Department of Population and Quantitative Health Sciences, CaseWestern Reserve University, Cleveland, OH, 44106, USA.
Department of Computer and Data Science, CaseWestern Reserve University, Cleveland, OH, USA.
Biostatistics. 2023 Oct 18;24(4):885-900. doi: 10.1093/biostatistics/kxac004.
A Bayesian framework for group testing under dilution effects has been developed, using lattice-based models. This work has particular relevance given the pressing public health need to enhance testing capacity for coronavirus disease 2019 and future pandemics, and the need for wide-scale and repeated testing for surveillance under constantly varying conditions. The proposed Bayesian approach allows for dilution effects in group testing and for general test response distributions beyond just binary outcomes. It is shown that even under strong dilution effects, an intuitive group testing selection rule that relies on the model order structure, referred to as the Bayesian halving algorithm, has attractive optimal convergence properties. Analogous look-ahead rules that can reduce the number of stages in classification by selecting several pooled tests at a time are proposed and evaluated as well. Group testing is demonstrated to provide great savings over individual testing in the number of tests needed, even for moderately high prevalence levels. However, there is a trade-off with higher number of testing stages, and increased variability. A web-based calculator is introduced to assist in weighing these factors and to guide decisions on when and how to pool under various conditions. High-performance distributed computing methods have also been implemented for considering larger pool sizes, when savings from group testing can be even more dramatic.
已经开发了一种基于格点模型的针对稀释效应下的分组检测的贝叶斯框架。鉴于迫切需要增强 2019 年冠状病毒病和未来大流行的检测能力，以及在不断变化的条件下需要大规模和重复进行监测检测，这项工作具有特殊的意义。所提出的贝叶斯方法允许在分组检测中存在稀释效应，并且允许测试响应分布具有一般的分布，而不仅仅是二项式结果。结果表明，即使在强烈的稀释效应下，一种直观的基于模型阶结构的分组检测选择规则，称为贝叶斯减半算法，具有吸引人的最优收敛特性。还提出并评估了类似的前瞻性规则，这些规则可以通过一次选择多个混合测试来减少分类的阶段数。即使在中等流行水平下，分组检测也被证明在所需的测试数量方面比个体检测具有更大的节省。然而，随着测试阶段数量的增加和变异性的增加，存在着权衡。引入了一个基于网络的计算器来帮助权衡这些因素，并指导在各种条件下何时以及如何进行混合。还实施了高性能分布式计算方法，以考虑更大的池大小，从而可以从分组检测中获得更大的节省。
