Bayesian group testing with dilution effects.
Bayesian group testing with dilution effects.
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DOI：
10.1093/biostatistics/kxac004
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发表时间：
2023-10-18
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影响因子：
--
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一个贝叶斯框架下的稀释效应的组测试已经开发出来，使用基于格子的模型。鉴于公共卫生迫切需要加强对2019年冠状病毒病和未来大流行病的检测能力，以及需要在不断变化的条件下进行大规模和重复的检测以进行监测，这项工作具有特别重要的意义。所提出的贝叶斯方法允许在组测试中的稀释效应和一般的测试响应分布，而不仅仅是二进制结果。结果表明，即使在强烈的稀释效应，一个直观的组测试选择规则，依赖于模型的阶结构，称为贝叶斯减半算法，具有吸引力的最优收敛性能。提出了类似的前瞻性规则，可以减少分类的阶段数，通过选择几个合并的测试在同一时间，以及评估。团体测试被证明提供了巨大的节省比个人测试所需的测试数量，即使是中等高的患病率水平。然而，有一个权衡与更多的测试阶段，并增加可变性。介绍了一个基于网络的计算器，以帮助权衡这些因素，并指导决定何时以及如何在各种条件下池。高性能的分布式计算方法也已经被实现，以考虑更大的池大小，当组测试的节省可以更显着。
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.
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DOI：
10.1111/j.1467-9876.2011.01008.x
发表时间：
2012-03-01
期刊：
Journal of the Royal Statistical Society. Series C, Applied statistics
影响因子：
--
作者：
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通讯作者：
Tebbs JM
影响因子：
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作者：
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作者：
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DOI：
10.1080/07474946.2014.916931
发表时间：
2014-01-01
影响因子：
0.8
作者：
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通讯作者：
Tatsuoka, Curtis
DOI：
10.1016/0378-3758(89)90061-x
发表时间：
1989-05-01
影响因子：
0.9
作者：
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通讯作者：
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