Estimating SARS-CoV-2 seroprevalence
Journal of the Royal Statistical Society Series a-Statistics in Society
; 2023.
Article
in English
| Web of Science | ID: covidwho-2328200
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Observational study
/
Prognostic study
/
Qualitative research
Language:
English
Journal:
Journal of the Royal Statistical Society Series a-Statistics in Society
Year:
2023
Document Type:
Article
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