Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

Abstract

<abstract><p>In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.</p></abstract>

An Epidemic Model with Infection Age and Vaccination Age Structure

“…Notably, another promising direction for enhancement is the integration of chronological age. Data on COVID-19 are often categorized by age groups, and different age brackets might exhibit varied transmission rates [ 126 , 127 , 128 ]. This consideration will be a focus in our subsequent analyses.…”

Prediction of the next major outbreak of COVID-19 in Mainland China and a vaccination strategy for it

“…10: 230655 through exponential functions [16,17] and, by fitting functions to the actual data, the functional expressions of an immune-waning rate that conforms to the reality are included. Compared to most studies where it is directly taken as a constant [33][34][35][36][37][38], the setting in this paper is more realistic. In addition, other function forms involved in this paper, such as the vaccine coverage rate function and the initial value function of vaccination, are fitted with the actual data to obtain the corresponding function expression, which is the highlight of this paper that is different from other similar studies [27][28][29][30][31][32].…”

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

“…This form is well-suited for post-hoc analyses, in which the number of vaccinations that were conducted per unit time (e.g., day or week) is known, see e.g. Islam et al (2021); Gozzi et al (2022); Luebben et al (2023); Kekíc et al (2023); Aruffo et al (2022); Ziarelli et al (2023); Ferreira et al (2022); Cattaneo et al (2022). Mathematically, this form requires careful attention when solving the ODE numerically, to ensure X ( t ) remains non-negative at all time.…”

“…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.…”

“…These studies find that, at R eff = 1.15, elderly should be prioritized when minimizing deaths as long as the vaccine roll-out is sufficiently fast; the speed at which the optimal prioritization switches is country-dependent. Some studies have been even shown that a vaccine with low effectiveness may be detrimental and yield worse outcomes than in the absence of a vaccine, due to behavior adaptation and a false belief of protection (Kadelka and McCombs, 2021;Luebben et al, 2023) (Kiem et al, 2021). In these circumstances, the vulnerable, older part of the population is indirectly protected by the vaccine through herd immunity, pending no changes in NPI adherence.…”