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Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779111/ https://www.ncbi.nlm.nih.gov/pubmed/33424076 http://dx.doi.org/10.1007/s10479-020-03871-7 |
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author | Khalilpourazari, Soheyl Hashemi Doulabi, Hossein |
author_facet | Khalilpourazari, Soheyl Hashemi Doulabi, Hossein |
author_sort | Khalilpourazari, Soheyl |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7779111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-77791112021-01-04 Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec Khalilpourazari, Soheyl Hashemi Doulabi, Hossein Ann Oper Res Original Research 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. Springer US 2021-01-03 2022 /pmc/articles/PMC7779111/ /pubmed/33424076 http://dx.doi.org/10.1007/s10479-020-03871-7 Text en © Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Khalilpourazari, Soheyl Hashemi Doulabi, Hossein Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec |
title | Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec |
title_full | Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec |
title_fullStr | Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec |
title_full_unstemmed | Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec |
title_short | Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec |
title_sort | designing a hybrid reinforcement learning based algorithm with application in prediction of the covid-19 pandemic in quebec |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779111/ https://www.ncbi.nlm.nih.gov/pubmed/33424076 http://dx.doi.org/10.1007/s10479-020-03871-7 |
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