12874_2020_1081_MOESM1_ESM.xls (408.5 kB)
Additional file 1 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 1 : Supplementary Table 1. Table containing prevalence estimates and, the estimated required number of tests, and the expected proportion incorrectly classified patients for all parameter combinations. Se = sensitivity. Sp = specificity. N = number of samples. k = pooling level. P = true prevalence. p 2.5%, p 50.0%, p 97.5% = 2.5, 50 and 97.5 quantile of estimated prevalence. T 2.5%, T 50.0%, T 97.5% = 2.5, 50 and 97.5 quantile of estimated number of tests required to get individual-level diagnoses. E(S) = Expected number of tests saved when compared to testing individually for this N. E(inc) = Expected percentage of patients that are diagnosed incorrectly at this parameter combination. [Excel file].
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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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