  Group testing can save testing resources in the context of the ongoing
COVID-19 pandemic. In group testing, we are given $n$ samples, one per
individual, and arrange them 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 use side
information (SI) collected from contact tracing (CT) within
non-adaptive/single-stage group testing algorithms. We generate data by
incorporating CT SI and characteristics of disease spread between individuals.
These data are fed into two signal and measurement models for group testing,
where numerical results show that our algorithms provide improved sensitivity
and specificity. While Nikolopoulos et al. utilized family structure to improve
non-adaptive group testing, ours is the first work to explore and demonstrate
how CT SI can further improve group testing performance.
