论文标题
通过广义近似消息传递通过侧面信息进行小组测试
Group Testing with Side Information via Generalized Approximate Message Passing
论文作者
论文摘要
小组测试可以在大流行中使用更少的资源来帮助维持广泛的测试计划。在小组测试设置中，我们获得了n个样本，每个样本一个。每个人都被感染或未感染。这些样品被排列到M <n合并样品中，其中每个池都是通过混合n个单独样品的子集获得的。然后使用组测试算法确定感染的个体。在本文中，我们将从接触跟踪（CT）收集的侧面信息（SI）中纳入了非适应性/单阶段组测试算法中。我们通过纳入疾病传播的不同可能的特征来生成不同类型的CT SI数据。这些数据基于广义近似消息传递（GAMP），将这些数据馈入组测试框架。数值结果表明，我们的基于GAMP的算法提供了提高的精度。
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy.
