Team registration is open and closes in COVID-19 Grand Challenge

Registration for the COVID-19 Grand Challenge competition opens on September 15th, 2020, and will announce the winners on December 9th, 2020. During this time, teams of up to five named participants will work to generate innovative and meaningful data-driven insights that will fuel leading decisions to fight the COVID-19 pandemic. Grand Challenge Timeline Grand Challenge COVID-19 Data Lake

The COVID-19 Data Lake standardizes and integrates 40 data sets, making it the largest corpus of integrated, publicly available COVID-19 data in the world. Nevertheless, it remains imperfect and incomplete. That’s the reality both of COVID-19 and of public data. And it’s also why we encourage participants to use additional publicly available data sets to develop solutions.

Access the COVID-19 Data Lake

Submission Requirements

Completed solutions must be submitted by Nov. 18, 11:59 pm Pacific. For more detailed submission information, check out the Submission Requirements checklist.

1. Questionnaire (200 words max.)

2. Non-Technical Abstract (100 words max.)

3. Demo Video (2 mins max.)

4. Descriptive Paper (1,000 words max.)

5. Source Code

6. List of Data Sources Used

What Makes a Great Solution

In response to the acute challenges the world faces during the COVID-19 pandemic, we are looking for data science projects that provide insight into the pandemic and help mitigate the spread of the virus, improve the ability of the medical establishment to respond, minimize the impact of COVID-19 on society, inform policy makers, and more. While the problem you focus on is critical, it is equally important that your solution be smart, innovative, and new. We want  you to apply your knowledge of data science to address a pandemic-related problem, produce data-driven insights for decision makers, and fight for a  better tomorrow. Great solutions will demonstrate  and leverage your team’s combined skill set to produce impressive and meaningful insights.

Projects may address but are not limited to:

  • Applying machine learning/AI methods to mitigate the spread of COVID-19
  • Genome-specific COVID-19 medical protocols, including precision medicine of host responses​
  • Biomedical informatics methods for drug design and repurposing existing therapies​
  • Design and sharing of clinical trials for collecting and analyzing data on medications, therapies, and interventions​
  • Modeling, simulation, prediction of COVID-19 propagation and efficacy of interventions​
  • Logistics and optimization analysis for design of public health strategies and interventions​
  • Rigorous approaches to designing sampling and testing strategies​
  • Data analytics for COVID-19 research harnessing private and sensitive data, including the role of edge computing/IoT for gathering data​
  • Improving societal resilience in response to the spread of the pandemic​
  • Broader efforts in biomedicine, infectious disease modeling, response logistics and optimization, public health, tools, and methodologies around the  containment of infectious diseases, and response to pandemics so as to be better prepared for future infectious disease response.​


Solutions will be evaluated on the extent to which they derive insights, leveraging data science techniques (e.g., statistical analyses, AI/ML algorithms, optimization approaches, etc.) that were not obvious before. Solutions must use a minimum of two data sources contained within the COVID-19 Data Lake. The evaluation process will also focus on the extent to which the derived data science insights, if implemented at scale, will result in significant public health and economic benefits. Applicants are encouraged to estimate the public health and economic benefits of their solutions as part of their application.

Judging Panel

Submissions will be evaluated by the following panel of judges.

Pat House

Pat House

Vice Chairman,

Mike Callagy

Mike Callagy

County Manager, County of San Mateo

Richard Levin

Richard Levin

Board of Directors, and Former President Emeritus, Yale University

S. Shankar Sastry

S. Shankar Sastry

Co-Director, Digital Transformation Institute and Professor of Electrical Engineering & Computer Sciences, UC Berkeley

Zico Kolter

Zico Kolter

Associate Professor of Computer Science, Carnegie Mellon University