Estimating SARS-CoV-2 Seroprevalence

TL;DR

This paper develops and compares statistical methods for accurately estimating SARS-CoV-2 seroprevalence, accounting for assay errors and sampling biases, with applications to data from multiple regions.

Contribution

It introduces nonparametric and parametric estimators that correct for misclassification and selection bias, demonstrating their consistency and asymptotic properties.

Findings

Estimators perform well across various simulation scenarios.

Application to real data yields plausible seroprevalence estimates.

Methods improve accuracy over traditional approaches.

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, nonparametric 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 SARS-CoV-2 seroprevalence in New York City, Belgium, and North Carolina.