Simple approximation of sample size for precise estimates of SARS-CoV-2 infection from point-seroprevalence studies

A M Nikiforuk; I Sekirov; A N Jassem

doi:10.1016/j.puhe.2022.08.008

Simple approximation of sample size for precise estimates of SARS-CoV-2 infection from point-seroprevalence studies

Public Health. 2022 Nov:212:7-9. doi: 10.1016/j.puhe.2022.08.008. Epub 2022 Aug 23.

Authors

A M Nikiforuk¹, I Sekirov², A N Jassem²

Affiliations

¹ British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada; School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: aidan.nikiforuk@bccdc.ca.
² British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.

Abstract

Objective: This study aimed to model the precision of SARS-CoV-2 seroprevalence estimates.

Methods: Sample size and precision estimates were calculated using the normal approximation to the binomial distribution. The relationship between sample size and precision was visualized across a range of assumed SARS-CoV-2 seroprevalence from 2% to 75%.

Results: The calculation found that 2% precision was attainable by taking moderately sized sample sets when the expected seroprevalence of SARS-CoV-2 infection exceeds 2%. In populations with a low incidence of SARS-CoV-2 infection and an expected seroprevalence of less than 2%, larger samples are required for precise estimates.

Conclusions: Taking a sample of 177-1000 participants can provide precise prevalence estimates of SARS-CoV-2 infection in vaccinated and unvaccinated populations. Larger sample sizes are only necessary in low prevalence settings.

Keywords: Epidemiology; Precision; Prevalence; SARS-CoV-2; Serological survey; Study design.

MeSH terms

COVID-19* / epidemiology
Humans
Prevalence
SARS-CoV-2
Sample Size
Seroepidemiologic Studies