Authors: Khalilpourazari S, Hashemi Doulabi H
Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.
Ann Oper Res. 2021 Jan 03; :1-45
Authors: Khalilpourazari S, Hashemi Doulabi H
Abstract
World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E-06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.
PMID: 33424076 [PubMed - as supplied by publisher]
Keywords: COVID-19 pandemic; Machine learning; Reinforcement learning; SARS-Cov-2; SIDARTHE;
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33424076
DOI: 10.1007/s10479-020-03871-7