Norwegian Institute of Public Health, Oslo, Norway.
Norwegian University of Life Science, Ås, Norway.
BMC Med Res Methodol. 2020 Jul 23;20(1):196. doi: 10.1186/s12874-020-01081-0.
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.
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.
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.
Sample pooling should be considered in COVID-19 prevalence estimation efforts.
用确诊的 COVID-19 病例数除以人口规模，作为人群疾病负担的粗略衡量标准。然而，这个分数在很大程度上取决于采样强度和不同司法管辖区使用的各种测试标准，许多消息来源表明，很大一部分病例往往未被发现。
通过随机抽样和聚合 RT-PCR 测试，可以估计人群中 COVID-19 的真实流行率。在这里，我使用模拟来探讨实验样本量和样本聚合程度如何影响流行率估计的精度以及最小化获得个体水平诊断结果所需的总测试数量的潜力。
样本聚合可以大大减少流行率估计所需的总测试数量。在低流行率人群中，理论上可以聚合数百个样本，而精度仅略有下降。即使真实流行率高达 10%，聚合多达 15 个样本也是合适的。当测试的特异性不完美时，样本聚合特别有益，因为它提供了比同等数量的个体水平测试更准确的流行率估计。
在 COVID-19 流行率估计工作中应考虑样本聚合。
