12874_2020_1081_MOESM3_ESM.pdf (1.03 MB)
Additional file 3 of COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence
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posted on 2020-07-24, 04:00 authored by Ola BrynildsrudAdditional file 3 : Figure S1. Testing for freedom of disease with a test with perfect specificity. The x-axis represents different true levels of p, and the colored lines represent the number of samples associated with 95% probability of having at least one positive sample at that prevalence level. For perfect specificity tests this is commonly interpreted as meaning that we can be 95% certain that the true prevalence is lower. The effects of sample pooling are explored with different color lines. Panel A: Test specificity = 1.0; Panel B: Test specificity = 0.99.
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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
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