Estimating SARS-CoV-2 seroprevalence

Samuel P Rosin; Bonnie E Shook-Sa; Stephen R Cole; Michael G Hudgens

doi:10.1093/jrsssa/qnad068

Estimating SARS-CoV-2 seroprevalence

J R Stat Soc Ser A Stat Soc. 2023 May 19;186(4):834-851. doi: 10.1093/jrsssa/qnad068. eCollection 2023 Oct.

Authors

Samuel P Rosin¹, Bonnie E Shook-Sa¹, Stephen R Cole², Michael G Hudgens¹

Affiliations

¹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
² Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.

PMID: 38145241
PMCID: PMC10746549 (available on 2024-05-19)
DOI: 10.1093/jrsssa/qnad068

Abstract

Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, non-parametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in New York City, Belgium, and North Carolina.

Keywords: COVID-19; diagnostic tests; estimating equations; seroepidemiologic studies; standardization.

Abstract

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