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Artificial neural networks and statistical models for optimization studying COVID-19
The biggest challenge facing the world in 2020 was the pandemic of the coronavirus disease (COVID-19). Since the start of 2020, COVID-19 has invaded the world, causing death to people and economic damage, which is cause for sadness and anxiety. Since the world has passed from the first peak with rel...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113008/ https://www.ncbi.nlm.nih.gov/pubmed/33996399 http://dx.doi.org/10.1016/j.rinp.2021.104274 |
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author | Elhag, Azhari A. Aloafi, Tahani A. Jawa, Taghreed M. Sayed-Ahmed, Neveen Bayones, F.S. Bouslimi, J. |
author_facet | Elhag, Azhari A. Aloafi, Tahani A. Jawa, Taghreed M. Sayed-Ahmed, Neveen Bayones, F.S. Bouslimi, J. |
author_sort | Elhag, Azhari A. |
collection | PubMed |
description | The biggest challenge facing the world in 2020 was the pandemic of the coronavirus disease (COVID-19). Since the start of 2020, COVID-19 has invaded the world, causing death to people and economic damage, which is cause for sadness and anxiety. Since the world has passed from the first peak with relative success, this should be evaluated by statistical analysis in preparation for potential further waves. Artificial neural networks and logistic regression models were used in this study, and some statistical indicators were extracted to shed light on this pandemic. WHO website data for 32 European countries from 11th of January 2020 to 29th of May 2020 was utilized. The rationale for choosing the stated methodological tools is that the classification accuracy rate of artificial neural networks is 85.6% while the classification accuracy rate of logistic regression models 80.8%. |
format | Online Article Text |
id | pubmed-8113008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81130082021-05-12 Artificial neural networks and statistical models for optimization studying COVID-19 Elhag, Azhari A. Aloafi, Tahani A. Jawa, Taghreed M. Sayed-Ahmed, Neveen Bayones, F.S. Bouslimi, J. Results Phys Article The biggest challenge facing the world in 2020 was the pandemic of the coronavirus disease (COVID-19). Since the start of 2020, COVID-19 has invaded the world, causing death to people and economic damage, which is cause for sadness and anxiety. Since the world has passed from the first peak with relative success, this should be evaluated by statistical analysis in preparation for potential further waves. Artificial neural networks and logistic regression models were used in this study, and some statistical indicators were extracted to shed light on this pandemic. WHO website data for 32 European countries from 11th of January 2020 to 29th of May 2020 was utilized. The rationale for choosing the stated methodological tools is that the classification accuracy rate of artificial neural networks is 85.6% while the classification accuracy rate of logistic regression models 80.8%. The Author(s). Published by Elsevier B.V. 2021-06 2021-05-12 /pmc/articles/PMC8113008/ /pubmed/33996399 http://dx.doi.org/10.1016/j.rinp.2021.104274 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Elhag, Azhari A. Aloafi, Tahani A. Jawa, Taghreed M. Sayed-Ahmed, Neveen Bayones, F.S. Bouslimi, J. Artificial neural networks and statistical models for optimization studying COVID-19 |
title | Artificial neural networks and statistical models for optimization studying COVID-19 |
title_full | Artificial neural networks and statistical models for optimization studying COVID-19 |
title_fullStr | Artificial neural networks and statistical models for optimization studying COVID-19 |
title_full_unstemmed | Artificial neural networks and statistical models for optimization studying COVID-19 |
title_short | Artificial neural networks and statistical models for optimization studying COVID-19 |
title_sort | artificial neural networks and statistical models for optimization studying covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113008/ https://www.ncbi.nlm.nih.gov/pubmed/33996399 http://dx.doi.org/10.1016/j.rinp.2021.104274 |
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