Dynamical analysis of SARS-CoV-2-Dengue co-infection mathematical model with optimum control and sensitivity analyses

• Published in: Nonlinear analysis: real world applications Vol. 80; p. 104175
• Main Authors: Kumar, R. Prem, Mahapatra, G.S., Santra, P.K.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Coinfection; Dengue; Numerical analysis; Optimal control; SARS-CoV-2; Sensitivity; Stability analysis; SARS-CoV-2; Sensitivity; Coinfection; Numerical analysis; Dengue; Optimal control; Stability analysis
• ISSN: 1468-1218
• DOI: 10.1016/j.nonrwa.2024.104175

This study develops an epidemic model to analyze the dynamics of SARS-CoV-2 and dengue coinfection in a population. The population is divided into sixteen compartments for humans and three for vectors. The model’s validity is ensured by maintaining bounded and non-negative solutions. The Basic Reproduction Number (BRN) is calculated for each sub-model to assess stability at equilibrium points. Sensitivity analysis identifies key parameters influencing the model. The complete coinfection model is analyzed to identify equilibrium points and evaluate stability conditions. The reciprocal influence of SARS-CoV-2 and dengue diseases is examined. An optimal control problem is formulated, incorporating six strategies: COVID-19 protection, mosquito bite prevention, treatment for COVID-19 and dengue, mosquito control, and coinfection treatment. Numerical simulations validate the effectiveness of these control strategies for the coinfection model and its sub-models. •Model Development: Epidemic model for SARS-CoV-2 and Dengue co-infection dynamics.•Stability and Sensitivity: Analysis identifies key parameters for disease control strategies.•Optimal Control: Formulates six controls for COVID-19, mosquito protection, and treatments.•Numerical Validation: Validates strategies via simulations, confirming their efficacy.•Practical Implications: Framework guides decision-making to minimize disease burdens and costs.