Abstract：
This research article presents a comprehensive analysis aimed at enhancing the stochastic modeling of COVID-19 dynamics by incorporating vaccination classes through a higher-order spectral scheme. The ongoing COVID-19 pandemic has underscored the critical need for accurate and adaptable modeling techniques to inform public health interventions. In this study, we introduce a novel approach that integrates various vaccination classes into a stochastic model to provide a more nuanced understanding of disease transmission dynamics. We employ a higher-order spectral scheme to capture complex interactions between different population groups, vaccination statuses, and disease parameters. Our analysis not only enhances the predictive accuracy of COVID-19 modeling but also facilitates the exploration of various vaccination strategies and their impact on disease control. The findings of this study hold significant implications for optimizing vaccination campaigns and guiding policy decisions in the ongoing battle against the COVID-19 pandemic.
摘要：
这篇研究文章提出了一项全面的分析，旨在通过高阶光谱方案纳入疫苗接种类别来增强COVID-19动力学的随机建模。持续的COVID-19大流行强调了对准确和适应性强的建模技术的迫切需要，以告知公共卫生干预措施。在这项研究中,我们引入了一种新的方法，将各种疫苗接种类别整合到一个随机模型中，以提供对疾病传播动态的更细致的理解。我们采用更高阶的频谱方案来捕获不同种群之间的复杂相互作用，疫苗接种状态，和疾病参数。我们的分析不仅提高了COVID-19模型的预测准确性，而且有助于探索各种疫苗接种策略及其对疾病控制的影响。这项研究的结果对优化疫苗接种活动和指导正在进行的对抗COVID-19大流行的政策决策具有重要意义。
