Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups, instead of testing each individual in the population, can be more efficient. In this work, we consider noisy adaptive group testing design with specific test sensitivity and specificity that select the optimal group given previous test results based on pre-selected utility function. As in prior studies on group testing, we model this problem as a sequential Bayesian Optimal Experimental Design (BOED) to adaptively design the groups for each test. We analyze the required number of group tests when using the updated posterior on the infection status and the corresponding Mutual Information (MI) as our utility function for selecting new groups. More importantly, we study how the potential bias on the ground-truth noise of group tests may affect the group testing sample complexity.
翻译:准确检测受感染者是阻止任何大流行病的关键步骤之一。当该疾病的基本感染率较低时,对人群进行检测,而不是对人口中的每个人进行检测,可以提高效率。在这项工作中,我们考虑根据预先选择的实用功能,以特别的测试敏感度和特殊性来选择根据先前测试结果而选择最佳群体的紧张适应性群体测试设计。正如以往关于群体测试的研究一样,我们将此问题作为按顺序排列的贝叶西亚最佳实验设计模型(BOED)来为每次测试的人群进行适应性设计。我们在使用最新的感染状况远地点和相应的相互信息(MI)作为我们选择新群体的实用功能时,分析所需的群体测试数量。更重要的是,我们研究在群体测试的地面真实性噪音方面的潜在偏差会如何影响小组测试样本的复杂性。