Section for Science of Complex Systems, Medical University of Vienna, Vienna, Austria.
Complexity Science Hub Vienna, Vienna, Austria.
PLoS One. 2020 Nov 4;15(11):e0240652. doi: 10.1371/journal.pone.0240652. eCollection 2020.
In the current COVID19 crisis many national healthcare systems are confronted with an acute shortage of tests for confirming SARS-CoV-2 infections. For low overall infection levels in the population the pooling of samples can drastically amplify the testing capacity. Here we present a formula to estimate the optimal group-size for pooling, the efficiency gain (tested persons per test), and the expected upper bound of missed infections in pooled testing, all as a function of the population-wide infection levels and the false negative/positive rates of the currently used PCR tests. Assuming an infection level of 0.1% and a false negative rate of 2%, the optimal pool-size is about 34, and an efficiency gain of about 15 tested persons per test is possible. For an infection level of 1% the optimal pool-size is 11, the efficiency gain is 5.1 tested persons per test. For an infection level of 10% the optimal pool-size reduces to about 4, the efficiency gain is about 1.7 tested persons per test. For infection levels of 30% and higher there is no more benefit from pooling. To see to what extent replicates of the pooled tests improve the estimate of the maximal number of missed infections, we present results for 1 to 5 replicates.
在当前的 COVID19 危机中，许多国家的医疗体系都面临着确认 SARS-CoV-2 感染的检测试剂严重短缺的问题。对于人群中总体感染水平较低的情况，样本的合并可以极大地提高检测能力。在这里，我们提出了一个公式，可以估计最佳的合并样本量，效率增益（每个测试检测到的人数），以及合并测试中错过的感染的预期上限，所有这些都作为人群感染水平和当前使用的 PCR 测试的假阴性/阳性率的函数。假设感染水平为 0.1%，假阴性率为 2%，最佳的合并样本量约为 34，每个测试可增加约 15 个检测对象。对于感染水平为 1%，最佳的合并样本量为 11，效率增益为每个测试检测到 5.1 个对象。对于感染水平为 10%，最佳的合并样本量减少到约 4，效率增益约为每个测试检测到 1.7 个对象。对于感染水平为 30%及更高水平，合并检测不再有优势。为了了解合并测试的重复次数在多大程度上提高了对错过的最大感染数量的估计，我们给出了 1 到 5 个重复的结果。
