The outbreak of COVID-19 has created a tremendous need for predicting both the dynamics and the size of regional COVID-19 outbreaks. Equally important is the need to determine the potential effects of early interventions such as school closures and mandatory or self-imposed quarantines. To answer these questions, this project will develop a general mathematical framework for analyzing the ongoing outbreak trends using data solely from partially observed new daily infection counts (also known as the epidemic curve). The PI?s new framework will not assume any specific infectious or recovery periods (which are often unknown) or observable prevalence of the disease. The tools developed as part of this project will both help predict the rate of growth of new infections and estimate the effect of social distancing and other preventative measures on flattening the epidemic curve. The PI will use a new dynamical survival analysis approach to predict the trajectory of the COVID-19 epidemic for a mid-western region of the United States. Data from elsewhere in the world, like the city of Wuhan in China, will be used to calibrate the predictions. The project will also provide a practical interdisciplinary training for a PhD student and a post-doctoral fellow.

The modeling and predictive framework to be developed is fundamentally different from the traditional approach based on the incidence or prevalence counts in a compartmental SIR model. Specifically, the PI will apply the dynamical survival analysis (DSA) approach that considers aggregated mean field equations for the underlying large stochastic network and regards them as the approximate survival law of the infection times. The PI will use these DSA-based equations to model both the epidemic and recovery curves and compare them with the ones observed during the COVID-19 outbreak. The statistical analysis of epidemic data performed with the help of the new framework will allow the quick elucidation of the dynamics of an epidemic (for example, the basic reproduction number, R0) and the potential impact of interventions (such as quarantine or social distancing). The new framework will help provide a better understanding of how preventive behaviors affect COVID-19 dynamics via changes in the network structure and changes in disease transmission across edges in the network. This project will develop a user-friendly software package for computer simulations under different parameter and intervention scenarios (for example, vaccination schemes) that will lead to a better understanding of how to control COVID-19 transmission.

This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.

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 Mathematical Sciences (DMS)
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Junping Wang
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Ohio State University
United States
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