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Research Article | DOI: https://doi.org/ISSRR-RA-25-016

Analysis and Control of a Listeriosis Transmission Dynamic Model

Lakshmi. N. Sridhar *

Chemical Engineering Department University of Puerto Rico Mayaguez,United States of America

*Corresponding Author: Lakshmi. N. Sridhar, Chemical Engineering Department University of Puerto Rico Mayaguez,United States of America

Citation: Lakshmi. N. Sridhar (2025). Analysis and Control of a Listeriosis Transmission Dynamic Model , J International Social Science Research and Review. 2 (3), DOI: ISSRR-RA-25-016.

Copyright: Lakshmi. N. Sridhar, et al © (2025). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Received: 24 July 2025 | Accepted: 31 July 2025 | Published: 15 August 2025

Keywords: bifurcation; optimization; control; listeriosis

Abstract

Human listeriosis has a high mortality rate and poses a significant health risk. Therefore, it is crucial to develop strategies to combat this disease. Humans are primarily infected with listeriosis through the consumption of Listeria-contaminated foods. Implementing effective control strategies is essential to eradicate the disease. Several factors must be considered, and multiple objectives must be achieved simultaneously. Bifurcation analysis and multi-objective nonlinear model predictive control (MNLMPC) calculations are performed on a dynamic model involving listeriosis transmission. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were carried out using the optimization language PYOMO in conjunction with the advanced global optimization solvers IPOPT and BARON. The bifurcation analysis revealed the existence of a branch point in the system. These branch point (which causes multiple steady-state solutions from a single point) is beneficial because it enables the multi-objective nonlinear model predictive control calculations to converge to the Utopia point which is the best possible solution. It has been demonstrated (with computational validation) that the branch point results from the presence of two distinct separable functions in one of the equations of the dynamic model. A theorem was developed to prove this fact for any dynamic model.

 

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