We fit a Bayesian model to data on the number of deaths attributable to COVID-19 with the goal of estimating the number of infected individuals. Our model links an underlying Susceptible Infectious Removed (SIR) model of disease dynamics to observed deaths via a time-to-death distribution informed by previous studies. This allows us to actually fit a statistical model to the data, unlike many epidemiological studies in which the SIR model parameters are simply "calibrated" to obtain outputs that look similar to the real data. The main outputs of our model are estimates of the number of infections currently, as well as forecasts of the number of infections and deaths under various scenarios for the effectiveness of social distancing measures . All of our outputs have attached Bayesian credible intervals. An important conclusion is that the confirmed case counts greatly underestimate the total number of infected individuals .
Understanding the number of individuals who have been infected with the novel coronavirus SARS-CoV-2, and the extent to which social distancing policies have been effective at limiting its spread, are critical for effective policy going forward. Here 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 measures have been at mitigating or suppressing the virus. Our analysis uses a Bayesian model of COVID-19 fatalities with a likelihood based on an underlying differential equation model of the epidemic. We provide analysis for four states with significant epidemics: California, Florida, New York, and Washington. Our short-term forecasts suggest that these states may be following somewhat different trajectories for growth of the number of cases and fatalities .
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We fit a Bayesian model to data on
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deaths attributable to COVID-19 with the goal of estimating the number of infected individuals. Our model links an underlying Susceptible Infectious Removed (SIR) model of disease dynamics to observed deaths via a time-to-death distribution informed by previous studies. This allows us to actually fit a statistical model to the data, unlike many epidemiological studies in which the SIR model parameters are simply "calibrated" to obtain outputs that look similar to the real data. The main outputs of our model are
individuals who have been infected with the novel coronavirus SARS-CoV-2, and the extent to which social distancing policies have been effective at limiting its spread, are critical for effective policy going forward. Here we present
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number of infections currently, as well as forecasts of the
extent to which confirmed cases in the United States undercount the true
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and deaths under various scenarios for the effectiveness of
, and analyze how effective
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. All of our outputs have attached Bayesian credible intervals. An important conclusion is that the confirmed case counts greatly underestimate the total number of infected individuals
have been at mitigating or suppressing the virus. Our analysis uses a Bayesian model of COVID-19 fatalities with a likelihood based on an underlying differential equation model of the epidemic. We provide analysis for four states with significant epidemics: California, Florida, New York, and Washington. Our short-term forecasts suggest that these states may be following somewhat different trajectories for growth of the number of cases and fatalities