Vaccination strategies in structured populations under partial immunity and reinfection

Abstract

Optimal protocols of vaccine administration to minimize the effects of infectiousdiseases depend on a number of variables that admit different degrees of control.Examples include the characteristics of the disease and how it impacts on differentgroups of individuals as a function of sex, age or socioeconomic status, its transmissionmode, or the demographic structure of the affected population. Here we introduce acompartmental model of infection propagation with vaccination and reinfection andanalyse the effect that variations on the rates of these two processes have on theprogression of the disease and on the number of fatalities. The population is split intotwo groups to highlight the overall effects on disease caused by different relationshipsbetween vaccine administration and various demographic structures. As a practicalexample, we study COVID-19 dynamics in various countries using real demographicdata. The model can be easily applied to any other disease and demographic structurethrough a suitable estimation of parameter values. Two main conclusions stand out.First, the higher the fraction of reinfected individuals, the higher the likelihood that thedisease becomes quasi-endemic. Second, optimal vaccine roll-out depends ondemographic structure and disease fatality, so there is no unique vaccination protocol,valid for all countries, that minimizes the effects of a specific disease. Simulations of thegeneral model can be carried out at this interactive webpage [1].

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

“…Many infectious disease models explicitly include vaccine hesitancy by assuming that a proportion of each sub-population cannot be vaccinated. Some models simply assume that this proportion is fixed (Bubar et al, 2021; Gavish and Katriel, 2022; Islam et al, 2021; Li et al, 2022; Luebben et al, 2023; Kadelka et al, 2022; Makhoul et al, 2020; Miura et al, 2021; Moore et al, 2021a; Rodriguez-Maroto et al, 2023; Tatapudi et al, 2021; Walker et al, 2022; Hogan et al, 2021; McBryde et al, 2021; Rahmandad, 2022), while others account for lower hesitancy among older individuals (Liu et al, 2022a; Moghadas et al, 2021; Han et al, 2021; Liu et al, 2022b; Moore et al, 2021b; Zavrakli et al, 2023). Most models in the latter category differentiate hesitancy using a binary age threshold.…”