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

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 ass...

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Detalles Bibliográficos
Autores principales: Nikiforuk, A.M., Sekirov, I., Jassem, A.N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Ltd on behalf of The Royal Society for Public Health. 2022
Materias:
Short Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395286/
https://www.ncbi.nlm.nih.gov/pubmed/36174438
http://dx.doi.org/10.1016/j.puhe.2022.08.008
Descripción
Sumario: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.

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