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

13 pages, 8 figures, Submitted to IEEE Transactions on Network Science and Engineering

References in corpus (4)

• Implicit energy regularization of neural ordinary-differential-equation control
• Generation of arbitrarily two-point correlated random networks
• Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions
• Switchover phenomenon induced by epidemic seeding on geometric networks

Cited by in corpus (2)

• Impact of random and targeted disruptions on information diffusion during outbreaks
• Dynamical processes on metric networks