Community aware group testing
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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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