Investigating the effects of immunization tactics on essential variables of s-i-r outbreak model.

In our current study, we focus on analyzing an infectious disease model. By employing nonlinear optimization and optimal control techniques, we are able to identify strategies that are more effective in controlling transmission and predicting the global spread of infectious diseases. The integration of epidemiology, mathematical modeling, and computational tools allows us to develop and validate theories for disease prevention and management. This research utilizes numerical methods to visualize the solutions to key control problems, such as assessing the impact of vaccination on these models. Additionally, a global sensitivity analysis using the LHS Monte Carlo approach, specifically the Partial Rank Correlation Coefficient (P.C.), has been conducted to investigate the crucial parameters in the model equations. Ultimately, this study aims to enhance our understanding of the spread of infectious diseases and contribute to the development of innovative concepts in disease control.