Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given $n$ samples, one per individual, and arrange them 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 use side information (SI) collected from contact tracing (CT) within non-adaptive/single-stage group testing algorithms. We generate data by incorporating CT SI and characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, where numerical results show that our algorithms provide improved sensitivity and specificity. While Nikolopoulos et al. utilized family structure to improve non-adaptive group testing, ours is the first work to explore and demonstrate how CT SI can further improve group testing performance.
翻译:在进行中的COVID-19大流行情况下,集体测试可以节省测试资源。在集体测试中,我们得到一美元样本,每个个人一份,并将他们安排为一美元 < n$的集合样本,其中每个集合样本是通过混合一组美元个人样本获得的。然后利用一个群体测试算法确定感染者的身份。在本文中,我们使用非适应/单一阶段群体测试算法中从接触跟踪中收集的侧面信息。我们通过纳入CT SI和个人之间疾病传播特征生成数据。这些数据被输入两个用于群体测试的信号和测量模型,其中数字结果显示我们的算法提供了更好的敏感性和特性。虽然Nikolopoul 等人利用家庭结构改进非适应群体测试,但我们是第一个探索和展示CT SI 如何进一步改进群体测试绩效的工作。