Controlling epidemics through optimal allocation of test kits and vaccine doses across networks

Tom Chou, Lucas Böttcher, Mingtao Xia

Publication date: 28 July 2021

Abstract: 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.

Has companion code repository: https://gitlab.com/ComputationalScience/epidemic-control

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