COVID-19 behaves very differently within individual states, yet state-level policy data is all that has been available to policymakers and scientists planning responses. The solution—zoom into the county level.

Students at Harvard Medical School have led a crowdsourcing effort of 100+ medical students and health professionals across the nation to build out the largest county-level COVID-19 policy dataset and associated interactive map in the nation.

Jagpreet Chhatwal, PhD, a senior scientist at the Mass General Institute for Technology Assessment and an assistant professor at Harvard Medical School, has provided guidance in the development and analysis of this novel granular policy data. Unforeseen trends not seen with state-level analysis are already being uncovered.

To date, the free and completely open-source dataset and associated interactive map contain detailed COVID-19 policy data for over 1,200 counties and federally recognized Native American nations. The dataset is neatly packaged for data users on GitHub and is ready for use by CDC-endorsed modelers or any group looking to do county-level policy analysis.

Given the highly variable nature of COVID-19, as well as the major differences in population and policies within states, county-level policy data will be an important analytics asset for epidemiologists, economists, and policymakers devising future steps, as all US states started reopening businesses over the past month. The interactive map also allows county residents to review a compilation of the local policy decisions that have shaped their lives for the past several months and view the associations between local initiatives and infection rates. 

For the counties and Native nations included, the dataset contains the types of policy interventions and timestamps for when they were put in place and lifted at a county level, coupled with the New York Times COVID-19 county-level case statistics. This dataset allows for a more precise analysis of the impact of such policies on COVID-19 spread.

Preliminary analysis has revealed unexpected findings not seen in state-level analyses related to how long it takes certain policies to impact infection rates, and how these vary based on county characteristics, as well as the considerable intrastate variation compared to interstate for certain policy interventions.

The largest of its kind, the dataset is already becoming the preferred policy dataset over more general state-level data – having been accessed thousands of times and used by modelers at local governments, hospitals, leading nonprofits, and universities including Harvard, Stanford, and Columbia.

Organized by Cray V. Noah and Dana M. Vigue of Harvard Medical School with oversight from Dr. Jagpreet Chhatwal and in association with Hikma Health — a nonprofit dedicated to the democratization of data/digital health for marginalized communities led the initial hackathon generating this idea — the dataset was crowdsourced by a team of 100+ volunteers spanning all Harvard University schools and numerous other academic institutions, all following a standardized 7-step protocol with frequent quality control source checks.

Despite being so valuable, data with this county-level resolution is scarce because finding it is so tedious. Every county is different, from their websites to their local news stations, so the process can't be automated and instead demands the thousands of hours and discernment our incredible volunteer team has put in," said Project Lead, 4th-year Harvard MD-MBA student and Mass General researcher Cray Noah.

While the county dataset is now complete, the Hikma Health team is working to keep it dynamically updated. Efforts are ongoing this month to update county policies as they change, and to continue crowdsourcing policies for the disproportionately affected Native American communities.

"This dynamic, high-resolution data set will help researchers and policymakers understand how individual communities are experiencing this pandemic," said Project Lead and 4th-year Harvard MD-PhD student Dana Vigue. "Insights into local variations in COVID-19 impact and response can inform community-centered policies to help curb the spread of the virus and save lives."

Those interested in being a part of this effort can contact