A Model of Tuberculosis and Diabetes Co-Infection with Optimal Control
Eunice Mueni Musyoki *
Department of Mathematics and Actuarial Science, Kenyatta University, P.O Box 43844 - 00100, Nairobi, Kenya.
Winfred Nduku Mutuku
Department of Mathematics and Actuarial Science, Kenyatta University, P.O Box 43844 - 00100, Nairobi, Kenya.
Nancy Matendechere Imbusi
Department of Industrial & Engineering Mathematics, Technical University of Kenya, P.O. Box 52428 - 00200, Nairobi, Kenya.
Evans Otieno Omondi
Institute of Mathematical Sciences, Strathmore University, P.O Box 59857-00200, Nairobi, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Aims/ objectives: Tuberculosis and diabetes co-infection is a complex health issue, thus, effective management requires understanding disease dynamics and interactions. This paper expands the existing model to incorporate the co-infection of diabetes and tuberculosis to understand disease complications better.
Methodology: The study employs the next-generation matrix to calculate RC and utilizes LaSalle’s invariance principle. It demonstrates that the model achieves global asymptotic stability at the disease-free equilibrium ( DF E ) when RC ≤ 1 . The Volterra-Lyapunov matrix is then employed to establish global asymptotic stability of the endemic equilibrium when RC > 1 . Based on the Jacobian matrix, local stability analysis suggests the potential for epidemic eradication when RC ≤ 1 , while RC ≥ 1 indicates a risk of epidemic spread. Numerical solutions using ODE45 in Matlab R2021b are employed for the analysis.
Results: The sensitivity analysis highlighted the significant impact of TB transmission coefficient β and diabetes acquisition rate α1 on RC , emphasizing the need for optimal control measures targeting these factors.
Conclusion: A decrease in TB transmission coefficient led to a reduction in RC from 1.0863 to 0.1845 , suggesting the potential effectiveness of control strategies. The study also recommends exploring models considering different diabetes types in future research.
Keywords: Mathematical model, non-standard finite difference, tuberculosis, diabetes