Abstract
Malaria is one of the infectious and life-threatening vector-borne diseases that causes life-threatening complications. Effective and efficient strategies must be implemented to minimize the damage caused by malaria, and to do this, we must understand the dynamics of the measles transmission and implement control methods that are beneficial and cost-effective. In this work, bifurcation analysis and multi objective nonlinear model predictive control is performed on two dynamic models involving malaria transmission. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON. The bifurcation analysis revealed the existence of branch points in both models. The MNLMPC calculations converged to the Utopia solution in both models. The branch points (which cause multiple steady-state solutions from a singular point) are very beneficial because they enable the Multi objective nonlinear model predictive control calculations to converge to the Utopia point ( the best possible solution) in both models.
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