Assessing the Impact of Vaccination Strategies on COVID-19 Dynamics Via Time-varying Copulas

Abstract

The COVID-19 pandemic has presented an unprecedented global challenge, significantly impacting public health, economies, and daily life worldwide. In response to this crisis, scientists and researchers around the world have worked tirelessly to understand the virus, its transmission dynamics, and most importantly, to develop effective vaccines. However, questions remain about the comparative effectiveness of these vaccines, particularly in real-world scenarios. This paper aimed to address this critical issue by employing advanced statistical techniques, including time-varying copulas. In this study, we employed four types of copula, namely Gaussian, Student-t, Clayton, and Gumbel, to model the temporal association between vaccination and the emergence of COVID-19 cases. The results of this study suggested that the number of subsequent vaccinations, especially the first to fourth vaccines, has a significant impact on the occurrence and spread of COVID-19 under particular conditions. Furthermore, we proposed several modeling scenarios for COVID-19 cases based on their temporal interactions with the number of subsequent vaccinations. The findings of this research provided a comprehensive understanding of the temporal relationship between vaccination and its impact on reducing COVID-19 cases.