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

Author

Listed:
  • Bernardo García-Carreras

    (University of Florida
    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
    UNC Carolina Population Center)

  • 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
    University of Florida)

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.

Suggested Citation

  • 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. Huan, 2023. "Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37944-5
    DOI: 10.1038/s41467-023-37944-5
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    References listed on IDEAS

    as
    1. Miguel Sánchez-Romero & Vanessa di Lego & Alexia Prskawetz & Bernardo L. Queiroz, 2021. "An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-14, January.
    2. Isobel Routledge & Saki Takahashi & Adrienne Epstein & Jill Hakim & Owen Janson & Keirstinne Turcios & Jo Vinden & John Tomas Risos & Margaret Rose Baniqued & Lori Pham & Clara Di Germanio & Michael B, 2022. "Using sero-epidemiology to monitor disparities in vaccination and infection with SARS-CoV-2," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    3. 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 & Al, 2020. "Substantial underestimation of SARS-CoV-2 infection in the United States," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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