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Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10479-020-03871-7;h=repec:spr:annopr:v:312:y:2022:i:2:d:10.1007_s10479-020-03871-7
Time Added
6/12/2022, 6:38:50 PM
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Machine Learning
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Authors
Soheyl Khalilpourazari and Hossein Hashemi Doulabi Soheyl Khalilpourazari: Concordia University Hossein Hashemi Doulabi: Concordia University
Abstract
Abstract World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then 26795847 cases have been reported worldwide and 878963 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.
Keywords
COVID-19 pandemic ; SARS-Cov-2 ; Reinforcement learning ; SIDARTHE ; Machine learning (search for similar items in EconPapers)
Year Published
2022
Series
Annals of Operations Research 2022 vol. 312 issue 2 No 24 1305 pages
Rank
0.73
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