara Bio",
        "stageOfDevelopment": "Pre-Clinical",
        "therapyType": "Vaccine",
        "origin": "WHO",
        "target": "COVID-19",
        "id": "8960724358014432571",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T09:58:36Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T09:58:36Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T09:58:36Z",
            "fetchInclude": "[]",
            "fetchType": "TherapeuticAsset"
        },
        "version": 1
    }
],
"count": 5,
"hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/therapeuticasset/fetch
https://api.c3.ai/covid/api/1/therapeuticasset/fetch

Request samples

Content type
application/json
Copy
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string External link ID.
urlType string The type of information available at the URL. Allowed values: News (e.g. news article, press release), Clinical Trial, Published Results.
url string The URL of the website.
therapeuticAsset TherapeuticAsset C3.ai Type TherapeuticAsset.
origin string The source of the data containing the therapy's R&D details. Allowed values: WHO, Milken.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Fetch URLs for all therapies (request only)

HTTP URL: https://api.c3.ai/covid/api/1/externallink/fetch

Request JSON:

{
  "spec": {
    "limit": -1
  }
}
Fetch URLs for therapies tracked by Milken Institute (request only)

HTTP URL: https://api.c3.ai/covid/api/1/externallink/fetch

Request JSON:

{
  "spec": {
    "filter": "origin == 'Milken'"
  }
}
Fetch URLs for clinical trials of therapies (request only)

HTTP URL: https://api.c3.ai/covid/api/1/externallink/fetch

Request JSON:

{
  "spec": {
    "filter": "urlType == 'Clinical Trial'"
  }
}
Fetch URLs for a particular therapy

HTTP URL: https://api.c3.ai/covid/api/1/externallink/fetch

Request JSON:

{
  "spec": {
    "filter": "therapeuticAsset == 'milkentreatment_036'"
  }
}



Response JSON:

{
  "objs": [
    {
        "urlType": "News",
        "url": "https://apps.who.int/trialsearch/Trial2.aspx?TrialID=ChiCTR2000030424",
        "therapeuticAsset": {
            "id": "milkentreatment_036"
        },
        "origin": "Milken",
        "id": "33bf68b9-0860-4046-8717-c8ac8adb5a52",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T10:07:13Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T10:07:13Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T10:07:13Z",
            "fetchInclude": "[]",
            "fetchType": "ExternalLink"
        },
        "version": 1
    },
    {
        "urlType": "Clinical Trial",
        "url": "http://www.chictr.org.cn/showprojen.aspx?proj=50174",
        "therapeuticAsset": {
            "id": "milkentreatment_036"
        },
        "origin": "Milken",
        "id": "3a8ce559-ce8c-4874-aa89-a3b86a5f61da",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T10:07:13Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T10:07:13Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T10:07:13Z",
            "fetchInclude": "[]",
            "fetchType": "ExternalLink"
        },
        "version": 1
    },
    {
        "urlType": "Clinical Trial",
        "url": "http://www.chictr.org.cn/showprojen.aspx?proj=49532",
        "therapeuticAsset": {
            "id": "milkentreatment_036"
        },
        "origin": "Milken",
        "id": "a82a982b-a31d-48f0-896a-6a7569554540",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T10:07:13Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T10:07:13Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T10:07:13Z",
            "fetchInclude": "[]",
            "fetchType": "ExternalLink"
        },
        "version": 1
    },
    {
        "urlType": "Clinical Trial",
        "url": "http://www.chictr.org.cn/showprojen.aspx?proj=50507",
        "therapeuticAsset": {
            "id": "milkentreatment_036"
        },
        "origin": "Milken",
        "id": "d581a310-f556-4e04-8cce-95508cb714b1",
        "meta": {
            "tenantTagId": 4,
            "tenant": "covid",
            "tag": "prod",
            "created": "2020-04-08T10:07:13Z",
            "createdBy": "santiago.lopez@c3iot.com",
            "updated": "2020-04-08T10:07:13Z",
            "updatedBy": "santiago.lopez@c3iot.com",
            "timestamp": "2020-04-08T10:07:13Z",
            "fetchInclude": "[]",
            "fetchType": "ExternalLink"
        },
        "version": 1
    }
],
"count": 4,
"hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/externallink/fetch
https://api.c3.ai/covid/api/1/externallink/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

Hospital

Hospital stores statistics for 6,000+ hospitals in the United States, such as the number of licensed beds, staffed beds, intensive care unit (ICU) beds, and bed utilization rate.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
name string Hopsital name.
location OutbreakLocation C3.ai Type OutbreakLocation associated with hospital.
hospitalType string Hospital type. Examples include Short Term Acute Care Hospital, Critical Access Hospital, and VA Hospital.
address string Hospital address.
lat float Hospital's latitude.
lon float Hospital's longitude.
licensedBeds int Maximum number of beds the hospital holds the license to operate.
staffedBeds int Number of adult, pediatric, birthing room, and ICU beds maintained in patient care areas of the hospital.
icuBeds int Number of intensive care unit (ICU) beds in the hospital.
icuBedsAdult int Number of ICU beds for adults in the hospital.
icuBedsPedi int Number of pediatric ICU beds in the hospital.
icuBedsPotential int Potential increase in bed capacity in the hospital, computed as the number of licensed beds minus the number of staffed beds.
ventilatorUsage int Hospital's average number of ventilators in use.
bedUtilization float Hospital's average bed utilization rate, computed based on the Medicare Cost Report as the total number of patient days (excluding nursery days) divided by the available bed days.

Examples (Click on the arrows to expand)

The following example shows how to use this API.

Example: Fetch all hospitals in Oklahoma

HTTP URL: https://api.c3.ai/covid/api/1/hospital/fetch

Request JSON:

{
  "spec": {
    "filter": "contains(location, 'Oklahoma')"
  }
}



Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Adair_Oklahoma_UnitedStates"
      },
      "name": "Stilwell Memorial Hospital",
      "hospitalType": "Short Term Acute Care Hospital",
      "address": "1401 W Locust St,Stilwell,74960,OK,USA",
      "staffedBeds": 30,
      "licensedBeds": 67,
      "icuBeds": 5,
      "icuBedsAdult": 5,
      "icuBedsPedi": 0,
      "bedUtilization": 0.49452,
      "icuBedsPotential": 37,
      "ventilatorUsage": 1,
      "lat": 35.8089,
      "lon": -94.6429,
      "id": "3019",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-23T09:11:12Z",
        "createdBy": "dataloader",
        "updated": "2020-04-23T09:11:12Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-04-23T09:12:07Z",
        "sourceFile": "definitive.csv",
        "fetchInclude": "[]",
        "fetchType": "Hospital"
      },
      "version": 1
    },
    ...
  ],
  "count": 158,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/hospital/fetch
https://api.c3.ai/covid/api/1/hospital/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

Diagnosis

Diagnosis stores basic clinical data (e.g. clinical notes, demographics, test results, X-ray or CT scan images) about individual patients tested for COVID-19, from research papers and healthcare institutions.

The fetch API provides tabular clinical data, while the getimageurls API provides accessible URLs for X-ray or CT scan images.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
source string Data source for the patient records. Allowed values: Montreal (University of Montreal), Braid (Carbon Health & Braid Health).
imageURL string URL hosting the patient's X-ray or CT scan images.
location OutbreakLocation C3.ai Type OutbreakLocation representing the location of the patient.
idPatient string Unique patient ID.
age float Patient's age, with noise added to protect patient privacy.
clinicalNotes string A short paragraph containing clinical notes about the patient.
temperature float Patient's temperature, in degrees Celsius.
diagnostics DiagnosisDetail C3.ai Type DiagnosisDetail containing associated patient-specific details.
testResults string Patient's test results. Examples include COVID-19: Negative, COVID-19: Positive, ARDS: positive, and Streptococcus: positive.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch all test results and affiliated Diagnosis Metadata information for patients tested for COVID-19

HTTP URL: https://api.c3.ai/covid/api/1/diagnosis/fetch

Request JSON:

{
  "spec" : {
    "filter": "contains(testResults, 'COVID-19')", include: "this, diagnostics.source, diagnostics.key, diagnostics.value"
  }
}



Response JSON:

{
  "objs": [
    {
      "diagnostics": [
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "date",
          "value": "2020",
          "source": "Montreal",
          "id": "date_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "folder",
          "value": "images",
          "source": "Montreal",
          "id": "folder_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "hospital",
          "value": "Italy",
          "source": "Montreal",
          "id": "hospital_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "modality",
          "value": "X-ray",
          "source": "Montreal",
          "id": "modality_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "offset",
          "value": "5.0",
          "source": "Montreal",
          "id": "offset_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "other_notes",
          "value": "Credit to Izzo Andrea, D'Aversa Lucia, Ceremonial Giuseppe, Mazzella Giuseppe, Pergoli Pericle, Faiola Eugenio Leone, Di Pastena Francesca",
          "source": "Montreal",
          "id": "other_notes_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "pO2_saturation",
          "value": "70.0",
          "source": "Montreal",
          "id": "pO2_saturation_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "sex",
          "value": "F",
          "source": "Montreal",
          "id": "sex_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "url",
          "value": "https://www.sirm.org/2020/03/10/covid-19-caso-26/",
          "source": "Montreal",
          "id": "url_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        },
        {
          "parent": {
            "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
          },
          "key": "view",
          "value": "PA",
          "source": "Montreal",
          "id": "view_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
          "meta": {
            "fetchInclude": "[id,source,key,value,parent,version]",
            "fetchType": "DiagnosisDetail"
          },
          "version": 1
        }
      ],
      "imageUrl": "MONTREAL/images/01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "source": "Montreal",
      "location": {
        "id": "Italy"
      },
      "idPatient": "46",
      "age": 55.0,
      "testResults": "COVID-19: positive",
      "clinicalNotes": "Woman, 55 years old, reports dyspnea for a few days, does not report fever. In the history of asthma and type II diabetes. At first he denies contacts with people in a feverish state and coming from areas at risk. After a more accurate and \"insistent\" anamnesis, he reports that the cohabiting son works in a company where COVID-19 cases have occurred in the risk area (Lombardy).",
      "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:17Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:17Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:17Z",
        "fetchInclude": "[this,{diagnostics:[id,source,key,value]}]",
        "fetchType": "Diagnosis"
      },
      "version": 1
    },
    ...
  ],
  "count": 1882,
  "hasMore": false
}
Example 2: Fetch all test results for patients who received a positive diagnosis for some disease

HTTP URL: https://api.c3.ai/covid/api/1/diagnosis/fetch

Request JSON:

{
  "spec": {
    "filter": "contains(lowerCase(testResults), 'positive')"
  }
}



Response JSON:

{
"objs": [
  {
    "imageUrl": "MONTREAL/images/01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
    "source": "Montreal",
    "location": {
      "id": "Italy"
    },
    "idPatient": "46",
    "age": 55.0,
    "testResults": "COVID-19: positive",
    "clinicalNotes": "Woman, 55 years old, reports dyspnea for a few days, does not report fever. In the history of asthma and type II diabetes. At first he denies contacts with people in a feverish state and coming from areas at risk. After a more accurate and \"insistent\" anamnesis, he reports that the cohabiting son works in a company where COVID-19 cases have occurred in the risk area (Lombardy).",
    "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
    "meta": {
      "tenantTagId": 4,
      "tenant": "covid",
      "tag": "prod",
      "created": "2020-04-27T12:35:17Z",
      "createdBy": "santiago.lopez@c3iot.com",
      "updated": "2020-04-27T12:35:17Z",
      "updatedBy": "santiago.lopez@c3iot.com",
      "timestamp": "2020-04-27T12:35:17Z",
      "fetchInclude": "[]",
      "fetchType": "Diagnosis"
    },
    "version": 1
  },
  ...
 ],
"count": 461,
"hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/diagnosis/fetch
https://api.c3.ai/covid/api/1/diagnosis/fetch

Request samples

Content type
application/json
Copy
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

GetImageURLs

This API returns the image URL for each requested Diagnosis ID. The image URL can then be used to access the associated X-ray or CT scan image.

Examples (Click on the arrow to expand)

The following example shows how to use this API.

Example: Get the URLs of two images in the Montreal image dataset

HTTP URL: https://api.c3.ai/covid/api/1/diagnosis/getimageurls

Request JSON:

{
  "ids": [
    "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
    "1-s2.0-S1684118220300682-main.pdf-003-b1.png"
  ]
}



Response JSON:

{
  "type": "ObjMapp",
  "value": {
    "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg": {
      "type": "string",
      "value": "https://..."
    },
    "1-s2.0-S1684118220300682-main.pdf-003-b1.png": {
      "type": "string",
      "value": "https://..."
    }
  }
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
ids
Array of strings

List of diagnostic image identifiers

Responses

200

OK. The request has succeeded.

Response Schema: application/json
type
string
value
object

Map from diagnostic image identifiers to image locations

post /api/1/diagnosis/getimageurls
https://api.c3.ai/covid/api/1/diagnosis/getimageurls

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "ids":
    [
    ]
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "type": "string",
  • "value": { }
}

DiagnosisDetail

DiagnosisDetail stores detailed clinical data (e.g. lab tests, pre-existing conditions, symptoms) about individual patients in key-value format. DiagnosisDetail holds specific types of information depending on the source dataset and availability of data for different patients. For example, the date of testing and data on presence of pre-existing conditions may be available for some patients, while lab tests such as blood oxygen level may be available for others.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
source string Data source for the patient records. Allowed values: Montreal (University of Montreal), Braid (Carbon Health & Braid Health).
parent Diagnosis C3.ai Type Diagnosis representing other data related to this patient.
key string The data type being stored. Examples include date, pO2_saturation, and asthma.
value string The value for the associated key. Examples include February 6, 2020, 91.0, and True.

Examples (Click on the arrows to expand)

The following example shows how to use this API.

Example: Fetch details associated with a specific diagnosis

HTTP URL: https://api.c3.ai/covid/api/1/diagnosisdetail/fetch

Request JSON:

{
    "spec": {
        "filter": "parent.id == '01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg'"
    }
}



Response JSON:

{
  "objs": [
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "date",
      "value": "2020",
      "source": "Montreal",
      "id": "date_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "folder",
      "value": "images",
      "source": "Montreal",
      "id": "folder_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "hospital",
      "value": "Italy",
      "source": "Montreal",
      "id": "hospital_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "modality",
      "value": "X-ray",
      "source": "Montreal",
      "id": "modality_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "offset",
      "value": "5.0",
      "source": "Montreal",
      "id": "offset_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "other_notes",
      "value": "Credit to Izzo Andrea, D'Aversa Lucia, Ceremonial Giuseppe, Mazzella Giuseppe, Pergoli Pericle, Faiola Eugenio Leone, Di Pastena Francesca",
      "source": "Montreal",
      "id": "other_notes_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "pO2_saturation",
      "value": "70.0",
      "source": "Montreal",
      "id": "pO2_saturation_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "sex",
      "value": "F",
      "source": "Montreal",
      "id": "sex_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "url",
      "value": "https://www.sirm.org/2020/03/10/covid-19-caso-26/",
      "source": "Montreal",
      "id": "url_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    },
    {
      "parent": {
        "id": "01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg"
      },
      "key": "view",
      "value": "PA",
      "source": "Montreal",
      "id": "view_01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-04-27T12:35:18Z",
        "createdBy": "santiago.lopez@c3iot.com",
        "updated": "2020-04-27T12:35:18Z",
        "updatedBy": "santiago.lopez@c3iot.com",
        "timestamp": "2020-04-27T12:35:18Z",
        "fetchInclude": "[]",
        "fetchType": "DiagnosisDetail"
      },
      "version": 1
    }
  ],
  "count": 10,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/diagnosisdetail/fetch
https://api.c3.ai/covid/api/1/diagnosisdetail/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

VaccineCoverage

VaccineCoverage stores historical vaccination rates for various demographic groups in US counties and states, based on data from the US Centers for Disease Control and Prevention (CDC).

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
location OutbreakLocation C3.ai Type OutbreakLocation affiliated with this vaccine coverage data.
vaxView string CDC VaxView source of the data. Allowed values: Child, School, Teenager, Adult, Influenza.
year int Year the vaccination rate was estimated.
timestamp datetime Year the vaccination rate was estimated, as a datetime.
value double Estimated percent of demographic group that is vaccinated.
lowerLimit double Lower limit of 95% confidence interval on value.
upperLimit double Upper limit of 95% confidence interval on value.
confidenceInterval double Size of confidence interval on value, i.e., there is a 95% probability that the demographic group's true vaccination rate falls within the value minus this interval and the value plus this interval.
target double Target vaccination rate for this population group.
sampleSize int Number of individuals in the sample used to estimate the population vaccination rate.
vaccineDetails string Details about the vaccine, e.g. >=3 doses HPV Vaccination, Tetanus (Td or Tdap) Vaccination, or >=2 doses MMR Vaccination.
demographicClass string Characteristic by which the population was divided into demographic groups, e.g. Poverty, Race/Ethnicity, or Urbanicity.
demographicClassDetails string Details about the demographic group surveyed, e.g. Ages 13-17, All kindergartners, or Hispanic.
totalPopulation int Total population of the demographic group at the time of survey.
surveyType string Type of survey conducted, e.g. Stratified 2-stage cluster sample or Census.
percentSurveyed double Percentage of the demographic group surveyed to determine vaccination rate estimate.
kindergartenPopulation int Total population of kindergarten students at the time of survey.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch vaccination rates of >=1 dose MenACWY vaccine in New York, United States among 13-17 year-olds

HTTP URL: https://api.c3.ai/covid/api/1/vaccinecoverage/fetch

Request JSON:

{
  "spec": {
    "filter": "demographicClassDetails == '13-17 Years' && contains(vaccineDetails, '>=1 dose MenACWY') && location == 'NewYork_UnitedStates'"
  }
}



Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2016,
      "timestamp": "2016-01-01T00:00:00Z",
      "value": 89.2,
      "lowerLimit": 86.0,
      "upperLimit": 91.8,
      "sampleSize": 655,
      "confidenceInterval": 2.9,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "1abde61b-a5c7-4ac2-a8b8-eed8cbb218a3",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2014,
      "timestamp": "2014-01-01T00:00:00Z",
      "value": 79.6,
      "lowerLimit": 75.1,
      "upperLimit": 83.5,
      "sampleSize": 594,
      "confidenceInterval": 4.2,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "1e393b54-204b-4255-8fde-751a6d8f65cc",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2009,
      "timestamp": "2009-01-01T00:00:00Z",
      "value": 62.9,
      "lowerLimit": 57.7,
      "upperLimit": 67.8,
      "sampleSize": 539,
      "confidenceInterval": 5.0,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "5b8fa5bb-71ab-429a-b305-d832315770e9",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2011,
      "timestamp": "2011-01-01T00:00:00Z",
      "value": 74.9,
      "lowerLimit": 70.9,
      "upperLimit": 78.5,
      "sampleSize": 840,
      "confidenceInterval": 3.8,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "9092dfef-7488-4695-845a-c369412be903",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2012,
      "timestamp": "2012-01-01T00:00:00Z",
      "value": 78.5,
      "lowerLimit": 74.1,
      "upperLimit": 82.3,
      "sampleSize": 627,
      "confidenceInterval": 4.1,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "b16c14fe-89e5-461e-9933-9c2ffa06313b",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2010,
      "timestamp": "2010-01-01T00:00:00Z",
      "value": 71.2,
      "lowerLimit": 66.5,
      "upperLimit": 75.4,
      "sampleSize": 701,
      "confidenceInterval": 4.5,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "bec1260f-e3f5-410e-8ec1-60d38a65ea6b",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2008,
      "timestamp": "2008-01-01T00:00:00Z",
      "value": 56.0,
      "lowerLimit": 51.1,
      "upperLimit": 60.8,
      "sampleSize": 600,
      "confidenceInterval": 4.8,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "c4c44ae6-05f4-4892-9a74-7b3f886ca625",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2015,
      "timestamp": "2015-01-01T00:00:00Z",
      "value": 86.2,
      "lowerLimit": 82.9,
      "upperLimit": 89.0,
      "sampleSize": 665,
      "confidenceInterval": 3.1,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "cf2e5495-3dc9-45c3-ba74-dfe3453706fe",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2018,
      "timestamp": "2018-01-01T00:00:00Z",
      "value": 94.9,
      "lowerLimit": 92.0,
      "upperLimit": 96.8,
      "sampleSize": 535,
      "confidenceInterval": 2.3,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "e982d16c-7700-42a2-b4c4-0b3c6ff5ca64",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2017,
      "timestamp": "2017-01-01T00:00:00Z",
      "value": 89.3,
      "lowerLimit": 85.9,
      "upperLimit": 91.9,
      "sampleSize": 651,
      "confidenceInterval": 3.0,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "f6385fa2-af92-42e1-a630-660370b46ac7",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "NewYork_UnitedStates"
      },
      "vaxView": "Teenager",
      "year": 2013,
      "timestamp": "2013-01-01T00:00:00Z",
      "value": 83.3,
      "lowerLimit": 79.7,
      "upperLimit": 86.4,
      "sampleSize": 710,
      "confidenceInterval": 3.4,
      "vaccineDetails": ">=1 dose MenACWY Vaccination ",
      "demographicClass": " Age",
      "demographicClassDetails": "13-17 Years",
      "id": "fe0b26f6-5ee2-46d7-b94c-969ec0781684",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:28:22Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:28:22Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:29:31Z",
        "sourceFile": "TeenagerMeningococcalVaccination-2008-2018_NORMALIZED_FORMATTED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    }
  ],
  "count": 11,
  "hasMore": false
}
Example 2: Fetch influenza vaccination rates in California, United States by race/ethnicity in 2018

HTTP URL: https://api.c3.ai/covid/api/1/vaccinecoverage/fetch

Request JSON:

{
  "spec": {
    "filter": "vaxView == 'Influenza' && contains(vaccineDetails, 'General Population') && location == 'California_UnitedStates' && contains(demographicClass, 'Race/ethnicity') && year == 2018"
  }
}



Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "California_UnitedStates"
      },
      "vaxView": "Influenza",
      "year": 2018,
      "timestamp": "2018-01-01T00:00:00Z",
      "value": 44.6,
      "lowerLimit": 41.8,
      "upperLimit": 47.4,
      "sampleSize": 3200,
      "confidenceInterval": 2.8,
      "vaccineDetails": "Influenza vaccination (General Population) ",
      "demographicClass": " Race/ethnicity",
      "demographicClassDetails": "Hispanic",
      "target": 70.0,
      "id": "54effd25-3f85-4138-a6aa-b635949093e7",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:27:46Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:27:46Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:27:58Z",
        "sourceFile": "InfluenzaVaccine-2010-2019-GeneralPopulation_FLATTENED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "California_UnitedStates"
      },
      "vaxView": "Influenza",
      "year": 2018,
      "timestamp": "2018-01-01T00:00:00Z",
      "value": 40.8,
      "lowerLimit": 32.6,
      "upperLimit": 49.0,
      "sampleSize": 500,
      "confidenceInterval": 8.2,
      "vaccineDetails": "Influenza vaccination (General Population) ",
      "demographicClass": " Race/ethnicity",
      "demographicClassDetails": "Black only, non-Hispanic",
      "target": 70.0,
      "id": "6b3d8a3e-e296-4a51-a878-cf18b5974d7c",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:27:46Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:27:46Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:27:58Z",
        "sourceFile": "InfluenzaVaccine-2010-2019-GeneralPopulation_FLATTENED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "California_UnitedStates"
      },
      "vaxView": "Influenza",
      "year": 2018,
      "timestamp": "2018-01-01T00:00:00Z",
      "value": 52.3,
      "lowerLimit": 49.9,
      "upperLimit": 54.7,
      "sampleSize": 4602,
      "confidenceInterval": 2.4,
      "vaccineDetails": "Influenza vaccination (General Population) ",
      "demographicClass": " Race/ethnicity",
      "demographicClassDetails": "White only, non-Hispanic",
      "target": 70.0,
      "id": "860bb1b6-95c1-4e34-8f32-318af2b9391a",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:27:46Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:27:46Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:27:58Z",
        "sourceFile": "InfluenzaVaccine-2010-2019-GeneralPopulation_FLATTENED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    },
    {
      "location": {
        "id": "California_UnitedStates"
      },
      "vaxView": "Influenza",
      "year": 2018,
      "timestamp": "2018-01-01T00:00:00Z",
      "value": 45.0,
      "lowerLimit": 40.6,
      "upperLimit": 49.4,
      "sampleSize": 1332,
      "confidenceInterval": 4.4,
      "vaccineDetails": "Influenza vaccination (General Population) ",
      "demographicClass": " Race/ethnicity",
      "demographicClassDetails": "Other or multiple races, non-Hispanic",
      "target": 70.0,
      "id": "a5971830-49d9-46cc-af62-1bb4322c5b00",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T23:27:46Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:27:46Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T23:27:58Z",
        "sourceFile": "InfluenzaVaccine-2010-2019-GeneralPopulation_FLATTENED.csv",
        "fetchInclude": "[]",
        "fetchType": "VaccineCoverage"
      },
      "version": 1
    }
  ],
  "count": 4,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/vaccinecoverage/fetch
https://api.c3.ai/covid/api/1/vaccinecoverage/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

ClinicalTrial

ClinicalTrial stores metadata for all clinical trials related to COVID-19 being conducted worldwide.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string Trial ID supplied by clinical trials registry.
url string Web link to entry in registry database.
pdf string Web link to summary of trial.
location OutbreakLocation C3.ai Type OutbreakLocation where trial is taking place.
startDate datetime Start date of the trial.
endDate datetime Planned or actual completion date of the trial.
trialStatus string Current status of the trial, e.g. recruiting, planned, or completed.
design string Randomization or other technical study criteria, e.g. Randomised or Single-arm.
blinding string Concealing or blinding of group allocation from one or more participants in the trial, e.g. Single, Double, or Open-Label.
arms int Number of treatment groups under observation.
covid19Status string Scope of participant conditions specified by study, e.g. Healthy (Exposed), Confirmed, or Resolved.
severity string Relative condition of participants under study, e.g. Non-severe, Severe, or Mixed.
patientSetting string Treatment environment of trial participants, e.g. Hospital, ICU, or Outpatient.
outcome string The result of a treatment or intervention used to measure efficacy.
treatmentType string The drug or therapy under evaluation.
size int Number of participants.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch trials of azidothymidine and hydroxychloroquine treatments that started in 2020

HTTP URL: https://api.c3.ai/covid/api/1/clinicaltrial/fetch

Request JSON:

{
  "spec": {
    "filter": "(contains(treatmentType,'HCQ') || contains(treatmentType,'AZT')) && startDate >= '2020-01-01'"
  }
}



Response JSON:

{
  "objs": [
    {
      "url": "https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-000890-25/FR/",
      "pdf": "https://www.sciencedirect.com/science/article/pii/S0924857920300996",
      "country": "France",
      "location": {
        "id": "Paris_France"
      },
      "startDate": "2020-03-01T00:00:00Z",
      "trialStatus": "Completed w. Results",
      "design": "Unspecified",
      "blinding": "Unspecified",
      "covid19Status": "Confirmed",
      "patientSetting": "Hospital",
      "outcome": "Mortality, Hospitalization, Viral Load or Clearance, Fever, Respiratory Rate",
      "treatmentType": "HCQ",
      "id": "2020-000890-25_Paris_France",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T04:29:06Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T04:29:06Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T04:29:26Z",
        "sourceFile": "LancetClincalTrialsTracker_05_28_2020.csv",
        "fetchInclude": "[]",
        "fetchType": "ClinicalTrial"
      },
      "version": 1
    },
    {
      "url": "https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-000982-18/NO/",
      "country": "Norway",
      "location": {
        "id": "Oslo_Norway"
      },
      "startDate": "2020-03-01T00:00:00Z",
      "trialStatus": "Recruiting",
      "design": "Randomised",
      "blinding": "Open-Label",
      "arms": 3,
      "covid19Status": "Confirmed",
      "severity": "Severe",
      "patientSetting": "Hospital, ICU",
      "outcome": "Mortality, ICU Admission, Invasive Mechanical Ventilation or ECMO, Serious or Seconday Infections, Organ Failure or Dysfunction (SOFA), Adverse Events, Treatment-emergent Adverse Events, eGFR, Quality of Life",
      "treatmentType": "HCQ, Remdesivir",
      "id": "2020-000982-18_Oslo_Norway",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T04:29:06Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T04:29:06Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T04:29:26Z",
        "sourceFile": "LancetClincalTrialsTracker_05_28_2020.csv",
        "fetchInclude": "[]",
        "fetchType": "ClinicalTrial"
      },
      "version": 1
    },
    ...
  ],
  "count": 417,
  "hasMore": false
}
Example 2: Fetch completed trials involving ICU patients

HTTP URL: https://api.c3.ai/covid/api/1/clinicaltrial/fetch

Request JSON:

{
  "spec": {
    "filter": "(contains(patientSetting, 'ICU') && contains(trialStatus, 'Complete'))"
  }
}



Response JSON:

{
  "objs": [
    {
      "url": "http://en.irct.ir/trial/47197",
      "country": "Iran",
      "location": {
        "id": "Tehran_Iran"
      },
      "startDate": "2020-05-01T00:00:00Z",
      "endDate": "2019-08-01T00:00:00Z",
      "trialStatus": "Completed",
      "design": "Randomised",
      "blinding": "Double",
      "arms": 2,
      "covid19Status": "Confirmed",
      "severity": "Moderate/Severe",
      "patientSetting": "ICU",
      "outcome": "Blood Gas",
      "treatmentType": "Plasma based therapy",
      "size": 30,
      "id": "IRCT20091012002582N21_Tehran_Iran",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T04:29:06Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T04:29:06Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T04:29:26Z",
        "sourceFile": "LancetClincalTrialsTracker_05_28_2020.csv",
        "fetchInclude": "[]",
        "fetchType": "ClinicalTrial"
      },
      "version": 1
    },
    {
      "url": "http://en.irct.ir/trial/46623",
      "country": "Iran",
      "location": {
        "id": "Tehran_Iran"
      },
      "startDate": "2020-03-01T00:00:00Z",
      "endDate": "2020-04-01T00:00:00Z",
      "trialStatus": "Completed",
      "design": "Randomised",
      "blinding": "Double",
      "arms": 2,
      "covid19Status": "Confirmed",
      "severity": "Unclear",
      "patientSetting": "ICU",
      "outcome": "Mortality, Pneumonia or ARDS, C-Reactive Protein, Procalcitonin, CD4, CD8, Radiographic Findings",
      "treatmentType": "Stem cells",
      "size": 10,
      "id": "IRCT20140528017891N8_Tehran_Iran",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-29T04:29:06Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T04:29:06Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-05-29T04:29:26Z",
        "sourceFile": "LancetClincalTrialsTracker_05_28_2020.csv",
        "fetchInclude": "[]",
        "fetchType": "ClinicalTrial"
      },
      "version": 1
    },
    ...
  ],
  "count": 10,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/clinicaltrial/fetch
https://api.c3.ai/covid/api/1/clinicaltrial/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

LocationPolicySummary

LocationPolicySummary stores COVID-19 social distancing and health policies and regulations enacted by US states.

The fetch API provides current policy data, while the allversionsforpolicy API provides both historical and current policy data.

The Policy type is deprecated. To query policy data from Kaiser Family Foundation, please use LocationPolicySummary instead.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
location OutbreakLocation C3.ai Type OutbreakLocation where the policy was enacted.
easingOrder string "Yes" if the location is easing their social distancing measures, "No" otherwise.
stayAtHome string Description of the latest status of the stay-at-home order.
mandatoryQuarantine string Description of status of mandatory quarantine for travelers.
nonEssentialBusiness string Description of restrictions on non-essential businesses.
largeGatherings string Description of restrictions on large gatherings.
schoolClosure string Description of school closures or restrictions.
restaurantLimit string Description of restrictions on restaurants.
PrimaryElectionPostponement string Description of postponement or cancellation of primary elections.
emergencyDeclaration string "Yes" if a state of emergency was declared, "No" otherwise.
waiveTreatmentCost string Description of policies regarding cost sharing for COVID-19 treatment.
freeVaccine string Description of policies requiring free cost COVID-19 vaccines when available.
waiverOfPriorAuthorizationRequirements string Description of policies requiring a waiver of prior authorization requirements. May be superseded by the federal Families First Coronavirus Response Act.
prescriptionRefill string Description of policies regrading early prescriptions refills.
premiumPaymentGracePeriod string Description of policies regarding premium payment grace periods.
marketplaceSpecialEnrollmentPeriod string "Yes" if the special enrollment period for the state's insurance marketplace extended, "No" otherwise.
section1135Waiver string Description of approval status of the Section 1135 waiver.
paidSickLeaves string Description of status of paid sick leave policies adding to federal emergency leave.
expandsAccesstoTelehealthServices string "Yes" if expanded access to Tele-health services are issued, "No" otherwise.
lastSavedTimestamp datetime Datetime of last update for this version.
version int Incrementing version ID for all policies.
versionDate datetime Date of the policy version.
numSavedVersions int Total number of versions of this policy available with allversionsforpolicy.
savedVersion int Incrementing version ID for this policy.

Examples (Click on the arrows to expand)

The following example shows how to use this API.

Example: Fetch all policies in Pennsylvania, US

HTTP URL: https://api.c3.ai/covid/api/1/locationpolicysummary/fetch

Request JSON:

{
  "spec" : {
    "filter": "location == 'Pennsylvania_UnitedStates'",
    "limit": -1
  }
}

Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Pennsylvania_UnitedStates"
      },
      "versionDate": "2020-05-29T00:00:00Z",
      "easingOrder": "Yes",
      "stayAtHome": "Lifted",
      "mandatoryQuarantine": "No Action",
      "nonEssentialBusiness": "Some Non-Essential Businesses Permitted to Reopen",
      "largeGatherings": "Expanded to New Limit of 25",
      "schoolClosure": "Closed for School Year",
      "restaurantLimit": "Closed Except for Takeout/Delivery",
      "PrimaryElectionPostponement": "No",
      "emergencyDeclaration": "Yes",
      "waiveTreatmentCost": "No Action",
      "freeVaccine": "No Action",
      "waiverOfPriorAuthorizationRequirements": "For COVID-19 Testing",
      "prescriptionRefill": "No Action",
      "premiumPaymentGracePeriod": "No Action",
      "marketplaceSpecialEnrollmentPeriod": "No",
      "section1135Waiver": "Approved",
      "paidSickLeaves": "No Action",
      "expandsAccesstoTelehealthServices": "No",
      "id": "Pennsylvania_UnitedStates_Policy",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-05-05T04:57:50Z",
        "createdBy": "dataloader",
        "updated": "2020-05-29T23:15:11Z",
        "updatedBy": "elliot.kirk@c3iot.com",
        "timestamp": "2020-05-29T23:15:11Z",
        "sourceFile": "StateSocialDistancingActionsMay29_cleaned.csv",
        "fetchInclude": "[]",
        "fetchType": "Policy"
      },
      "version": 10,
      "lastSavedTimestamp": "2020-05-29T23:15:11Z",
      "numSavedVersions": 4,
      "savedVersion": 4
    }
  ],
  "count": 1,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/locationpolicysummary/fetch
https://api.c3.ai/covid/api/1/locationpolicysummary/fetch

Request samples

Content type
application/json
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
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{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

AllVersionsForPolicy

This API returns all historical versions of a policy, in addition to the current policy version returned by fetch.

Examples (Click on the arrows to expand)

The following example shows how to use this API.

Example: Retrieve all versions of policies in California

HTTP URL: https://api.c3.ai/covid/api/1/locationpolicysummary/allversionsforpolicy

Request JSON:

{
    "this": {
        "id": "California_UnitedStates_Policy"
    }
}

Response JSON:

[
  {
    "location": {
      "id": "California_UnitedStates"
    },
    "versionDate": "2020-05-29T00:00:00Z",
    "easingOrder": "Yes",
    "stayAtHome": "Statewide",
    "mandatoryQuarantine": "No Action",
    "nonEssentialBusiness": "Some Non-Essential Businesses Permitted to Reopen with Reduced Capacity",
    "largeGatherings": "All Gatherings Prohibited",
    "schoolClosure": "Recommended Closure for School Year",
    "restaurantLimit": "Closed Except for Takeout/Delivery",
    "PrimaryElectionPostponement": "No",
    "emergencyDeclaration": "Yes",
    "waiveTreatmentCost": "No Action",
    "freeVaccine": "No Action",
    "waiverOfPriorAuthorizationRequirements": "No Action",
    "prescriptionRefill": "State Requires",
    "premiumPaymentGracePeriod": "No Action",
    "marketplaceSpecialEnrollmentPeriod": "Active",
    "section1135Waiver": "Approved",
    "paidSickLeaves": "Enacted",
    "expandsAccesstoTelehealthServices": "Yes",
    "id": "California_UnitedStates_Policy",
    "meta": {
      "tenantTagId": 4,
      "tenant": "covid",
      "tag": "prod",
      "created": "2020-05-05T04:57:50Z",
      "createdBy": "dataloader",
      "updated": "2020-05-29T23:15:10Z",
      "updatedBy": "elliot.kirk@c3iot.com",
      "timestamp": "2020-05-29T23:15:10Z",
      "sourceFile": "StateSocialDistancingActionsMay29_cleaned.csv",
      "fetchInclude": "[this,versionEdits]",
      "fetchType": "Policy"
    },
    "version": 10,
    "lastSavedTimestamp": "2020-05-29T23:15:10Z",
    "numSavedVersions": 4,
    "savedVersion": 4
  },
  {
    "location": {
      "id": "California_UnitedStates"
    },
    "easingOrder": "No",
    "stayAtHome": "Statewide",
    "mandatoryQuarantine": "No Action",
    "nonEssentialBusiness": "All Non-Essential Businesses Closed",
    "largeGatherings": "All Gatherings Prohibited",
    "schoolClosure": "Recommended Closure for School Year",
    "restaurantLimit": "Closed Except for Takeout/Delivery",
    "PrimaryElectionPostponement": "No",
    "emergencyDeclaration": "Yes",
    "waiveTreatmentCost": "No Action",
    "freeVaccine": "No Action",
    "waiverOfPriorAuthorizationRequirements": "No Action",
    "prescriptionRefill": "State Requires",
    "premiumPaymentGracePeriod": "No Action",
    "marketplaceSpecialEnrollmentPeriod": "Yes",
    "section1135Waiver": "Approved",
    "paidSickLeaves": "Enacted",
    "id": "California_UnitedStates_Policy",
    "meta": {
      "tenantTagId": 4,
      "tenant": "covid",
      "tag": "prod",
      "created": "2020-05-05T04:57:50Z",
      "createdBy": "dataloader",
      "updated": "2020-05-29T19:39:11Z",
      "updatedBy": "elliot.kirk@c3iot.com",
      "timestamp": "2020-05-29T19:39:11Z",
      "sourceFile": "StateSocialDistancingPolicies_05012020_cleaned.csv",
      "fetchInclude": "[this,versionEdits]",
      "fetchType": "Policy"
    },
    "version": 7,
    "lastSavedTimestamp": "2020-05-29T19:39:11Z",
    "numSavedVersions": 3,
    "savedVersion": 3
  },
  {
    "location": {
      "id": "California_UnitedStates"
    },
    "easingOrder": "No",
    "stayAtHome": "Statewide",
    "mandatoryQuarantine": "No Action",
    "nonEssentialBusiness": "All Non-Essential Businesses Closed",
    "largeGatherings": "All Gatherings Prohibited",
    "schoolClosure": "Recommended Closure for School Year",
    "restaurantLimit": "Closed Except for Takeout/Delivery",
    "emergencyDeclaration": "Yes",
    "waiveTreatmentCost": "No Action",
    "freeVaccine": "No Action",
    "waiverOfPriorAuthorizationRequirements": "No Action",
    "prescriptionRefill": "State Requires",
    "premiumPaymentGracePeriod": "No Action",
    "marketplaceSpecialEnrollmentPeriod": "Yes",
    "section1135Waiver": "Approved",
    "paidSickLeaves": "Enacted",
    "id": "California_UnitedStates_Policy",
    "meta": {
      "tenantTagId": 4,
      "tenant": "covid",
      "tag": "prod",
      "created": "2020-05-05T04:57:50Z",
      "createdBy": "dataloader",
      "updated": "2020-05-05T04:59:27Z",
      "updatedBy": "ethan.ho@c3iot.com",
      "timestamp": "2020-05-05T04:59:27Z",
      "sourceFile": "StateSocialDistancingPolicies_05012020_cleaned.csv",
      "fetchInclude": "[this,versionEdits]",
      "fetchType": "Policy"
    },
    "version": 5,
    "lastSavedTimestamp": "2020-05-05T04:59:27Z",
    "numSavedVersions": 2,
    "savedVersion": 2
  },
  {
    "location": {
      "id": "California_UnitedStates"
    },
    "easingOrder": "No",
    "stayAtHome": "Statewide",
    "mandatoryQuarantine": "No Action",
    "nonEssentialBusiness": "All Non-Essential Businesses",
    "largeGatherings": "All Gatherings Prohibited",
    "schoolClosure": "Recommended Closure for School Year",
    "restaurantLimit": "Closed Except for Takeout/Delivery",
    "emergencyDeclaration": "Yes",
    "waiveTreatmentCost": "No Action",
    "freeVaccine": "No Action",
    "waiverOfPriorAuthorizationRequirements": "No Action",
    "prescriptionRefill": "State Requires",
    "premiumPaymentGracePeriod": "No Action",
    "marketplaceSpecialEnrollmentPeriod": "Yes",
    "section1135Waiver": "Approved",
    "paidSickLeaves": "Enacted",
    "id": "California_UnitedStates_Policy",
    "meta": {
      "tenantTagId": 4,
      "tenant": "covid",
      "tag": "prod",
      "created": "2020-05-05T04:57:50Z",
      "createdBy": "dataloader",
      "updated": "2020-05-05T04:58:29Z",
      "updatedBy": "ethan.ho@c3iot.com",
      "timestamp": "2020-05-05T04:58:29Z",
      "sourceFile": "StateSocialDistancingMeasures_cleaned.csv",
      "fetchInclude": "[this,versionEdits]",
      "fetchType": "Policy"
    },
    "version": 3,
    "lastSavedTimestamp": "2020-05-05T04:58:29Z",
    "numSavedVersions": 1,
    "savedVersion": 1
  }
]
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
this
object

Container of query evaluation attributes

id
string

A policy ID

Responses

200

OK. The request has succeeded.

Response Schema: application/json
Array
LocationPolicySummary
object

A LocationPolicySummary object. Click open for more details.

...
object

More LocationPolicySummary objects.

post /api/1/locationpolicysummary/allversionsforpolicy
https://api.c3.ai/covid/api/1/locationpolicysummary/allversionsforpolicy

Request samples

Content type
application/json
Copy
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{
  • "this":
    {
    }
}

Response samples

Content type
application/json
Copy
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[
  • {
    }
]

PolicyDetail

PolicyDetail stores country-level policy responses to COVID-19 including:

  • Financial sector policies (from The World Bank: Finance Related Policy Responses to COVID-19),
  • Containment and closure, economic, and health system policies (from University of Oxford: Coronavirus Government Response Tracker, OxCGRT), and
  • Policies in South Korea (from Data Science for COVID-19: South Korea).

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
location OutbreakLocation C3.ai Type OutbreakLocation where the policy was enacted.
startDate datetime Date on which a specific policy took effect.
endDate datetime Date on which a specific policy ended.
entryDate datetime Date on which a specific policy was recorded (only for World Bank data).
policyType string Type, classification, or indicator of a policy. Allowed values for OxCGRT data: C1-C8, E1-E4, H1-H5. Allowed values for World Bank data: Banking sector, Financial Markets/NBFI, Insolvency, Liquidity/funding, Payments systems, Other. Allowed values for South Korea data: Heath, Social, Technology, Immigration, Transformation, Alert, Administrative, Education.
policySubType string Sub-type or sub-classification of a policy, only applicable to World Bank data. Allowed values: Crisis management, Integrity, Operational continuity, Prudential, Support borrowers, Market functioning, NBFI, Public debt management, Asset purchases, Liquidity (incl FX)/ELA, Policy rate, Cash/Check usage restrictions, Consumer protection, Digital payments, Relaxation compliance.
name string Name of the policy; examples are School closing and Testing policy.
value int Value of a specific policy (only applicable to OxCGRT data). 0 = no restrictions; 1 = restrictions on very large gatherings (the limit is above 1000 people); 2 = restrictions on gatherings between 101-1000 people; 3 = restrictions on gatherings between 11-100 people; 4 = restrictions on gatherings of 10 people or less.
flag int Whether the policy is targets at a specific region or applies to the whole country (only applicable to OxCGRT). 0 = targeted at specific geographical region; 1 = applies to the whole country.
details string Additional details or notes respective to a specific policy.
origin string Source of the policy data. Allowed values are World Bank Finance, South Korea, University of Oxford.

Examples (Click on the arrows to expand)

The following examples show how to use this API.

Example 1: Fetch all school-closing policies that restrict gatherings between 11-100 people from OxCGRT dataset

HTTP URL: https://api.c3.ai/covid/api/1/policydetail/fetch

Request JSON:

{
  "spec" : {
    "filter": "contains(lowerCase(name),'school closing') && value == 3 && origin == 'University of Oxford'",
    "limit": -1
  }
}

Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-06-07T00:00:00Z",
      "policyType": "C1",
      "name": " School closing",
      "value": 3,
      "flag": 1,
      "details": "[COVID-19: Despite 3-Month Extension of School Leave Remote Learning Continues  Following the decision of the Emergency Committee to extend school closures for the next three months, the Afghan Ministry of Education (MoE) announced that remote education programmes will continue.  The MoE in its statement said that the Committee’s decision to prevent the spread of the virus within schools, private institutes and public teacher training centres has been noted.  The decision to start face-to-face training in schools would depend on the number of Coronavirus cases and whether they were declining within the community, they said.  Remote learning will continue for the time being. Start Alternative options for educational services  The MoE said that alternatives to formal education services, limited gatherings with health and preventive measures and online courses, would be formally launched soon.]  https://web.archive.org/web/20200608190520/http://reporterly.net/live/newsfeed/sunday-june-7-2020/covid-19-despite-3-month-extension-of-school-leave-remote-learning-continues/",
      "origin": "University of Oxford",
      "id": "Afghanistan_00e495f4b1dce8012dc388df52858596",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-12T16:10:24Z",
        "createdBy": "dataloader",
        "updated": "2020-06-12T16:10:24Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-12T16:10:26Z",
        "sourceFile": "OxCGRT_PolicyAll_latest.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-03-14T00:00:00Z",
      "policyType": "C1",
      "name": " School closing",
      "value": 3,
      "flag": 1,
      "details": "On March 14, 2020, the Afghan government announced the closure of all schools and universities for a month. The academic year in most of the country beings on March 21st, but this postponed the commencement until mid-April. https://web.archive.org/web/20200402185825/http://www.afghanistantimes.af/afghanistan-shuts-schools-bans-public-events-amid-coronavirus-fears/",
      "origin": "University of Oxford",
      "id": "Afghanistan_08bb900a1c5cfdddf64ac643627c89ec",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-12T16:10:24Z",
        "createdBy": "dataloader",
        "updated": "2020-06-12T16:10:24Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-12T16:10:26Z",
        "sourceFile": "OxCGRT_PolicyAll_latest.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-05-22T00:00:00Z",
      "policyType": "C1",
      "name": " School closing",
      "value": 3,
      "flag": 1,
      "details": "\"UNICEF Afghanistan Representative, Dr. Aboubacar Kampo, told Pajhwok Afghan News in an exclusive interview that this is a global pandemic impacting several countries, and currently, some 450 million children are out of school across the South Asia region.    Dr. Aboubacar Kampo said Covid-19 times in Afghanistan worsens the situation for children. Continued conflict and insecurity over the past four decades have impacted on development, resulting to lost livelihoods and increased poverty, especially amongst vulnerable families, with children being the most affected.    He further added there were over 600,000 malnourished children in Afghanistan while over three million children were out of school across the country, even before Covid.    Kampo added that UNICEF was supporting the Ministry of Education on alternative solutions to learning during the school closure and supporting the government to make sure handwashing facilities existed in schools when they do reopen.    UNICEF Afghanistan Representative said the provision of sanitation and hygiene facilities would be supported for children both in schools and community-based education centres. He said it was not known when schools would reopen formally due to the evolving situation. “If it comes in winter season, then we need to get prepared the schools and the kids as well.”    On the other hand, the Ministry of Education (MoE) says they have been working on alternative options for the education system in the country.    Nooria Nehzat, spokeswoman for the MoE, told Pajhwok they conducted short term courses in the open sky where social distancing was also observed nation-wide.    She said the MoE had signed a contract of 3,000 textbooks, videos materials with the education system of Turkey to keep alive the teaching process for primary students in the country.    Meanwhile, Dr. Mirwais Balkhi, the minister of education, says they have been working on a comprehensive education plan which would be implemented in Kabul and in the rest of provinces in near future.\"  https://web.archive.org/web/20200604235818/https://www.pajhwok.com/en/2020/05/06/covid-19-pandemic-shouldn%E2%80%99t-hamper-education-afghanistan-unicef",
      "origin": "University of Oxford",
      "id": "Afghanistan_17c877f89f6b0f34270499298244982a",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-06T15:26:45Z",
        "createdBy": "dataloader",
        "updated": "2020-06-06T15:26:45Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-06T15:26:46Z",
        "sourceFile": "OxCGRT_PolicyAll_latest.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    ...
  ],
  "count": 711,
  "hasMore": false
}
Example 2: Fetch all banking sector prudential-related policies from World Bank Finance dataset

HTTP URL: https://api.c3.ai/covid/api/1/policydetail/fetch

Request JSON:

{
  "spec" : {
    "filter": "contains(policyType, 'Banking Sector') && contains(policySubType, 'Prudential') && contains(lowerCase(origin), 'world bank')" ,
    "limit": -1
  }
}

Response JSON:

{
  "objs": [
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-04-09T00:00:00",
      "entryDate": "2020-04-17T00:00:00",
      "policyType": "Banking Sector",
      "policySubType": "Prudential",
      "details": "Increased frequency of Financial Stability Committee meetings and daily cashflow reports required from banks",
      "origin": "World Bank Finance",
      "id": "Afghanistan_697e31b366ea0b4e4e0dc6db1c83b668",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-09T03:04:17Z",
        "createdBy": "dataloader",
        "updated": "2020-06-09T03:04:17Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-09T03:04:43Z",
        "sourceFile": "covid-fci-data.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-04-09T00:00:00",
      "entryDate": "2020-04-17T00:00:00",
      "policyType": "Banking Sector",
      "policySubType": "Prudential",
      "details": "Relaxation of provisioning requirements for loans that are covered by partial credit guarantees",
      "origin": "World Bank Finance",
      "id": "Afghanistan_6c6650eec8ec6bc01d427d9d8634b378",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-09T03:04:17Z",
        "createdBy": "dataloader",
        "updated": "2020-06-09T03:04:17Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-09T03:04:43Z",
        "sourceFile": "covid-fci-data.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-04-09T00:00:00",
      "entryDate": "2020-04-17T00:00:00",
      "policyType": "Banking Sector",
      "policySubType": "Prudential",
      "details": "Suspension of on-site examinations Until June 2020.  On-site spot checks will be done as needed",
      "origin": "World Bank Finance",
      "id": "Afghanistan_77f802230be3a22e36482a2fa7f2caa4",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-09T03:04:17Z",
        "createdBy": "dataloader",
        "updated": "2020-06-09T03:04:17Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-09T03:04:43Z",
        "sourceFile": "covid-fci-data.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-04-09T00:00:00",
      "entryDate": "2020-04-17T00:00:00",
      "policyType": "Banking Sector",
      "policySubType": "Prudential",
      "details": "Flexibility in application of supervision rules: loan classifications frozen as of February 29 2020 until June; \nextension of overdraft debt due between March-June until October; suspension of supervisory and adminstrative fees and penalties.",
      "origin": "World Bank Finance",
      "id": "Afghanistan_7a41786ee68aa9b3ddbd03cf17095ec7",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-09T03:04:17Z",
        "createdBy": "dataloader",
        "updated": "2020-06-09T03:04:17Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-09T03:04:43Z",
        "sourceFile": "covid-fci-data.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    {
      "location": {
        "id": "Afghanistan"
      },
      "startDate": "2020-04-09T00:00:00",
      "entryDate": "2020-04-17T00:00:00",
      "policyType": "Banking Sector",
      "policySubType": "Prudential",
      "details": "IFRS 9 implementation date has been extended to June 2021",
      "origin": "World Bank Finance",
      "id": "Afghanistan_c3e88cb81ae2fc99d791017da47a25d3",
      "meta": {
        "tenantTagId": 4,
        "tenant": "covid",
        "tag": "prod",
        "created": "2020-06-09T03:04:17Z",
        "createdBy": "dataloader",
        "updated": "2020-06-09T03:04:17Z",
        "updatedBy": "dataloader",
        "timestamp": "2020-06-09T03:04:43Z",
        "sourceFile": "covid-fci-data.csv",
        "fetchInclude": "[]",
        "fetchType": "PolicyDetail"
      },
      "version": 1
    },
    ...
  ],
  "count": 664,
  "hasMore": false
}
Example 3: Fetch all health-related policies in South Korea from Data Science for COVID-19: South Korea Dataset

HTTP URL: https://api.c3.ai/covid/api/1/policydetail/fetch

Request JSON:

{
  "spec" : {
    "filter": "contains(policyType, 'Health') && contains(lowerCase(origin), 'korea')",
    "limit": -1
  }
}

Response JSON:

{

}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/policydetail/fetch
https://api.c3.ai/covid/api/1/policydetail/fetch

Request samples

Content type
application/json
Copy
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{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}

SurveyData

SurveyData stores COVID-19-related public opinion, demographic, and symptom prevalence data collected from COVID-19 survey responses.

Fetch

Fields

NOTE: If the field is not present in a returned object, then that field will not be returned.

Field Data type Description
id string Unique ID of the response.
location OutbreakLocation C3.ai Type OutbreakLocation (state-level) affiliated with this survey response, based on the 3-digit zip code the participant provides.
zipcodePrefix double Participant's self-reported 3-digit zip code.
annualHouseholdIncome double Participant's response to the question: What is your approximate annual household income in dollars?
birthYear2020 int Participant's response to the question: What is your year of birth?
coronavirusConcern double Participant's response to the question: On a scale from 0 to 10, how concerned are you about the coronavirus? (0: Not at All, 5: Somewhat, 10: Extremely Concerned)
coronavirusEmployment string Participant's response to the question: How has your employment status changed since January 1, 2020? The value of this field is a comma-separated string consisting one or more of the following choices:
was-full: I was employed full-time on January 1, 2020.
was-part: I was employed part-time on January 1, 2020.
was-jobless: I was unemployed on January 1, 2020.
now-full: I am now employed full-time.
now-part: I am now employed part-time.
now-jobless: I am now unemployed.
now-retired: I am now retired.
was-retired: I was retired on January 1, 2020.
was-disabled: I was disabled and unable to work on January 1, 2020.
now-disabled: I am now disabled and unable to work.
coronavirusIntent_Mask double Participant's response to the question: On a scale from 0 to 100, because of Coronavirus, do you intend to wear a face mask in public? (0: No, 50: Possibly / Unsure, 100: Yes)
coronavirusIntent_SixFeet double Participant's response to the question: On a scale from 0 to 100, are you trying to stay 6 feet away from other people, because of Coronavirus? (0: No, 50: Possibly / Unsure, 100: Yes)
coronavirusIntent_StayHome double Participant's response to the question: On a scale from 0 to 100, do you intend to stay at home as much as possible right now, because of Coronavirus? (0: No, 50: Possibly / Unsure, 100: Yes)
coronavirusIntent_WashHands double Participant's response to the question: On a scale from 0 to 100, do you intend to wash your hands more than usual, for at least 20 seconds each time? (0: No, 50: Possibly / Unsure, 100: Yes)
coronavirusLocalCommunity double Participant's response to the question: Do you know anyone in your local community who has contracted Coronavirus? If so, how many people? Please enter “0” if none.
coronavirusSupportSystem string Participant's response to the question: If you need help in the next 6 months because of the COVID-19 pandemic, who do you think is most likely to help you? The value of this field is a comma-separated string consisting one or more of the following choices:
fam-friend: Family and friends,
employer: Employer,
religious: Religious community,
local-gov: Local government,
state-gov: State government,
fedgov: Federal government,
other: Other,
no-one: No one,
local-community: Local community groups,
private-org: A private organization.
coronavirusSymptoms string Participant's response to the question: Are you personally experiencing any of the following symptoms? The value of this field is a comma-separated string consisting one or more of the following choices:
dry-cough: Dry cough,
short-breath: Shortness of breath,
diarrhea: Diarrhea,
muscle-ache: Muscle ache,
fatigue: Fatigue,
nasal: Runny nose or nasal congestion,
sore-throat: Sore throat,
lost-smell-taste: Loss of smell / taste,
fever: Fever,
headache: Headache,
nausea-vomit: Nausea and/or vomiting,
none: None.
ratioOfAdultHospitalization string Participant's response to the question: What proportion of 35 year olds who get Coronavirus will require hospitalization? Allowed values: one-in-30k, one-in-1k, three-in-ten, three-percent, thirty-percent, almost-all.
coronavirusWhenShouldReopen string Participant's response to the question: From your understanding, roughly how long should it be before restrictions on normal in-person activities are lifted in your area? Allowed values: immediate, few-days, 1-wk, 2-wk ,3-wk ,1-mo ,2-mo ,3-mo ,4-mo ,5-mo ,6-mo ,1-yr ,yr-plus.
hasCoronavirusBelief double Participant's response to the question: On a scale from 0 to 10, do you believe you currently have Coronavirus? (0: Definitely No, 5: Unlikely but Possible, 10: Yes)
coronaSimilarFlu boolean Whether the participant agrees with the statement: Coronavirus is similar to the flu: it does not kill people unless they’re old or already sick.
coronaOnlyElderly boolean Whether the participant agrees with the statement: Young people cannot contract Coronavirus, only older people can.
youngInvulnerable boolean Whether the participant agrees with the statement: Young, healthy people can contract Coronavirus but cannot be harmed by it.
elderlyMoreRisk boolean Whether the participant agrees with the statement: People of all ages can contract Coronavirus, but older people are more vulnerable to becoming severely ill.
coronaAllHospitalize boolean Whether the participant agrees with the statement: Coronavirus can require hospitalization for people of any age, even those who were otherwise healthy.
coronaKillsMost boolean Whether the participant agrees with the statement: Coronavirus will kill most people who contract it.
ethnicitySpreadsCovid boolean Whether the participant agrees with the statement: It is much more likely to get Coronavirus from people of some ethnicities than others.
allSpreadCovid boolean Whether the participant agrees with the statement: People of any background are equally at risk of spreading Coronavirus.
nonNativesSpreadCovid boolean Whether the participant agrees with the statement: People who were born overseas are more likely to spread Coronavirus.
asymptomaticSpread boolean Whether the participant agrees with the statement: People can be infected with Coronavirus and feel fine but still spread it to others.
onlySickSpread boolean Whether the participant agrees with the statement: People who get sick from Coronavirus can spread it to others, but those who feel fine cannot.
infectFromAnimal boolean Whether the participant agrees with the statement: People who contract Coronavirus generally get it from infected animals and animal products.
politicalBelief double Participant's response to the question: On a scale from very liberal to very conservative, how would you best describe your political views? (0: Very Liberal, 5: Moderate, 10: Very Conservative)
politicalParty double Participant's response to the question: On a scale from 0 to 10, in terms of politics, do you consider yourself a Democrat, independent, or Republican? (0: Strongly Democrat, 5: Independent, 10: Strongly Republican)
trumpApproval double Participant's response to the question: On a scale from 0 to 10, do you approve or disapprove of the way Donald Trump is handling his job as President? (0: Strongly Disapprove, 5: Neither Approve nor Disapprove, 10: Strongly Approve)
religiosity double Participant's response to the question: On a scale from 0 to 10, how important would you say religion is in your life? (0: Not Very Important, 5: Somewhat Important, 10: Very Important)
religion string Participant's self-reported religious belief. Allowed values:
evangelical-protestant: Evangelical Protestant,
other-protestant: Other Protestant,
catholic: Catholic,
mormon: Mormon,
orthodox: Orthodox,
jewish: Jewish,
muslim: Muslim,
buddhist: Buddhist,
hindu: Hindu,
atheist: Atheist,
agnostic: Agnostic,
something-else: Something Else,
nothing-in-particular: Nothing in Particular.
education string Participant's self-reported education background. Allowed values:
school: Some School / No Diploma,
highschool: High School Graduate,
some-college: Some College,
college: College Degree,
postgrad: Postgraduate Degree.
ethnicity string Participant's self-reported ethnicity. Allowed values:
asian: Asian,
black: Black,
hispanic-latino: Hispanic or Latino,
white: White,
other-mixed: Other/Mixed.
gender string Participant's self-reported gender. Allowed values: female, male, other.
startTime datetime Start time of the survey.

Examples (Click on the arrows to expand)

The following example shows how to use this API.

Example: Fetch the employment status of the participants who are located in California and who have a relatively strong intent to wear a mask in public because of COVID-19.

HTTP URL: https://api.c3.ai/covid/api/1/surveydata/fetch

Request JSON:

{
  "spec": {
    "filter": "location == 'California_UnitedStates' && coronavirusIntent_Mask >= 75",
    "include": "coronavirusEmployment",
    "limit": -1
  }
}



Response JSON:

{
  "objs": [
    {
      "id": "0014fa29afed18b4c3533df6d3fe3893",
      "coronavirusEmployment": "was-jobless, now-jobless",
      "meta": {
        "fetchInclude": "[coronavirusEmployment,id,version]",
        "fetchType": "SurveyData"
      },
      "version": 1
    },
    {
      "id": "003e154ae15ee3b01b61b712fbc294d5",
      "coronavirusEmployment": "was-part",
      "meta": {
        "fetchInclude": "[coronavirusEmployment,id,version]",
        "fetchType": "SurveyData"
      },
      "version": 1
    },
    {
      "id": "00b47a4ea2fab1b5ff469085e804f2db",
      "coronavirusEmployment": "now-jobless",
      "meta": {
        "fetchInclude": "[coronavirusEmployment,id,version]",
        "fetchType": "SurveyData"
      },
      "version": 1
    },
    ...
  ],
  "count": 2025,
  "hasMore": false
}
header Parameters
Content-Type
required
string

Set this to application/json.

Accept
required
string

Set this to application/json.

Request Body schema: application/json
spec
object

Container of query evaluation attributes

filter
string

Filter expression for which Obj instances to return. For example: "filter": 'id == "Afghanistan" && age == 45'. Filter expressions must evaluate to a value type of boolean. They support basic comparison operators (e.g. ==, <, <=, >, >=, !=), arithmetic operators (e.g. +, -, /), &&, || and most non-time series functions supported by the expression engine."

include
string

Specifies which fields to bring back values for in the returned objects. A list of fields. For example: "include": "productType, description, origin, links.url".

limit
integer

Maximum number of rows that should be returned, starting from offset.

offset
integer <int32> (The Offset Schema)

Number of rows to skip.

Responses

200

OK. The request has succeeded.

Response Schema: application/json
objs
object

Container of query evaluation attributes

count
integer <int32> (The Count Schema)

Number of rows returned.

hasMore
boolean (The Hasmore Schema)

If set to true there were more objs that were not returned.

post /api/1/surveydata/fetch
https://api.c3.ai/covid/api/1/surveydata/fetch

Request samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "spec":
    {
    }
}

Response samples

Content type
application/json
Copy
 Expand all Collapse all
{
  • "objs":
    {
    },
  • "count": 0,
  • "hasMore": true
}