The emergence and rapid spread of COVID-19 has transformed our world in a matter of months. In the course of a year, COVID-related illness has claimed the lives of more than 2 million people across the globe and more than 400,000 people in the United States. The outbreak has exposed critical vulnerabilities in our healthcare system and created unprecedented logistical challenges in the distribution of equipment, vaccines, tests, and other resources. Furthermore, policymakers have found themselves facing difficult choices regarding the timing and severity of non-therapeutic interventions which slow the spread of disease but also incur negative economic and downstream health impacts. An optimal adaptive decision strategy uses accumulating data to inform how resources should be allocated over space and time to maximize cumulative health while controlling cost. In this context, an optimal decision strategy has the potential to save tens of thousands of lives, use resources more efficiently, and reduce negative economic impacts. This research project develops new methodologies for estimation of optimal decision strategies using heterogeneous data streams including human mobility data, air traffic, as well as information on disease spread and vaccine dissemination. This project also provides training opportunities for students and a postdoctoral scholar.

This research project advances statistical and mathematical disease models by including behavior-change into stochastic compartmental models. The integration of multiple data streams including human-mobility, the census, incidence and testing reports, vaccination dissemination, and air-travel allows for richer and more realistic models than those fit using a single data modality. Furthermore, mobility data linked with census data will generate new knowledge about heterogeneity in response to interventions such as shelter-in-place across demographics. These models are integrated into novel spatio-temporal reinforcement learning methods for estimation of an optimal decision strategy that recommends how resources and non-therapeutic interventions should be allocated across multiple resolutions (e.g., locally and nationally) to minimize disease spread subject to constraints on economic and other downstream impacts.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Environmental Biology (DEB)
Standard Grant (Standard)
Application #
Program Officer
Katharina Dittmar
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Duke University
United States
Zip Code