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Adaptive Block Testing

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  • von Oertzen, Timo

    (University BW Munich, Germany)

Abstract

This article introduces Adaptive Block Testing (ABT), a method to test N units for a binary variable with known baseline probability pi for each unit, assuming that a test is available which may take arbitrary number of units and tests negative if all units are negative, and positive otherwise. A proof is given that the current method is optimal up to rounding. ABT is applicable to screen a large population of patients for the presence of the RNA of a virus, as for example the SARS-CoV-2, using block testing by polymerase chain reactions. ABT uses the block tests and adaptively chooses from the pool participants such that the entropy gain in each test is maximized. For a baseline probability of 1% of the tested patients to be sick, the method needs 2.4 times less tests than a block testing method with a block size of 10, the optimal block size for a standard block test at a baseline probability of 1%.

Suggested Citation

  • von Oertzen, Timo, 2020. "Adaptive Block Testing," OSF Preprints z3q4h, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:z3q4h
    DOI: 10.31219/osf.io/z3q4h
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    References listed on IDEAS

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    1. Soma, Nei Yoshihiro & Toth, Paolo, 2002. "An exact algorithm for the subset sum problem," European Journal of Operational Research, Elsevier, vol. 136(1), pages 57-66, January.
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