Estimating area-level variation in SARS-CoV-2 infection fatality ratios

ABSTRACT

BackgroundDuring a pandemic, estimates of geographic variability in disease burden are important but limited by the availability and quality of data. MethodsWe propose a framework for estimating geographic variability in testing effort, total number of infections, and infection fatality ratio (IFR). Because symptomatic people are more likely to seek testing, we use a noncentral hypergeometric model that accounts for differential probability of positive tests. We apply this framework to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs from March 1, 2020 to October 31, 2020. Using data on population size, number of observed cases, number of reported deaths in each U.S. county and state, and number of tests in each U.S. state, we develop a series of estimators to identify the number of SARS-CoV-2 infections and IFRs at the county level. We then perform a simulation and compare the estimated values to simulated values to demonstrate the validity of our approach. FindingsApplying the county-level estimators to the real, unsimulated COVID-19 data spanning March 1, 2020 to October 31, 2020 from across the U.S., we found that IFRs varied from 0 to 0.0273, with an interquartile range of 0.0022 and a median of 0.0018. The estimators for IFRs, number of infections, and number of tests showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.88, 0.95, and 0.74, respectively. InterpretationWe propose an estimation framework that can be used to identify area-level variation in IFRs and performs well to estimate county-level IFRs in the U.S. COVID-19 pandemic.