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When screening for infectious diseases, group testing has proven to be a cost efficient alternative to individual level testing. Cost savings are realized by testing pools of individual specimens (eg, blood, urine, saliva, and so on) rather than by testing the specimens separately. However, a common concern that arises in group testing is the so‐called “dilution effect.” This occurs if the signal from a positive individual's specimen is diluted past an assay's threshold of detection when it is pooled with multiple negative specimens. In this article, we propose a new statistical framework for group testing data that merges estimation and case identification, which are often treated separately in the literature. Our approach considers analyzing continuous biomarker levels (eg, antibody levels, antigen concentrations, and so on) from pooled samples to estimate both a binary regression model for the probability of disease and the biomarker distributions for cases and controls. To increase case identification accuracy, we then show how estimates of the biomarker distributions can be used to select diagnostic thresholds on a pool‐by‐pool basis. Our proposals are evaluated through numerical studies and are illustrated using hepatitis B virus data collected on a prison population in Ireland.
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On the Number of Tests of the Pooled Testing Halving Scheme
We develop recursions for computing the mean and variance of the number of pooled tests needed by the halving scheme to determine the disease positive members of a population. It is assumed that each population member is independently positive with probability p, that the individual blood samples can be pooled to test whether or not at least one member of that pooled group is positive, and that a positive tested group is then split in half and the process continued.
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- Award ID(s):
- 1662442
- PAR ID:
- 10315409
- Date Published:
- Journal Name:
- Operations research letters
- Volume:
- 49
- Issue:
- 2
- ISSN:
- 0167-6377
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.orl.2021.01.005
