1. Estimating SARS-CoV-2 seroprevalence.
- Author
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Rosin, Samuel P, Shook-Sa, Bonnie E, Cole, Stephen R, and Hudgens, Michael G
- Subjects
SARS-CoV-2 ,SELECTION bias (Statistics) ,SEROPREVALENCE ,COVID-19 pandemic - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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