Accurate estimates of SARS-CoV-2 infection are crucial for pandemic response. Confirmed COVID-19 case counts in the U.S. underestimate the true burden due to limited testing. Using a semi-Bayesian probabilistic bias analysis, the study estimated 6,454,951 cumulative infections by April 18, 2020, compared to 721,245 confirmed cases. Incomplete testing accounted for 86% of this difference, with imperfect test accuracy accounting for the remaining 14%. The methodology can be applied elsewhere to improve COVID-19 burden assessment.
Publisher
Nature Communications
Published On
Jul 28, 2020
Authors
Sean L. Wu, Andrew N. Mertens, Yoshika S. Crider, Anna Nguyen, Nolan N. Pokpongkiat, Stephanie Djaajadi, Anmol Seth, Michelle S. Hsiang, John M. Colford Jr., Art Reingold, Benjamin F. Arnold, Alan Hubbard, Jade Benjamin-Chung
Tags
SARS-CoV-2
COVID-19
infection estimates
probabilistic bias analysis
testing accuracy
public health
pandemic response
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