A Semi-Analytical Study on SEIR Model with Vaccinated Effects

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Abstract

This research describes the dynamics of COVID-19 propagation using a mathematical model that considers vaccination and self-defence. Furthermore, the impact of vaccination on the development of disease transmission has been investigated. Our findings specifically show that putting into practice the best control measures, including time-dependent interventions, lowers the total infection load and disease transmission. Five different compartments (Susceptible, Exposed, Vaccinated, Infected, and Recovered) are used in this study to examine an epidemiological model of COVID-19 dynamics. Approximate analytical solutions to the model’s system of equations were obtained using the homotopy analysis method (HAM). The numerical simulation using MATLAB was employed to validate the accuracy and effectiveness of the solutions obtained through the Homotopy Analysis Method (HAM) by comparing the results. Excellent agreement is found when comparing the approximate analytical solution and the numerical simulation. The five-compartment model includes many more aspect parameters that are explored and graphically represented. These parameters include recovery rate and vaccination rate, among others. Moreover, it underscores the potential of the HAM as a powerful tool for exploring epidemic models and formulating control strategies.

Author Biographies

  • V. Ananthaswamy

    Research Centre and PG Department of Mathematics, the Madura College (Affiliated to Madurai Kamaraj University), Madurai, Tamil Nadu, India

  • B. Sathyapriya

    Research Scholar, Research Centre and PG Department of Mathematics, the Madura College (Affiliated to Madurai Kamaraj University), Madurai, Tamil Nadu, India

  • M. Shruthi

    Research Scholar, Research Centre and PG Department of Mathematics, the Madura College (Affiliated to Madurai Kamaraj University), Madurai, Tamil Nadu, India

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DOI

10.12150/jnma.2026.338

How to Cite

A Semi-Analytical Study on SEIR Model with Vaccinated Effects. (2026). Journal of Nonlinear Modeling and Analysis, 8(2), 338–367. https://doi.org/10.12150/jnma.2026.338