RAPID: Dynamical Modeling of COVID-19

This project will develop a set of mathematical models of the spread of SARS-CoV-2, the cause of COVID-19. The 2019-2020 global coronavirus pandemic is ongoing. Much remains unknown about the epidemic trajectory and potential paths to containment. The researchers will map the spatial spread of the virus, estimate key parameters related to transmission, compile clinical and epidemiological information, and assess the effectiveness of public health interventions on containment. In doing so the work will develop concepts and mathematical theory essential for the understanding of the spread of COVID-19 with possible applications to other emerging infectious diseases. The proposed work will provide estimates of key epidemiological parameters necessary for understanding the spread of SARS-CoV-2 as a public health emergency, in particular, and the future control of other emerging zoonosis. This research will provide timely information on the COVID-19 epidemic immediately useful to the operations of the CDC, as well as contribute to the training of two graduate students and a postdoc.

The specific aims of this project are, first, to contextualize COVID-19 in comparison to previous outbreaks of infectious respiratory diseases and estimate infectiousness, incubation period, transmissibility, case severity, and case fatality rate. Second, it will estimate and visualize epidemiological parameters for COVID-19, including: the epidemic curve, the basic reproduction number (R0), the case detection rate, the incubation period, transmissibility, and the lag between symptom onset and isolation. Third, it will develop and parameterize a U.S. spatial model to help determine the optimal allocation of resources such as personnel, funding, supplies at multiple spatial scales such as states, cities, and hospitals. Fourth, it will fit stochastic dynamical models to case notification data to draw conclusions about the effectiveness of interventions such as lockdowns, curfews, and school closures. These aims will be accomplished by leveraging public, crowd-sourced, and government data with stochastic dynamical models for transmission and spatial spread.

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.