Data and Code for: Behavior and the Transmission of COVID-19

We show that a simple model of COVID-19 that incorporates feedback fromdisease prevalence to disease transmission through an endogenous response ofhuman behavior does a remarkable job of fitting the main features of data onthe growth rates of daily deaths observed across a large number countries andStates of the United States from March into November of 2020. This finding,however, suggests a new empirical puzzle. Using an accounting procedure akinto that used for Business Cycle Accounting as in Chari et al. (2007), we showthat when the parameters of the behavioral response of transmission to diseaseprevalence are estimated from the early phase of the epidemic, very largewedges that shift disease transmission rates holding disease prevalence fixed arerequired both across regions and within a region over time to have the modelmatch the data on deaths from COVID-19 as an equilibrium outcome exactly.We show that these wedges correspond to large shifts in model forecasts for theherd immunity threshold for COVID-19 both across locations and over time.Future research should focus on understanding the sources in these wedges inthe relationships between disease prevalence and disease transmission.<br>