RAPID: Inference, Forecasting, and Intervention Modeling of COVID-19

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A novel coronavirus emerged in late 2019 and by the end of March 2020 had spread to more than 170 countries, causing more than 750,000 confirmed cases of COVID-19 disease and over 4000 deaths. There is an urgent need to better understand this rapidly evolving crisis to predict and mitigate its effects. The focus of this project is to apply mathematical and statistical models to: 1) estimate critical characteristics of the virus and its transmission; 2) forecast new COVID-19 outcomes; and 3) estimate the potential effectiveness of non-pharmaceutical (e.g. school closures, travel restrictions) interventions. Projections of COVID-19 cases, hospital bed demand and ventilator demand, will be communicated to the public, public health agencies and government officials as they are developed in order to support real-time policy decision making. Inference, forecasting, and intervention modeling of the COVID-19 outbreak in the US is needed to improve understanding of SARS-CoV2 transmission dynamics and to support COVID-19 response and intervention efforts. For this project, observations of reported infections in the US, in conjunction with commuting data, a networked dynamic metapopulation model and Bayesian inference, will be used to infer critical and evolving characteristics associated with SARS-CoV2 spread in the US, including the fraction of undocumented infections and their contagiousness. Using these same model-inference methods, ensemble projections of future COVID-19 incidence in the US will be developed and generated. Finally, models will be developed to simulate and study the effects of non-pharmaceutical interventions, including school closure, isolation, quarantine and travel restrictions, on COVID-19 incidence. Project findings will be communicated in real time to public health and government officials. This award is co-funded with the Ecology and Evolution of Infectious Diseases program (Division of Environmental Biology), the Applied Mathematics program (Division of Mathematical Sciences), and the Office of Multidisciplinary Activities (OMA) program.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.