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Commun Stat Theory Methods. 2011 Jan 1;40(3):436-448.

Optimal Configuration of a Square Array Group Testing Algorithm.

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1
Department of Biostatistics, University of North Carolina at Chapel Hill.

Abstract

We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi.

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