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 that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

Summary statistics for our study are attached here for reference.