ArticleBMC medical research methodology2020
COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial.JMIR public health and surveillance · 2023Trial
- Determinants of COVID-19 prevalence in Central Java, Indonesia: An ecological study of socio-demographic, environmental, and healthcare factors.Dialogues in health · 2026Article
- Comparison of Infection-Induced SARS-CoV-2 Seroprevalence Across Large-Scale Residual Samples From Blood Donors, Commercial Laboratories, and Health Checkups in Japan, 2023.Open forum infectious diseases · 2025Article
- Detection ofInsects · 2024Article
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- The Italian data on SARS-CoV-2 infection in transplanted patients support an organ specific immune response in liver recipients.Frontiers in immunology · 2023Article
- The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.Scientific reports · 2021Article
- Pooled Testing Strategies for SARS-CoV-2 diagnosis: A comprehensive review.Diagnostic microbiology and infectious disease · 2021Review
- Pooling Nasopharyngeal Swab Specimens to Improve Testing Capacity for SARS-CoV-2 by Real-Time RT-PCR.Biological procedures online · 2021Article
- Efficiency of pooled surveillance testing in academic labs to detect and inhibit COVID-19 outbreaks.Bioanalysis · 2021Article
- Sample pooling is efficient in PCR testing of SARS-CoV-2: a study in 7400 healthcare professionals.Diagnostic microbiology and infectious disease · 2021Article
- Considerations for diagnostic COVID-19 tests.Nature reviews. Microbiology · 2021Review
- Sample pooling strategies for SARS-CoV-2 detection.Journal of virological methods · 2021Review
- Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails.Scientific reports · 2021Article
- Article
- Optimizing the diagnostic capacity for COVID-19 PCR testing for low resource and high demand settings: The development of information-dependent pooling protocol.Journal of global health · 2020Article
- COVID-19 Surveillance in the Primary Health Care Population of Qatar: Experience of Prioritizing Timeliness Over Representativeness When Sampling the Population.Frontiers in public healthArticle
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe number of confirmed COVID-19 cases divided by population size is used as a coarse measurement for the burden of disease in a population. However, this fraction depends heavily on the sampling intensity and the various test criteria used in different jurisdictions, and many sources indicate that a large fraction of cases tend to go undetected.
methodsEstimates of the true prevalence of COVID-19 in a population can be made by random sampling and pooling of RT-PCR tests. Here I use simulations to explore how experiment sample size and degrees of sample pooling impact precision of prevalence estimates and potential for minimizing the total number of tests required to get individual-level diagnostic results.
resultsSample pooling can greatly reduce the total number of tests required for prevalence estimation. In low-prevalence populations, it is theoretically possible to pool hundreds of samples with only marginal loss of precision. Even when the true prevalence is as high as 10% it can be appropriate to pool up to 15 samples. Sample pooling can be particularly beneficial when the test has imperfect specificity by providing more accurate estimates of the prevalence than an equal number of individual-level tests.
conclusionSample pooling should be considered in COVID-19 prevalence estimation efforts.
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