实施分步合并测试以快速检测COVID-19
Implementing Stepped Pooled Testing for Rapid COVID-19 Detection
摘要：
COVID-19是一种病毒性呼吸道大流行病，已迅速蔓延至全球。需要对人群进行大规模和快速的检测，以控制疾病，但这种检测在资源、成本和时间方面令人望而却步。最近，基于RT-PCR的混合检测已成为提高检测效率的一种有前途的方法。我们介绍了一个逐步汇集测试策略，概率驱动的方法，显着减少了测试的数量，需要在一个大的人口中识别感染的个人。我们的综合方法结合了假阴性和假阳性率的影响，不仅可以准确地确定合并的效率，还可以确定其准确性。在各种合理的情况下，我们表明，这种方法显着降低了测试的成本，也降低了有效的假阳性率的测试相比，一个战略的测试人口中的每一个人。我们还概述了一个优化策略，以获得池的大小，最大限度地提高效率的池给定的诊断协议参数和当地的感染条件。
英文摘要：
COVID-19, a viral respiratory pandemic, has rapidly spread throughout the globe. Large scale and rapid testing of the population is required to contain the disease, but such testing is prohibitive in terms of resources, cost and time. Recently RT-PCR based pooled testing has emerged as a promising way to boost testing efficiency. We introduce a stepped pooled testing strategy, a probability driven approach which significantly reduces the number of tests required to identify infected individuals in a large population. Our comprehensive methodology incorporates the effect of false negative and positive rates to accurately determine not only the efficiency of pooling but also it's accuracy. Under various plausible scenarios, we show that this approach significantly reduces the cost of testing and also reduces the effective false positive rate of tests when compared to a strategy of testing every individual of a population. We also outline an optimization strategy to obtain the pool size that maximizes the efficiency of pooling given the diagnostic protocol parameters and local infection conditions.
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