论文标题
网络组测试
Network Group Testing
论文作者
论文摘要
我们考虑了识别N大小N群体中受感染的个体的问题。我们引入了一种小组测试方法，该方法在感染患病率较低时使用了比N测试明显少得多的问题。小组测试，Dorfman测试，随机分组的最常见方法。但是，随着传染病通过基本的社交网络从个人到个人传播，我们的方法利用网络信息来提高绩效。网络分组（按社区组成的个人分组）弱地主导了Dorfman的测试，以预期的测试数量。网络组的跑性超越取决于网络中社区结构的强度。当网络具有强大的社区结构时，网络分组将实现下的下限，以进行两阶段的测试程序。作为一个经验例子，我们考虑了大学对Covid-19的人口进行测试的情况。使用丹麦大学的社交网络数据，我们证明网络分组需要比Dorfman的测试要少得多。与许多建议的小组测试方法相反，网络分组对于实践者实施非常简单。实际上，个人可以按家庭部门，社会团体或工作组进行分组。
We consider the problem of identifying infected individuals in a population of size N. We introduce a group testing approach that uses significantly fewer than N tests when infection prevalence is low. The most common approach to group testing, Dorfman testing, groups individuals randomly. However, as communicable diseases spread from individual to individual through underlying social networks, our approach utilizes network information to improve performance. Network grouping, which groups individuals by community, weakly dominates Dorfman testing in terms of the expected number of tests used. Network grouping's outperformance is determined by the strength of community structure in the network. When networks have strong community structure, network grouping achieves the lower bound for two-stage testing procedures. As an empirical example, we consider the scenario of a university testing its population for COVID-19. Using social network data from a Danish university, we demonstrate network grouping requires significantly fewer tests than Dorfman. In contrast to many proposed group testing approaches, network grouping is simple for practitioners to implement. In practice, individuals can be grouped by family unit, social group, or work group.
