Optimization of Medical Supplies Allocation Based on the Dynamic Evolution of Public Health Emergencies

The effective allocation of medical supplies is critical when controlling the outbreak of infectious diseases. However, existing studies often neglect the interaction between medical supplies distribution and epidemic transmission, potentially leading to over- or underestimation of control effectiveness. This study proposes a multi-period medical supplies allocation model considering interaction effects with epidemic (MMSA-IEE). By incorporating transmission and treatment functions that are sensitive to medical supply levels, along with a time-varying demand function, the model more accurately captures the interaction between epidemic transmission and resource allocation. To solve the model, this paper develops a dynamic programming algorithm framework based on the division of the main problem and subproblems. Numerical experiments were conducted to evaluate the computational performance of the algorithm under various scenario scales. The analysis results of case studies demonstrate that sufficient stockpiles and timely response can delay the infection peak, allowing more time for vaccine development. Compared to single-period models, the multi-period dynamic model improves epidemic prevention effectiveness. Under a dynamic multi-modal transportation adjustment strategy, the total cost is reduced by 12.0% and 19.7% compared to single-mode railway and air transport, respectively. Furthermore, improvements in both recovery rate and full immunity rate significantly reduce infection and total costs. These findings provide quantitative evidence and decision-making support for scientific stockpiling and dynamic allocation of medical supplies during public health emergencies.