  We consider the problem of identifying infected individuals in a population
of size N. We introduce a group testing approach that uses significantly fewer
than N tests when infection prevalence is low. The most common approach to
group testing, Dorfman testing, groups individuals randomly. However, as
communicable diseases spread from individual to individual through underlying
social networks, our approach utilizes network information to improve
performance. Network grouping, which groups individuals by community, weakly
dominates Dorfman testing in terms of the expected number of tests used.
Network grouping's outperformance is determined by the strength of community
structure in the network. When networks have strong community structure,
network grouping achieves the lower bound for two-stage testing procedures. As
an empirical example, we consider the scenario of a university testing its
population for COVID-19. Using social network data from a Danish university, we
demonstrate network grouping requires significantly fewer tests than Dorfman.
In contrast to many proposed group testing approaches, network grouping is
simple for practitioners to implement. In practice, individuals can be grouped
by family unit, social group, or work group.
