Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests

David Kline (), Zehang Li, Yue Chu, Jon Wakefield, William C. Miller, Abigail Norris Turner and Samuel J. Clark ()
Additional contact information
David Kline: Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210
Zehang Li: Department of Statistics, University of California, Santa Cruz, CA 95064
Yue Chu: Department of Sociology, The Ohio State University, Columbus, OH 43210
Jon Wakefield: Department of Statistics, University of Washington, Seattle, WA 98195; Department of Biostatistics, University of Washington, Seattle, WA 98195
William C. Miller: Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43210
Abigail Norris Turner: Division of Infectious Diseases, College of Medicine, The Ohio State University, Columbus, OH 43210
Samuel J. Clark: Department of Sociology, The Ohio State University, Columbus, OH 43210

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 26, e2023947118

Abstract: Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States—Indiana and Connecticut—have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.

Keywords: coronavirus; COVID-19; imperfect diagnostic tests; SARS-CoV-2; seroprevalence survey (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.pnas.org/content/118/26/e2023947118.full (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:118:y:2021:p:e2023947118

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().