论文标题
接触跟踪提高了COVID-19组测试的效率
Contact Tracing Enhances the Efficiency of COVID-19 Group Testing
论文作者
论文摘要
小组测试可以在正在进行的COVID-19大流行的背景下节省测试资源。在小组测试中，我们将获得$ n $样本，每个人一个人，并将它们排列为$ m <n $ plimed样品，每个池都是通过混合$ n $单个样本的子集来获得的。然后使用组测试算法确定感染的个体。在本文中，我们使用非自适应/单阶段组测试算法中从触点跟踪（CT）收集的侧面信息（SI）。我们通过合并CT SI和个体之间疾病传播的特征来生成数据。这些数据被馈送到两个信号和小组测试的测量模型中，其中数值结果表明我们的算法提供了提高的灵敏度和特异性。而Nikolopoulos等。我们利用家庭结构来改善非自适应小组测试，我们的工作是探索和证明CT SI如何进一步改善小组测试绩效的第一项工作。
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.
