Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
For public health officials managing epidemics with limited resources, this provides a principled control-theoretic framework for allocating test kits and vaccines across networks.
The paper derives optimal testing and vaccination policies for heterogeneous contact networks, showing that targeting high-degree nodes first then shifting to lower-degree nodes delays outbreaks and reduces incidence rates more than uniform or RL-based interventions, especially on scale-free networks.
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.