Modeling of COVID-19 Transmission Dynamics on US Population: Inter-transfer Infection in Age Groups, Mutant Variants, and Vaccination Strategies

Abstract

The in-depth understanding of the dynamics of COVID-19 transmission among different age groups is of great interest for governments and health authorities so that strategies can be devised to reduce the pandemic s detrimental effects. We developed the SIRDV-Virulence epidemiological model based on a population balance equation to study the effect of mutants of the virus and the effect of vaccination strategies on mitigating the transmission among the population in the United States. Based on the available data from the Centers for Disease Control and Prevention (CDC), we obtain the key parameters governing the dynamic evolution of the spread of the COVID-19 pandemic. In the context studied, the results show that a large fraction of infected cases comes from the adult and children populations in the presence of a mutant variant of COVID-19 with high infection rates. We further investigate the optimum vaccine distribution strategy among different age groups. Given the current situation in the United States, the results show that prioritizing children and adult vaccinations over that of seniors can contain the spread of the active cases, thereby preventing the healthcare system from being overwhelmed and minimizing subsequent deaths. The model suggests that the only option to curb the effects of this pandemic is to reduce the population of unvaccinated individuals. A higher fraction of Anti/Non-vaxxers can lead to the resurgence of the pandemic.