Quantifying the benefits of customized vaccination strategies: A network-based optimization approach

Abstract

We study the problem of designing vaccine distribution strategies that maximally mitigate the negative impact of an infectious disease outbreak. This is achieved through a multiperiod optimization-based framework that embeds important subject-specific risk and contact information into the decision-making process. By analyzing the structure of the resulting optimization problem, we identify key structural properties which we use to construct a globally convergent solution scheme (suitable for smaller problem instances) and two, more scalable, heuristic schemes. We demonstrate the benefits of the considered framework through a case study on COVID-19 in Texas. Our results highlight the importance of considering risk and contact information as doing so substantially reduces the total expected number of fatalities over conventional compartmental-based approaches. These findings indicate that customization can have a significant benefit, particularly for community-scale planning.