COVID-19 Transmission Dynamics in Afghanistan: Insights from Compartmental Models on Vaccination and Control
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Abstract
Background: The COVID-19 pandemic has created substantial public health challenges worldwide, with Afghanistan facing unique vulnerabilities due to limited healthcare infrastructure and uneven vaccine coverage. Understanding the transmission dynamics of COVID-19 in this context is essential for designing effective intervention strategies.
Methods: Epidemiological data, including confirmed cases, mortality, and vaccination rates, were obtained from Our World in Data. Vaccination data were available from February 22, 2021, to December 31, 2023, and mortality rate estimation was based on data spanning April 1, 2020, to June 29, 2025. We developed a deterministic SEVIR compartmental model capturing susceptible, exposed, vaccinated, infectious, and recovered populations. The model was analyzed for biological feasibility, and key parameters, including the vaccination and mortality rates, were estimated from the data. Sensitivity analyses were conducted to determine the influence of parameters on disease progression. The basic reproduction number ( ) was derived analytically, and stability analysis of the disease-free equilibrium was performed.
Results: Model simulations indicate that the current vaccination rate in Afghanistan is insufficient to eliminate COVID-19. Doubling vaccination coverage could significantly reduce infection prevalence, while achieving herd immunity would require vaccinating approximately 86% of the population. Sensitivity analyses highlighted the critical role of vaccination and transmission rates in controlling disease spread. The disease-free equilibrium is locally stable whenever , confirming the theoretical feasibility of disease elimination.
Conclusion: These findings provide comprehensive insights into COVID-19 dynamics in Afghanistan and offer evidence-based guidance for public health policymakers to optimize vaccination strategies and mitigate the ongoing impact of the pandemic.
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