This paper is motivated by the occurrence of vaccine nationalism in the setting of pandemics. Certain high-income countries (HICs) aggressively accumulated vaccinations while showing little concern for the vaccination challenges faced by low- and middle- income countries. This disparity fosters the proliferation and mutation of viruses, thus risking the global population’s health and welfare. Hence, we create a data-driven framework to tackle this humanitarian problem by facilitating the provision of vaccines. The framework comprises of two models: a network model named multi-strain Susceptible–Vaccinated–Infected–Removed–Susceptible and a vaccine distribution model with equitable constraints. The latter also encompasses the diverse uncertainty associated with vaccination hesitancy in different countries, in order to avoid potential wastage of resources. The vaccine distribution from our framework is based on greedy thought, thus enabling decision-makers to actively engage in the real-time vaccine allocation process. When the suggested framework is applied to the scenario of the COVID-19 pandemic, the simulation results indicate that fair distributions could accelerate the end of the pandemic. Additional scenarios, such as equitable levels and traveling intensity, are also examined in the sensitivity analysis. The progression of the epidemic under vaccine nationalism is moreover simulated to highlight its harmfulness and validate the efficacy of our framework. We demonstrate that the inequitable advantage experienced by HICs is temporary, as HICs are bound to suffer from virus variants in due course when vaccinations become less efficacious against them.

中文翻译：

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在人道主义视角下支持公平的全球疫苗分配的决策框架

本文的动机是在大流行病背景下发生疫苗民族主义。某些高收入国家 （HIC） 积极积累疫苗接种，同时对低收入和中等收入国家面临的疫苗接种挑战漠不关心。这种差异促进了病毒的增殖和突变，从而危及全球人口的健康和福利。因此，我们创建了一个数据驱动的框架，通过促进疫苗的供应来解决这一人道主义问题。该框架由两个模型组成：一个名为多菌株 Susceptible-Vaccinated-Infected-Removed-Susceptible 的网络模型和一个具有公平约束的疫苗分发模型。后者还包括与不同国家/地区的疫苗接种犹豫相关的各种不确定性，以避免潜在的资源浪费。我们框架的疫苗分发基于贪婪的想法，因此使决策者能够积极参与实时疫苗分配过程。当建议的框架应用于 COVID-19 大流行的情景时，模拟结果表明公平分配可以加速大流行的结束。敏感性分析还检查了其他情况，例如公平水平和旅行强度。此外，还模拟了疫苗民族主义行病的进展，以突出其危害性并验证我们框架的有效性。我们证明，HIC 所经历的不公平优势是暂时的，因为当疫苗接种对 HIC 的疗效降低时，HIC 必然会在适当的时候遭受病毒变种的影响。