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arXiv · 1911.02287

Optimal group testing

Amin Coja-Oghlan·Oliver Gebhard·Max Hahn-Klimroth·Philipp Loick
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Abstract

In the group testing problem the aim is to identify a small set of $k\sim n^\theta$ infected individuals out of a population size $n$, $0<\theta<1$. We avail ourselves of a test procedure capable of testing groups of individuals, with the test returning a positive result iff at least one individual in the group is infected. The aim is to devise a test design with as few tests as possible so that the set of infected individuals can be identified correctly with high probability. We establish an explicit sharp information-theoretic/algorithmic phase transition $\minf$ for non-adaptive group testing, where all tests are conducted in parallel. Thus, with more than $\minf$ tests the infected individuals can be identified in polynomial time \whp, while learning the set of infected individuals is information-theoretically impossible with fewer tests. In addition, we develop an optimal adaptive scheme where the tests are conducted in two stages.

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Amin Coja-Oghlan, Oliver Gebhard, Max Hahn-Klimroth, Philipp Loick. 2019-11-06. Optimal group testing. https://doi.org/10.1017/s096354832100002x

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