论文标题
社区意识小组测试
Community aware group testing
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。我们考虑一个在不相交的社区中组织的人群：每个人都参与一个社区，其感染概率取决于他参与的社区。用例包括家庭，参加几个班级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，如果我们考虑了社区结构的测试策略，我们可以大大减少自适应和非自适应组测试所需的测试数量，并且可以在测试嘈杂的情况下提高可靠性。
In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depends on the community (s)he participates in. Use cases include families, 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 if we design the testing strategy taking into account the community structure, we can significantly reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in cases where tests are noisy.
