Author: Nikolopoulos, Pavlos; Guo, Tao; Fragouli, Christina; Diggavi, Suhas
Title: Community aware group testing Cord-id: raprhr6l Document date: 2020_7_16
ID: raprhr6l
Snippet: Group testing pools together diagnostic samples to reduce the number of tests needed to identify infected members in a population. The observation we make in this paper is that we can leverage a known community structure to make group testing more efficient. For example, if $n$ population members are partitioned into $F$ families, then in some cases we need a number of tests that increases (almost) linearly with $k_f$, the number of families that have at least one infected member, as opposed to
Document: Group testing pools together diagnostic samples to reduce the number of tests needed to identify infected members in a population. The observation we make in this paper is that we can leverage a known community structure to make group testing more efficient. For example, if $n$ population members are partitioned into $F$ families, then in some cases we need a number of tests that increases (almost) linearly with $k_f$, the number of families that have at least one infected member, as opposed to $k$, the total number of infected members. We show that taking into account community structure allows to reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in the case where tests are noisy.
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