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Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks

SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 20...

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Detalles Bibliográficos
Autores principales: Saba, Amal I., Elsheikh, Ammar H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Institution of Chemical Engineers. Published by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237379/
https://www.ncbi.nlm.nih.gov/pubmed/32501368
http://dx.doi.org/10.1016/j.psep.2020.05.029
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author Saba, Amal I.
Elsheikh, Ammar H.
author_facet Saba, Amal I.
Elsheikh, Ammar H.
author_sort Saba, Amal I.
collection PubMed
description SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.
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spelling pubmed-72373792020-05-20 Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks Saba, Amal I. Elsheikh, Ammar H. Process Saf Environ Prot Article SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic. Institution of Chemical Engineers. Published by Elsevier B.V. 2020-09 2020-05-20 /pmc/articles/PMC7237379/ /pubmed/32501368 http://dx.doi.org/10.1016/j.psep.2020.05.029 Text en © 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. 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
Saba, Amal I.
Elsheikh, Ammar H.
Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks
title Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks
title_full Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks
title_fullStr Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks
title_full_unstemmed Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks
title_short Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks
title_sort forecasting the prevalence of covid-19 outbreak in egypt using nonlinear autoregressive artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237379/
https://www.ncbi.nlm.nih.gov/pubmed/32501368
http://dx.doi.org/10.1016/j.psep.2020.05.029
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