On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic |
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Authors: | Bryan P Bednarski Akash Deep Singh William M Jones |
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Institution: | 1. Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, California, USA;2. School of Medicine, University of California, Irvine, Irvine, California, USA |
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Abstract: | ObjectiveThis 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 MethodsThe system presented is simulated with disease impact statistics from the Institute of Health Metrics, Centers for Disease Control and Prevention, and Census Bureau. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications.ResultsThe reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93% to 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.ConclusionsThese findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies. |
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Keywords: | machine learning artificial intelligence allocation resource coronavirus |
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