Abstract
To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.
Highlights
To increase situational awareness and support evidencebased policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population
To contribute to situational awareness of COVID-19 transmission dynamics, we developed a mathematical model for the daily incidence of COVID-19 in each of the 15 most populous US metropolitan statistical areas (MSAs) [10]
Our analysis focused on the populations of US cities and their MSAs instead of regional populations within other political boundaries, such as those of US states
Summary
To increase situational awareness and support evidencebased policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We quantified uncertainty in parameter estimates and forecasts This online learning approach enables early identification of new trends despite considerable variability in case reporting. Thereafter, surveillance testing expanded nationwide [4] These and other efforts revealed community spread across the United States and exponential growth of new COVID-19 cases throughout most of March. This approach enabled identification of new epidemic trends despite variability in case detection These findings can inform policymakers designing evidence-based responses to regional COVID-19 epidemics in the United States
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