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

ABSTRACT:

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, 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.

Results

The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93% to 95%. Performance improves consistently with the number of r

SUBMITTER: Bednarski BP 

PROVIDER: S-EPMC7799039 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature