  Efficient testing and vaccination protocols are critical aspects of epidemic
management. To study the optimal allocation of limited testing and vaccination
resources in a heterogeneous contact network of interacting susceptible,
recovered, and infected individuals, we present a degree-based testing and
vaccination model for which we use control-theoretic methods to derive optimal
testing and vaccination policies. Within our framework, we find that optimal
intervention policies first target high-degree nodes before shifting to
lower-degree nodes in a time-dependent manner. Using such optimal policies, it
is possible to delay outbreaks and reduce incidence rates to a greater extent
than uniform and reinforcement-learning-based interventions, particularly on
certain scale-free networks.

Populations and Evolution;Social and Information Networks;Optimization and Control