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.

中文翻译：

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使用 RT-qPCR 进行 COVID-19 筛选的两阶段自适应池

我们提出了两阶段自适应池方案，2-STAP 和 2-STAMP，用于使用实时逆转录定量聚合酶链反应 (RT-qPCR) 测试套件检测 COVID-19。与 Ghosh 等人的 Tapestry 方案类似，所提出的方案利用来自 RT-qPCR 过程的关于池中总病毒载量的软信息。这与测量是布尔值的常规组测试方案形成对比。所提出的方案比普遍使用的 Dorfman 方案提供更高的测试吞吐量。它们还提供比最先进的非自适应 Tapestry 方案更高的测试吞吐量、灵敏度和特异性。移液操作的数量低于最先进的非自适应池方案，但高于 Dorfman 方案。与非自适应方案相比，所提出的方案可以在更小的组规模下工作，并且易于描述。在 Ghosh 等人的工作中使用统计模型的 Monte-Carlo 模拟。（Tapestry）显示，在 961 人的人口中，平均可以通过 70.86 次测试识别出 10 名感染者，灵敏度为 99.50%，特异性为 99.62。这分别是单个测试、Dorfman 测试和 Tapestry 方案的测试吞吐量的 13.5 倍、4.24 倍和 1.3 倍。