A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction syste...

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Autores principales:	Theyazn H. H. Aldhyani, Hasan Alkahtani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Bi-LSTM deep learning time series model COVID-19 Gulf countries Science Q
Acceso en línea:	https://doaj.org/article/9eaa5e46d1c14e5e8c16ac41bbb187db
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spelling	oai:doaj.org-article:9eaa5e46d1c14e5e8c16ac41bbb187db2021-11-25T18:10:25ZA Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries10.3390/life111111182075-1729https://doaj.org/article/9eaa5e46d1c14e5e8c16ac41bbb187db2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-1729/11/11/1118https://doaj.org/toc/2075-1729Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.Theyazn H. H. AldhyaniHasan AlkahtaniMDPI AGarticleBi-LSTMdeep learningtime series modelCOVID-19Gulf countriesScienceQENLife, Vol 11, Iss 1118, p 1118 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bi-LSTM deep learning time series model COVID-19 Gulf countries Science Q
spellingShingle	Bi-LSTM deep learning time series model COVID-19 Gulf countries Science Q Theyazn H. H. Aldhyani Hasan Alkahtani A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
description	Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.
format	article
author	Theyazn H. H. Aldhyani Hasan Alkahtani
author_facet	Theyazn H. H. Aldhyani Hasan Alkahtani
author_sort	Theyazn H. H. Aldhyani
title	A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
title_short	A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
title_full	A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
title_fullStr	A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
title_full_unstemmed	A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
title_sort	bidirectional long short-term memory model algorithm for predicting covid-19 in gulf countries
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9eaa5e46d1c14e5e8c16ac41bbb187db
work_keys_str_mv	AT theyaznhhaldhyani abidirectionallongshorttermmemorymodelalgorithmforpredictingcovid19ingulfcountries AT hasanalkahtani abidirectionallongshorttermmemorymodelalgorithmforpredictingcovid19ingulfcountries AT theyaznhhaldhyani bidirectionallongshorttermmemorymodelalgorithmforpredictingcovid19ingulfcountries AT hasanalkahtani bidirectionallongshorttermmemorymodelalgorithmforpredictingcovid19ingulfcountries
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A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

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