Optimal Risk-Based Group Testing
Optimal Risk-Based Group Testing
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DOI：
10.1287/mnsc.2018.3138
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发表时间：
2019-09-01
影响因子：
5.4
通讯作者：
Bish, Ebru K.
中科院分区：
文献类型：
--
作者：
Aprahamian, Hrayer;Bish, Douglas R.;Bish, Ebru K.
群体检测（即，用单一检测同时检测多个受试者）对于将大量受试者分类为二元特征（例如，存在某种疾病）的阳性或阴性至关重要。在考虑分类准确性、效率和公平性目标的情况下，研究了受试者特定风险特征和不完善测试下的最优群体测试设计，并描述了最优测试设计的重要结构特性。这些属性允许我们将测试设计问题建模为划分问题，开发有效的算法，并获得公平与准确性权衡的见解。我们的一个模型简化为一个约束最短路径问题，对于其中的一个特殊情况，我们开发了一个多项式时间算法。我们还表明，确定一个最优的基于风险的多夫曼测试方案，最小化测试的预期数量是可处理的，解决了一个开放的猜想。我们以公共健康筛查为例，论证了基于风险的最佳检测方案的价值。
Group testing (i.e., testing multiple subjects simultaneously with a single test) is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease). We study optimal group testing designs under subject-specific risk characteristics and imperfect tests, considering classification accuracy-, efficiency- and equity-based objectives, and characterize important structural properties of optimal testing designs. These properties allow us to model the testing design problems as partitioning problems, develop efficient algorithms, and derive insights on equity versus accuracy trade-off. One of our models reduces to a constrained shortest path problem, for a special case of which we develop a polynomial-time algorithm. We also show that determining an optimal risk-based Dorfman testing scheme that minimizes the expected number of tests is tractable, resolving an open conjecture. We demonstrate the value of optimal risk-based testing schemes with a case study of public health screening.