Vaccination Schedule under Conditions of Limited Vaccine Production Rate

Abstract

The paper is devoted to optimal vaccination scheduling during a pandemic to minimize the probability of infection. The recent COVID-19 pandemic showed that the international community is not properly prepared to manage a crisis of this scale. Just after the vaccines had been approved by medical agencies, the policymakers needed to decide on the distribution strategy. To successfully fight the pandemic, the key is to find the equilibrium between the vaccine distribution schedule and the available supplies caused by limited production capacity. This is why society needs to be divided into stratified groups whose access to vaccines is prioritized. Herein, we present the problem of distributing protective actions (i.e., vaccines) and formulate two mixed-integer programs to solve it. The problem of distributing protective actions (PDPA) aims at finding an optimal schedule for a given set of social groups with a constant probability of infection. The problem of distributing protective actions with a herd immunity threshold (PDPAHIT) also includes a variable probability of infection, i.e., the situation when herd immunity is obtained. The results of computational experiments are reported and the potential of the models is illustrated with examples.

A decision support system for scheduling a vaccination campaign during a pandemic emergency: The COVID-19 case

“…A data-driven model of COVID-19 transmission to deal with the vaccination prioritization problem has been proposed for the Chinese context by Han et al (2021) . The problems of choosing the best candidates for the vaccination not only on the basis of age has been considered by Książek et al (2022) , who propose two MILPs, one focusing on the social groups and the second one on territorial units.…”