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Triggered by the COVID-19 crisis, Israel's Ministry of Health (MoH) held avirtual Datathon based on deidentified governmental data. Organized by amultidisciplinary committee, Israel's research community was invited to offerinsights to COVID-19 policy challenges. The Datathon was designed to (1)develop operationalizable data-driven models to address COVID-19 health-policychallenges and (2) build a community of researchers from academia, industry,and government and rebuild their trust in the government. Three specificchallenges were defined based on their relevance (significance, dataavailability, and potential to anonymize the data): immunization policies,special needs of the young population, and populations whose rate of compliancewith COVID-19 testing is low. The MoH team extracted diverse, reliable,up-to-date, and deidentified governmental datasets for each challenge. Secureremote-access research environments with relevant data science tools were seton Amazon Web. The MoH screened the applicants and accepted around 80participants, teaming them to balance areas of expertise as well as representall sectors of the community. One week following the event, anonymous surveysfor participants and mentors were distributed to assess overall usefulness andpoints for improvement. The 48-hour Datathon and pre-event sessions included 18multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5presentation mentors, and 12 judges. The insights developed by the 3 winningteams are currently considered by the MoH as potential data science methodsrelevant for national policies. The most positive results were increased trustin the MoH and greater readiness to work with the government on these or futureprojects. Detailed feedback offered concrete lessons for improving thestructure and organization of future government-led datathons.

COVID-19 Datathon Based on Deidentified Governmental Data as an Approach  for Solving Policy Challenges, Increasing Trust, and Building a Community:  Case Study