Qualitative aspects and sensitivity analysis of MERS-Corona epidemic model with and without noise

Background. MERS-CoV (Middle East Respiratory Syndrome Coronavirus) is a severe respiratory illness that poses a significant threat to the Arabic community and has the potential for global spread. In this paper, we present deterministic and stochastic models to study the dynamics of MERS infection within hosts. Objective. For the purpose of describing the dynamics of MERS transmission throughout host populations, a deterministic model is created. To gauge the likelihood of an epidemic spreading, the reproduction number is calculated. Furthermore, Lyapunov function theory is used to assess the stability of the deterministic model. In order to account for the intrinsic heterogeneity in the disease dynamics, a stochastic model is also developed. In order to verify the analytic conclusions and learn more about the behavior of the system, numerical simulations are carried out. Methods. Investigating the reproduction rate yields vital details about MERS's capacity to start an outbreak. The stability analysis based on Lyapunov function theory sheds light on the deterministic model's long-term behavior. The analytical results are supported by numerical simulations, which also provide a thorough insight into the dynamics of the disease. Conclusion. The deterministic and stochastic models for MERS infection inside hosts are thoroughly examined in this paper. Our knowledge of the dynamics of the disease and its potential for epidemic transmission is improved by the examination of the reproduction number and stability analysis. The combination of analytical and numerical approaches contributes to a more comprehensive assessment of MERS-CoV and aids in informing public health interventions and control strategies.

Automated summary

This paper thoroughly examines the deterministic and stochastic models for MERS infection inside hosts. Our understanding of the dynamics of the disease and its potential for epidemic transmission is improved by studying the reproduction number and stability analysis. The combination of analytical and numerical approaches contributes to a more comprehensive assessment of MERS-CoV and aids in informing public health interventions and control strategies.