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

TL;DR

This paper develops a control-theoretic model for optimal testing and vaccination in contact networks, showing targeted strategies that prioritize high-degree nodes can better delay outbreaks and reduce incidence.

Contribution

It introduces a degree-based epidemic control model and derives optimal policies that outperform uniform and reinforcement-learning interventions.

Findings

Optimal policies target high-degree nodes first.

Targeted interventions delay outbreaks more effectively.

Performance surpasses uniform and reinforcement-learning strategies on scale-free networks.

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.