Planning for the optimal vaccination sequence in the context of a population-stratified model

Abstract

We explore strategies for optimally controlling the pandemic through practical vaccination planning across different subpopulations, with the aiming of striking a balance between minimizing mortalities and conserving vaccines. To achieve this objective, the total population is categorized into four subpopulations: health workers, young individuals, middle-aged individuals, and the elderly, based on immunity and exposure risk. We assume a heterogeneous pattern of social contacts among these subpopulations. Accordingly, we formulate a population-stratified SPMILHRD model with 32 compartments that accounts for different statuses of detection and symptoms to define the epidemic dynamics. Numerical simulations demonstrate a staged optimization-based, population-stratified vaccination approach, where health workers, the middle-aged, the elderly, and the young receive priority in succession. The analysis reveals that our optimal vaccination control strategy can significantly reduce the number of infections, including fatalities, compared to scenarios involving proportional policies or no control in the numerical experiment. It has demonstrated that well-designed vaccination planning could substantially curb dissemination and reduce mortality. The findings of this study are instructive for the potential implementation of future vaccine administration.