论文标题
重叠社区的小组测试
Group testing for overlapping communities
论文作者
论文摘要
在本文中，我们提出的算法利用已知的社区结构使小组测试效率更高。我们考虑一个在互联社区中组织的人群：每个人都参与一个或多个社区，每个人的感染概率取决于他参与的社区。用例包括参加多个课程的学生，以及共享共同空间的工人。小组测试可减少通过汇总诊断样本并一起测试所需的测试数量。我们表明，使测试算法了解社区结构，可以大大减少自适应和非自适应组测试所需的测试数量。
In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in connected communities: each individual participates in one or more communities, and the infection probability of each individual depends on the communities (s)he participates in. Use cases include students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that making testing algorithms aware of the community structure, can significantly reduce the number of tests needed both for adaptive and non-adaptive group testing.
