2023 journal article

Group Testing With Side Information via Generalized Approximate Message Passing

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 71, 2366–2375.

By: S. Cao*, R. Goenka*, C. Wong n , A. Rajwade * & D. Baron n 

author keywords: Compressed sensing; contact tracing; generalized approximate message passing (GAMP); nonadaptive group testing
TL;DR: Side information (SI) collected from contact tracing (CT) is incorporated into nonadaptive/single-stage group testing algorithms based on generalized approximate message passing (GAMP) and results show that the GAMP-based algorithms provide improved accuracy. (via Semantic Scholar)
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Added: July 31, 2023

Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m \lt n$</tex-math></inline-formula> pooled samples, where each pool is obtained by mixing a subset of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy.