论文标题
COVID-19测试的小组测试架构的比较
A comparison of group testing architectures for COVID-19 testing
论文作者
论文摘要
每个国家的Covid -19响应的重要组成部分是快速有效的测试 - 识别和隔离病例，以及早期发现当地热点。对于许多国家来说，进行足够数量的测试是他们控制Covid-19感染的严重限制因素。小组测试是一种完善的数学工具，可以提供大量且廉价的测试能力扩展。在本说明中，我们比较了COVID-19的QPCR测试中的几种流行组测试方案。我们发现，在实用环境中，对于识别Covid-19的个体，Dorfman测试是多达30％的患病率的最佳选择，而对于总体人口中的Covid-19患病率估计，Gibbs-Gower测试是固定且相对较少的测试的最佳患者的最佳选择。例如，在多达2％的患病率下，Dorfman测试的效率增长为3.5--8；在1％的患病率下，吉布斯 - 吉尔测试的效率增长也为18，即使将池尺寸限制为可行的数字。 该注释旨在作为实施组测试方法的实验室的有用手册。
An important component of every country's COVID-19 response is fast and efficient testing - to identify and isolate cases, as well as for early detection of local hotspots. For many countries, producing a sufficient number of tests has been a serious limiting factor in their efforts to control COVID-19 infections. Group testing is a well-established mathematical tool, which can provide a substantial and inexpensive expansion of testing capacity. In this note, we compare several popular group testing schemes in the context of qPCR testing for COVID-19. We find that in practical settings, for identification of individuals with COVID-19, Dorfman testing is the best choice at prevalences up to 30%, while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing is the best choice at prevalences up to 30% given a fixed and relatively small number of tests. For instance, at a prevalence of up to 2%, Dorfman testing gives an efficiency gain of 3.5--8; at 1% prevalence, Gibbs-Gower testing gives an efficiency gain of 18, even when capping the pool size at a feasible number . This note is intended as a helpful handbook for labs implementing group testing methods.
