  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.
