Μax Planck Institute for Software Systems, Kaiserslautern, Germany.
ΙIT Bombay, Mumbai, India.
PLoS Comput Biol. 2022 Mar 28;18(3):e1010008. doi: 10.1371/journal.pcbi.1010008. eCollection 2022 Mar.
Testing is recommended for all close contacts of confirmed COVID-19 patients. However, existing pooled testing methods are oblivious to the circumstances of contagion provided by contact tracing. Here, we build upon a well-known semi-adaptive pooled testing method, Dorfman's method with imperfect tests, and derive a simple pooled testing method based on dynamic programming that is specifically designed to use information provided by contact tracing. Experiments using a variety of reproduction numbers and dispersion levels, including those estimated in the context of the COVID-19 pandemic, show that the pools found using our method result in a significantly lower number of tests than those found using Dorfman's method. Our method provides the greatest competitive advantage when the number of contacts of an infected individual is small, or the distribution of secondary infections is highly overdispersed. Moreover, it maintains this competitive advantage under imperfect contact tracing and significant levels of dilution.
建议对所有确诊 COVID-19 患者的密切接触者进行检测。然而，现有的集中检测方法忽略了接触者追踪提供的感染情况。在这里，我们基于一种著名的半自适应集中检测方法——存在不完美检测的 Dorfman 方法，通过动态规划推导出一种简单的集中检测方法，专门用于利用接触者追踪提供的信息。使用各种繁殖数量和分散水平（包括在 COVID-19 大流行背景下估计的数量）进行的实验表明，使用我们的方法找到的集中检测数量明显少于使用 Dorfman 方法找到的集中检测数量。当感染者的接触者数量较少或二次感染的分布高度过分散时，我们的方法提供了最大的竞争优势。此外，它在不完美的接触者追踪和显著的稀释水平下保持了这种竞争优势。
