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...
Main Authors: | , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Nature Portfolio
2023-04-01
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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. |
first_indexed | 2024-04-09T16:23:06Z |
format | Article |
id | doaj.art-aaed3585d81743a8943026cd71dc3efe |
institution | Directory Open Access Journal |
issn | 2041-1723 |
language | English |
last_indexed | 2024-04-09T16:23:06Z |
publishDate | 2023-04-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Nature Communications |
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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