Gonzalez-Parra Gilberto, Mahmud Md Shahriar, Kadelka Claus
Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain.
Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA.
medRxiv. 2024 Mar 7:2024.03.04.24303726. doi: 10.1101/2024.03.04.24303726.
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.
随着世界联系日益紧密,大流行病发生的可能性也在增加。最近的新冠疫情以及同时进行的全球大规模疫苗接种,为我们提供了一个理想的环境,以便从中学习并完善我们对传染病模型的理解,从而更好地为未来做好准备。在这篇综述中,我们系统地分析并分类了为设计针对初始有限疫苗的最佳疫苗优先排序策略而开发的数学模型。由于老年人受新冠疫情的影响尤为严重,因此重点关注那些明确考虑年龄因素的模型。老年人较低的流动性和活动水平带来了重要的权衡。次要研究问题涉及疫苗接种剂量之间的最佳时间间隔以及疫苗的空间分配。这篇综述展示了各种建模假设对模型结果的影响。对这些关系的深入理解将产生更好的传染病模型,进而在下次大流行期间做出更好的公共卫生决策。