Author: Tatsuoka, C.; Chen, W.
Title: BAYESIAN GROUP TESTING WITH DILUTION EFFECTS Cord-id: efn2zdq6 Document date: 2021_1_20
ID: efn2zdq6
Snippet: A Bayesian framework for group testing under dilution effects is intro- duced. This work has particular relevance given the pressing public health need to enhance testing capacity for COVID-19, and the need for wide- scale and repeated testing for surveillance. The proposed Bayesian ap- proach allows for dilution effects in group testing and for general test re- sponse distributions beyond just binary outcomes. It is shown that even with strong dilution effects, an intuitive and simple-to-implem
Document: A Bayesian framework for group testing under dilution effects is intro- duced. This work has particular relevance given the pressing public health need to enhance testing capacity for COVID-19, and the need for wide- scale and repeated testing for surveillance. The proposed Bayesian ap- proach allows for dilution effects in group testing and for general test re- sponse distributions beyond just binary outcomes. It is shown that even with strong dilution effects, an intuitive and simple-to-implement group testing selection rule, referred to as the Bayesian halving algorithm, has attractive optimal properties. A web-based calculator is introduced to assist and guide decisions on when and how to pool under various condi- tions.
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