Optimized HIV/AIDS Resource Allocation in Ohio: A linear programming approach

This study employs a linear and integer programming approach to optimize HIV resource allocation in Ohio, aiming to minimize new infections and enhance the impact of limited resources. With the advances in HIV prevention and treatment, Ohio faces challenges in addressing disparities in access to healthcare, particularly among high-risk populations. The proposed model integrates data on infection rates, transmission patterns, demographic factors, and cost-effectiveness to provide a decision-support framework for policymakers. Using epidemiological data and equity constraints, the model prioritizes high-risk regions and populations while ensuring fair resource distribution. Results indicate that increased funding allocations significantly enhance the potential to avert new infections. This research underscores the importance of data-driven strategies in achieving the objectives outlined in the HIV National Strategic Plan, emphasizing targeted interventions and equitable healthcare access to curb the epidemic effectively.