Additional file 1 of COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence

Additional 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].

附加文件1：补充表1。该表格涵盖了所有参数组合下的患病率估计值、估算所需检测数，以及被错误分类的患者预期比例。其中，Se为灵敏度（Sensitivity），Sp为特异度（Specificity），N为样本量（number of samples），k为混样检测水平（pooling level），P为真实患病率（true prevalence）。p 2.5%、p 50.0%、p 97.5%分别代表估算患病率的2.5%、50%和97.5%分位数；T 2.5%、T 50.0%、T 97.5%分别代表获得个体水平诊断所需检测数的2.5%、50%和97.5%分位数。E(S)为相较于单样本逐个检测，当前N值下节省的预期检测数。E(inc)为该参数组合下被误诊的患者预期百分比。[Excel文件]