COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence
Posted on 2020-07-24 - 04:01
Abstract Background The 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. Methods Estimates 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. Results Sample 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. Conclusion Sample pooling should be considered in COVID-19 prevalence estimation efforts.
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Brynildsrud, Ola (2020). COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5072054.v1
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AUTHORS (1)
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Ola Brynildsrud
KEYWORDS
COVID -19Conclusion Samplesampling intensityprevalence Abstract BackgroundResults Sampleimpact precisionprevalence estimationRT-PCR testslow-prevalence populationsprevalence estimatesexperiment sample sizeCOVID -19 casesCOVID -19 prevalence estimation effortspool hundredsindividual-level testspopulation sizetest criteria15 samplesuse simulationsCOVID -19 prevalence estimationMethods Estimates