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

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  • García-Carreras, B
    • Other Affiliation: Department of Biology, University of Florida, Gainesville, FL, United States
  • Hitchings, M.D.T
    • Other Affiliation: Department of Biostatistics, University of Florida, Gainesville, FL, United States
  • Johansson, M.A
    • Other Affiliation: COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, United States
  • Biggerstaff, M
    • Other Affiliation: COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, United States
  • Slayton, R.B
    • Other Affiliation: COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, United States
  • Healy, J.M
    • Other Affiliation: COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, United States
  • Lessler, J
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Quandelacy, T
    • Other Affiliation: University of Colorado Anschutz Medical Campus, Aurora, CO, United States
  • Salje, H
    • Other Affiliation: Department of Genetics, University of Cambridge, Cambridge, United Kingdom
  • Huang, A.T
    • Other Affiliation: Department of Genetics, University of Cambridge, Cambridge, United Kingdom
  • Cummings, D.A.T
    • Other Affiliation: Department of Biology, University of Florida, Gainesville, FL, United States
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.
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  • Article
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  • In Copyright
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  • Attribution 4.0 International
Journal title
  • Nature Communications
Journal volume
  • 14
Journal issue
  • 1
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  • English
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  • Publisher
Funder
  • National Science Foundation, NSF: 2223843
  • Centers for Disease Control and Prevention, CDC
ISSN
  • 2041-1723
Publisher
  • Nature Research

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