Data and Models for COVID-19 Decision-Making
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As states begin to reopen, there is an urgent need for COVID-19 projections that can guide decision-makers in relaxing restrictions in a way that minimizes the chance of resurgence. Though researchers have access to a wide variety of mathematical infectious disease transmission models, limited data and uncertainties about epidemiological features of COVID-19 make statistical calibration of these models challenging. Futhermore, decision-makers are often more interested in future projections under specified re-opening scenarios than in estimated parameter values. In this presentation, I outline mathematical and statistical approaches for projecting COVID-19 incidence, hospitalization, and deaths. I describe a simple class of transmission models that balance parsimony with epidemiological realism for the epidemic in Connecticut. Using unique access to data from Connecticut, and information about the Governor's stated reopening plans, we find that closure of schools and the statewide “Stay Safe, Stay Home” order have effectively reduced COVID-19 transmission in Connecticut, with model projections estimating incidence at about 1,500 new infections per day. If close interpersonal contact increases quickly in Connecticut following reopening on May 20, the state is at risk of a substantial increase COVID-19 infections, hospitalizations, and deaths by late Summer 2020. However, real-time metrics including case counts, hospitalizations, and deaths may fail to provide enough advance warning to avoid resurgence. Substantial uncertainty remains in our knowledge of cumulative COVID-19 incidence, the proportion of infected individuals who are asymptomatic, infectiousness of children, the effects of testing and contact tracing on isolation of infected individuals, and how contact patterns may change following reopening.