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

Sean L. Wu, Andrew N. Mertens, Yoshika S. Crider, Anna Nguyen, Nolan N. Pokpongkiat, Stephanie Djajadi, Anmol Seth, Michelle S. Hsiang, John M. Colford, Art Reingold, Benjamin F. Arnold, Alan Hubbard and Jade Benjamin-Chung ()
Additional contact information
Sean L. Wu: University of California
Andrew N. Mertens: University of California
Yoshika S. Crider: University of California
Anna Nguyen: University of California
Nolan N. Pokpongkiat: University of California
Stephanie Djajadi: University of California
Anmol Seth: University of California
Michelle S. Hsiang: University of Texas Southwestern Medical Center
John M. Colford: University of California
Art Reingold: University of California
Benjamin F. Arnold: University of California
Alan Hubbard: University of California
Jade Benjamin-Chung: University of California

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-18272-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18272-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-18272-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().