This project will produce a practical mathematical modeling framework that will assess pharmaceutical and non-pharmaceutical intervention strategies in the US, over the course of the COVID-19 pandemic. In the midst of this current unprecedented crisis, public health decisions must be made quickly when there is a large level of uncertainty about the burden of COVID-19 in the community. Research to evaluate intervention strategies can help decision-makers to identify the proper control measures and intensity needed for containment and mitigation of COVID-19. To accomplish these goals, the researchers will develop a mathematical framework that will provide estimates of healthcare demands and effectiveness of various containment and mitigation approaches at state and county levels in the U.S. An open-access, interactive dashboard will provide vital insights that policy makers can use to make and adjust informed decisions during the course of the pandemic depending on available resources as well as under various scenarios and locales.
A stochastic modelling framework will be used. The framework will follow the natural history of the disease, with a compartmentalization of susceptible-exposed-infected-removed (SEIR) and stratified by 16 age groups. The model will be parametrized useing data representing contact patterns, demographic variables, hospitalizations, deaths, incidence, disease characteristics, and capacity of critical healthcare infrastructure (e.g., hospital beds) in US states and counties. To determine an optimal deployment strategy of diagnostic tests tailored to the stage of the COVID-19 outbreak, the researchers will simulate transmission dynamics of COVID-19 to project relevant outcomes of disease. Strategies will account for the available number of testing kits. As the symptoms of COVID-19 are difficult to distinguish from influenza, historical data trends will be used to adjust the number of cases of influenza-like-illness seeking medical treatment. The researchers will explore strategies ranging from testing of individuals presenting in hospitals with COVID-19 symptoms to age and risk dependent surveillance where individuals from high-risk areas would be tested. To assess the impact of various non-pharmaceutical interventions on COVID-19 transmission and the accompanying healthcare surge capacity, the researchers will explore the consequences of different public health measures such as contact tracing, self-quarantine, and self-isolation.
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.
