Tatsuoka Curtis, Chen Weicong, Lu Xiaoyi
medRxiv. 2021 Dec 26:2021.01.15.21249894. doi: 10.1101/2021.01.15.21249894.
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 COVID-19 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.
已经开发出一种基于格点模型的贝叶斯框架，用于在稀释效应下进行分组测试。鉴于迫切的公共卫生需求，即提高对新冠病毒和未来大流行病的检测能力，以及在不断变化的条件下进行大规模重复监测检测的需求，这项工作具有特殊的相关性。所提出的贝叶斯方法允许在分组测试中考虑稀释效应，并适用于除二元结果之外的一般测试响应分布。结果表明，即使在强稀释效应下，一种基于模型阶结构的直观分组测试选择规则（称为贝叶斯二分算法）也具有吸引人的最优收敛特性。还提出并评估了类似的前瞻规则，这些规则可以通过一次选择多个混合测试来减少分类阶段的数量。结果表明，即使对于中等偏高的流行率水平，分组测试在所需测试数量上也比单独测试节省很多。然而，这与更多的测试阶段和更大的变异性之间存在权衡。引入了一个基于网络的计算器，以帮助权衡这些因素，并指导在各种条件下何时以及如何进行混合的决策。还实施了高性能分布式计算方法，以考虑更大的混合样本量，此时分组测试的节省效果可能会更加显著。
