Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.
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DOI:
10.1038/s41467-023-37944-5
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发表时间:
2023-04-19
影响因子:
16.6
通讯作者:
Cummings, Derek A. T.
中科院分区:
文献类型:
--
作者:
Garcia-Carreras, Bernardo;Hitchings, Matt D. T.;Johansson, Michael A.;Biggerstaff, Matthew;Slayton, Rachel B.;Healy, Jessica M.;Lessler, Justin;Quandelacy, Talia;Salje, Henrik;Huang, Angkana T.;Cummings, Derek A. T.
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. SARS-CoV-2 seroprevalence surveys aim to estimate the proportion of the population that has been infected, but their accuracy depends on the characteristics of the test assay used. Here, the authors use statistical models to assess the impact of the use of different assays on estimates of seroprevalence in the United States.
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影响因子:
13.6
作者:
Peluso MJ;Takahashi S;Hakim J;Kelly JD;Torres L;Iyer NS;Turcios K;Janson O;Munter SE;Thanh C;Donatelli J;Nixon CC;Hoh R;Tai V;Fehrman EA;Hernandez Y;Spinelli MA;Gandhi M;Palafox MA;Vallari A;Rodgers MA;Prostko J;Hackett J Jr;Trinh L;Wrin T;Petropoulos CJ;Chiu CY;Norris PJ;DiGermanio C;Stone M;Busch MP;Elledge SK;Zhou XX;Wells JA;Shu A;Kurtz TW;Pak JE;Wu W;Burbelo PD;Cohen JI;Rutishauser RL;Martin JN;Deeks SG;Henrich TJ;Rodriguez-Barraquer I;Greenhouse B
通讯作者:
Greenhouse B
DOI:
10.15585/mmwr.mm7117e3
发表时间:
2022-04-29
期刊:
MMWR. Morbidity and mortality weekly report
影响因子:
--
作者:
通讯作者:
--
影响因子:
4.3
作者:
通讯作者:
--
影响因子:
120.7
作者:
Jones, Jefferson M.;Stone, Mars;Busch, Michael P.
通讯作者:
Busch, Michael P.
DOI:
10.1073/pnas.2103272118
发表时间:
2021-08-03
影响因子:
11.1
作者:
Irons NJ;Raftery AE
通讯作者:
Raftery AE