Adaptive Block Testing

Timo von Oertzen
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Timo von Oertzen: University BW Munich, Germany

No z3q4h, OSF Preprints from Center for Open Science

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%.

Date: 2020-04-23
New Economics Papers: this item is included in nep-gen
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:z3q4h

DOI: 10.31219/osf.io/z3q4h

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