Screening is a powerful tool for infection control, allowing for infectious individuals, whether they be symptomatic or asymptomatic, to be identified and isolated. The resource burden of regular and comprehensive screening can often be prohibitive, however. One such measure to address this is pooled testing, whereby groups of individuals are each given a composite test; should a group receive a positive diagnostic test result, those comprising the group are then tested individually. Infectious disease is spread through a transmission network, and this paper shows how assigning individuals to pools based on this underlying network can improve the efficiency of the pooled testing strategy, thereby reducing the resource burden. We designed a simulated annealing algorithm to improve the pooled testing efficiency as measured by the ratio of the expected number of correct classifications to the expected number of tests performed. We then evaluated our approach using an agent-based model designed to simulate the spread of SARS-CoV-2 in a school setting. Our results suggest that our approach can decrease the number of tests required to regularly screen the student body, and that these reductions are quite robust to assigning pools based on partially observed or noisy versions of the network.

中文翻译：

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利用网络结构提高集中测试效率

筛查是控制感染的有力工具，可以识别和隔离感染者，无论他们是有症状还是无症状。然而，定期和全面筛查的资源负担往往令人望而却步。解决这一问题的措施之一是集中测试，即对每组个体进行综合测试；如果某个群体收到阳性诊断检测结果，则该群体中的成员将分别接受检测。传染病是通过传播网络传播的，本文展示了如何将个体分配到基于该底层网络的池中，可以提高池检测策略的效率，从而减轻资源负担。我们设计了一种模拟退火算法来提高合并测试效率，该效率通过正确分类的预期数量与执行的测试的预期数量的比率来衡量。然后，我们使用基于代理的模型评估了我们的方法，该模型旨在模拟 SARS-CoV-2 在学校环境中的传播。我们的结果表明，我们的方法可以减少定期筛选学生群体所需的测试数量，并且这些减少对于基于部分观察或嘈杂的网络版本分配池来说非常稳健。