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