On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic

Objective: This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic.

Materials and Methods: The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications.

Results: The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states.

Conclusion: These findings bolster confidence tha...

研究目标：本工作旨在探究强化学习（Reinforcement Learning）与深度学习（Deep Learning）模型如何实现医疗设备的近最优再分配，以强化公共卫生系统对未来类似新冠疫情的危机应对能力。

材料与方法：本研究所提出的系统，依托健康指标与评估研究所（Institute of Health Metrics, IHME）、美国疾病控制与预防中心以及美国人口普查局发布的疾病影响统计数据开展仿真实验[1, 2, 3]。本文提出了一套稳健的标准化流程，涵盖数据预处理、未来需求推演以及可适配多尺度与多应用场景的再分配算法。

研究结果：所提出的强化学习再分配算法的性能最优区间为93%~95%。随着参与资源调配的模拟区域数量增加，算法性能持续提升：在包含5个模拟区域的仿真场景中，平均物资短缺量降低78.74%（±30.8）；而在包含50个模拟区域的场景中，该降幅可达93.50%（±0.003）。

研究结论：上述研究结果进一步增强了……