论文标题
使用RT-QPCR进行两阶段自适应合并，用于筛选
Two-Stage Adaptive Pooling with RT-qPCR for COVID-19 Screening
论文作者
论文摘要
我们提出了使用实时逆转录定量聚合酶链反应（RT-QPCR）测试试剂盒检测两阶段自适应合并方案，即2-STAP和2 stamp，用于检测COVID-19。与Ghosh等人的挂毯方案类似，该方案利用RT-QPCR过程中有关库中总病毒负荷的软信息。这与测量值布尔值的常规组测试方案相反。所提出的方案提供的测试吞吐量要比普遍使用的Dorfman方案更高。与最先进的非自适应挂毯方案相比，它们还提供更高的测试吞吐量，灵敏度和特异性。移液操作的数量低于最新的非自适应池计划，并且高于Dorfman计划。所提出的方案的群体大小比非自适应方案的群体大小要小得多，并且易于描述。在Ghosh等人的工作中使用统计模型的蒙特卡洛模拟。 （挂毯）表明，可以通过70.86个测试来识别10名受感染人群中的受感染者，其灵敏度为99.50％，特异性为99.62。这分别为13.5倍，4.24倍和1.3倍，分别是单个测试，Dorfman测试和挂毯方案的测试吞吐量。
We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for detecting COVID-19 using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme of Ghosh et al., the proposed schemes leverage soft information from the RT-qPCR process about the total viral load in the pool. This is in contrast to conventional group testing schemes where the measurements are Boolean. The proposed schemes provide higher testing throughput than the popularly used Dorfman's scheme. They also provide higher testing throughput, sensitivity and specificity than the state-of-the-art non-adaptive Tapestry scheme. The number of pipetting operations is lower than state-of-the-art non-adaptive pooling schemes, and is higher than that for the Dorfman's scheme. The proposed schemes can work with substantially smaller group sizes than non-adaptive schemes and are simple to describe. Monte-Carlo simulations using the statistical model in the work of Ghosh et al. (Tapestry) show that 10 infected people in a population of size 961 can be identified with 70.86 tests on the average with a sensitivity of 99.50% and specificity of 99.62. This is 13.5x, 4.24x, and 1.3x the testing throughput of individual testing, Dorfman's testing, and the Tapestry scheme, respectively.
