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

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 s...

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Main Authors: 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, Derek A. T. Cummings
Format: Article
Language:English
Published: Nature Portfolio 2023-04-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-023-37944-5
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author 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
Derek A. T. Cummings
author_facet 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
Derek A. T. Cummings
author_sort Bernardo García-Carreras
collection DOAJ
description 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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spelling doaj.art-aaed3585d81743a8943026cd71dc3efe2023-04-23T11:22:13ZengNature PortfolioNature Communications2041-17232023-04-0114111110.1038/s41467-023-37944-5Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.Bernardo García-Carreras0Matt D. T. Hitchings1Michael A. Johansson2Matthew Biggerstaff3Rachel B. Slayton4Jessica M. Healy5Justin Lessler6Talia Quandelacy7Henrik Salje8Angkana T. Huang9Derek A. T. Cummings10Department of Biology, University of FloridaDepartment of Biostatistics, University of FloridaCOVID-19 Response, US Centers for Disease Control and PreventionCOVID-19 Response, US Centers for Disease Control and PreventionCOVID-19 Response, US Centers for Disease Control and PreventionCOVID-19 Response, US Centers for Disease Control and PreventionDepartment of Epidemiology, University of North Carolina at Chapel HillUniversity of Colorado Anschutz Medical CampusDepartment of Genetics, University of CambridgeDepartment of Genetics, University of CambridgeDepartment of Biology, University of FloridaAbstract 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.https://doi.org/10.1038/s41467-023-37944-5
spellingShingle 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
Derek A. T. Cummings
Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
Nature Communications
title Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
title_full Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
title_fullStr Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
title_full_unstemmed Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
title_short Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
title_sort accounting for assay performance when estimating the temporal dynamics in sars cov 2 seroprevalence in the u s
url https://doi.org/10.1038/s41467-023-37944-5
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