Coronavirus disease 2019 (COVID-19), a global pandemic, has affected every sector of human life. While our understanding of the spread of COVID-19 is still evolving, policymakers need to make strategic decisions amidst this uncertainty to mitigate the pandemic. Appropriate policy decisions can reduce morbidity, mortality, and damage to the healthcare system. This study will estimate the underlying prevalence of COVID-19 at the county level and project future trajectories of COVID-19 under various sequences of non-pharmaceutical and pharmaceutical interventions. In addition, these new estimations and projections will be incorporated into our existing online COVID-19 Simulator (www.covid19sim.org) to inform county-level decisions. In addition, the COVID-19 Simulator will detect early signs of community-level COVID-19 outbreaks and predict hotspots in different jurisdictions. From a societal perspective, this research will improve our understanding of COVID-19 transmission and inform policies to mitigate the spread of the virus, ultimately leading to a reduction in COVID-19 disease burden.
This research will use epidemiological and operations research modeling approaches to simulate the spread of COVID-19 at the county level and the effects of different interventions on mitigation of COVID-19. In addition, causal decision tree and mixed-integer programing-based machine learning algorithms will be used to identify county level hotspots of transmission. The proposed research will not only inform key decisions with important public health implications, but also contribute to intellectual merits, including 1) linking and using various datasets in near real-time to improve our understanding of disease epidemiology, 2) estimating the underlying prevalence of COVID-19 and projecting future trajectories under various interventions, 3) developing innovative approaches for real time parameter estimations, model calibrations, and projections, and (4) detecting early signs of community-level COVID-19 outbreaks using epidemiological, statistical and machine learning based modeling techniques.
This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act
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.
