接触者追踪提高 COVID-19 分组检测的效率
Contact Tracing Enhances the Efficiency of COVID-19 Group Testing
AI总结：
本文研究 COVID-19 分组检测，提出在非自适应单阶段算法中融合接触者追踪辅助信息与疾病传播特征，并通过两个信号测量模型验证其可提升灵敏度和特异度。
摘要：
在当前 COVID-19 大流行背景下，分组检测可以节省检测资源。在分组检测中，给定 n 个样本（每人一个），并将它们安排成 m<n 个混合样本，其中每个混合池由 n 个个体样本的一个子集混合而成。随后使用分组检测算法识别受感染个体。本文在非自适应/单阶段分组检测算法中使用通过接触者追踪（CT）收集的辅助信息（SI）。我们通过纳入 CT SI 以及疾病在个体间传播的特征来生成数据。这些数据被输入到两个用于分组检测的信号与测量模型中，数值结果表明，我们的算法提高了灵敏度和特异度。尽管 Nikolopoulos 等人曾利用家庭结构来改进非自适应分组检测，但本工作是首个探索并证明 CT SI 如何进一步提升分组检测性能的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
