Group Testing with Side Information via Generalized Approximate Message Passing

TL;DR

This paper enhances group testing efficiency by integrating contact tracing side information into a GAMP-based framework, leading to improved accuracy in identifying infected individuals with fewer tests.

Contribution

It introduces a novel method that incorporates contact tracing data into group testing algorithms using GAMP, improving detection accuracy over traditional methods.

Findings

GAMP-based algorithms outperform existing methods in accuracy.

Incorporating contact tracing side information improves detection performance.

Numerical results validate the effectiveness of the proposed approach.

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.