2023 journal article
Group Testing With Side Information via Generalized Approximate Message Passing
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 71, 2366–2375.
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)
open access via arxiv.org (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science; OpenAlex)
Source: Web Of Science
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><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><tex-math notation="LaTeX">$m < n$</tex-math></inline-formula> pooled samples, where each pool is obtained by mixing a subset of the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> individual samples. Infected individuals are then identified using a group testing algorithm. In this article, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of 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.