Control Strategies for HIV/AIDS-Hepatitis B Coinfection using Optimal Control Approach and Cost-Effectiveness Analysis

HIV/AIDS and Hepatitis B are infectious diseases caused by viruses, sharing similar transmission mechanisms. This study seeks to determine the most effective and cost-efficient strategies for controlling the spread of these diseases by utilizing a modified HIV/AIDS-Hepatitis B coinfection model with various control variables. The model divides the total population into nine subpopulations, each representing a specific disease state. Based on these classifications, the model incorporates four key control variables, namely Hepatitis B vaccination program, Hepatitis B treatment, HIV/AIDS treatment, and public health education program. The research employs optimal control theory and the Pontryagin Maximum Principle to address the optimal control problem to minimize infection rates and implementation costs over a specific periode. The Hamilton function integritas the dynamic system and cost function. The model is analyzed through simulations using parameter values from previous studies, then optimizing control variables to generate a numerically solved system of differential equations that uses Scilab 2024 software. Simulation result show that the optimal combination of four control strategies reduces HIV/AIDS-Hepatitis B infection by 79,2% in under ten years. Furthermore, the cost-effectiveness of different strategies is evaluated using the Average Cost-Effectiveness Ratio (ACER) and Incremental Cost-Effectiveness Ratio (ICER) indicates that single control strategies are more cost-efficient, while combining all four strategies is more expensive. However, successful implementation depends on financial constraints (limited vaccination and ARV treatment), healthcare infrastructure (availability of testing facilities), and public compliance with health education programs. Consequently, the proposed strategies are recommended for policymakers, with consideration of associated costs to ensure feasibility.