Asymptotic Analysis of Optimal Vaccination Policies

The effect of heterogeneity of relative vaccine costs on the mean population vaccination rate with mpox as an example

Mpox (formerly known as monkeypox) is a neglected tropical disease that became notorious during its 2022-2023 worldwide outbreak. The vaccination was available, but there were inequities in vaccine access. In this paper, we extend existing game-theoretic models to study a population that is heterogeneous in the relative vaccination costs. We consider a population with two groups. We determine the Nash equilibria (NE), i.e., optimal vaccination rates, for each of the groups. We show that the NE always exists and that, for a narrow range of parameter values, there can be multiple NEs. We focus on comparing the mean optimal vaccination rate in the heterogeneous population with the optimal vaccination rate in the corresponding homogeneous population. We show that there is a critical size for the group with lower relative costs and the mean optimal vaccination in the heterogeneous population is more than in the homogeneous population if and only if the group is larger than the critical size.

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

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Optimal vaccination strategies for imperfect vaccines and variable host susceptibility

I present a method to allocate a given number of vaccines to members of a population who differ in their susceptibility to the disease so that the final size of the epidemic is minimised. I consider an arbitrary distribution of protection that the vaccine confers, including the extreme cases of leaky and all-or-none vaccines. The optimal vaccination policy depends on the distribution of protection. While for low values of the basic reproduction number R0 the optimal policy prioritises the most susceptible hosts, I show that for almost any distribution the order of priority reverses and the least susceptible hosts should be vaccinated when R0 is high. The exception where this does not happen is the all-or-none vaccine. However, even a small deviation from the ideal all-or-none distribution can imply that the limited number of vaccines should be given to less susceptible hosts already at realistic values of R0.

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

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Optimality of Maximal-Effort Vaccination

It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible-Infected-Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models.