Group Testing with Side Information via Generalized Approximate Message Passing

Abstract

Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n 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.

To appear in IEEE Trans. Signal Processing. arXiv admin note: substantial text overlap with arXiv:2106.02699, arXiv:2011.14186

References in corpus (4)

• Low-Cost and High-Throughput Testing of COVID-19 Viruses and Antibodies via Compressed Sensing: System Concepts and Computational Experiments
• Empirical Bayes and Full Bayes for Signal Estimation
• Universality of Approximate Message Passing algorithms and tensor networks
• Model-Based and Graph-Based Priors for Group Testing