Substantial underestimation of SARS-CoV-2 infection in the United States

Sean L Wu,Anna Nguyen,Michelle S Hsiang,Anmol Seth,Andrew N Mertens,John M Colford,Alan Hubbard,Nolan N Pokpongkiat,Benjamin F Arnold,Art Reingold,Yoshika S Crider,Jade Benjamin-Chung,Stephanie Djajadi

doi:10.1038/s41467-020-18272-4

Abstract

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64–99%) of this difference is due to incomplete testing, while 14% (0.3–36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.

Highlights

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response
We estimate the total number of SARS-CoV-2 infections in each U.S state from February 28 to April 18, 2020 using probabilistic bias analysis, a semi-Bayesian approach, to correct empirical confirmed case counts for bias due to incomplete testing and imperfect test accuracy
When correcting for incomplete testing and imperfect test accuracy, we estimated that the total number of SARS-CoV-2 infections in the U.S by April 18, 2020 was 6,454,951 (19 per 1000)— an estimate nine times larger than the 721,245 confirmed cases (2 per 1000) reported during this period

Summary

Introduction

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. We estimate the total number of SARS-CoV-2 infections in each U.S state from February 28 to April 18, 2020 using probabilistic bias analysis, a semi-Bayesian approach, to correct empirical confirmed case counts for bias due to incomplete testing and imperfect test accuracy. This method is commonly used to quantify the impact of and correct for measurement bias in observational epidemiologic studies[22]. The simplicity of our approach facilitates transparent assessment of modeling assumptions

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