Group Testing With Side Information via Generalized Approximate Message Passing

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 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.