Knowledge of the number of individuals who have been infected with the novel coronavirus SARS-CoV-2 and the extent to which attempts for mitigation by executive order have been effective at limiting its spread are critical for effective policy going forward. Directly assessing prevalence and policy effects is complicated by the fact that case counts are unreliable. In this paper, we present a model for using death-only data—in our opinion, the most stable and reliable source of COVID-19 information—to estimate the underlying epidemic curves. Our model links observed deaths to an SIR model of disease spread via a likelihood that accounts for the lag in time from infection to death and the infection fatality rate. We present estimates of the extent to which confirmed cases in the United States undercount the true number of infections, and analyze how effective social distancing orders have been at mitigating or suppressing the virus. We provide analysis for four states with significant epidemics: California, Florida, New York, and Washington.
Keywords: COVID-19, SARS-CoV-2, compartmental model, SIR model, Bayesian
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