  Group testing can help maintain a widespread testing program using fewer
resources amid a pandemic. In group testing, we are given $n$ samples, one per
individual. 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 use side information (SI) collected from contact tracing (CT) within
nonadaptive/single-stage group testing algorithms. We generate CT SI data by
incorporating characteristics of disease spread between individuals. These data
are fed into two signal and measurement models for group testing, and numerical
results show that our algorithms provide improved sensitivity and specificity.
We also show how to incorporate CT SI into the design of the pooling matrix.
That said, our numerical results suggest that the utilization of SI in the
pooling matrix design based on the minimization of a weighted coherence measure
does not yield significant performance gains beyond the incorporation of SI in
the group testing algorithm.
