BAYESIAN GROUP TESTING WITH DILUTION EFFECTS
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.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2021 |
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| Enthalten in: |
medRxiv : the preprint server for health sciences - (2021) vom: 26. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tatsuoka, Curtis [VerfasserIn] |
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| Anmerkungen: |
Date Revised 18.10.2023 published: Electronic UpdateIn: Biostatistics. 2022 Apr 11;:. - PMID 35403204 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2021.01.15.21249894 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM320632873 |
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| 500 | |a Date Revised 18.10.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a UpdateIn: Biostatistics. 2022 Apr 11;:. - PMID 35403204 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
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| 700 | 1 | |a Chen, Weicong |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Xiaoyi |e verfasserin |4 aut | |
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