Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S

Bernardo García-Carreras (), Matt D. T. Hitchings, Michael A. Johansson, Matthew Biggerstaff, Rachel B. Slayton, Jessica M. Healy, Justin Lessler, Talia Quandelacy, Henrik Salje, Angkana T. Huang and Derek A. T. Cummings
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Bernardo García-Carreras: University of Florida
Matt D. T. Hitchings: University of Florida
Michael A. Johansson: US Centers for Disease Control and Prevention
Matthew Biggerstaff: US Centers for Disease Control and Prevention
Rachel B. Slayton: US Centers for Disease Control and Prevention
Jessica M. Healy: US Centers for Disease Control and Prevention
Justin Lessler: University of North Carolina at Chapel Hill
Talia Quandelacy: University of Colorado Anschutz Medical Campus
Henrik Salje: University of Cambridge
Angkana T. Huang: University of Cambridge
Derek A. T. Cummings: University of Florida

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.

Date: 2023
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DOI: 10.1038/s41467-023-37944-5

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