Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States

Abstract

The dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. We propose a simple method for estimating the time-varying infection rate and reproduction number R_t using a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of R_t. We demonstrate that the proposed adaptive SIR (aSIR) model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. The aSIR model was applied to data at the state and county levels.