Applying Deep Learning Methods on Time‐Series Data for Forecasting COVID‐19 in Egypt, Kuwait, and Saudi Arabia

Omran, Nahla F.; Ghany, Sara F. Abd-el; Saleh, Hager; Ali, Abdelmgeid A.; Gumaei, Abdu; Al-Rakhami, Mabrook S.

doi:10.1155/2021/6686745

Cited by 44 publications

(31 citation statements)

References 17 publications

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“…By the end of March 2021, Saudi Arabia had confirmed cases of more than 390,000 and more than 6500 deaths. This study is in line with other COVID-19 studies that have been conducted based on region-specific data, such as [8] in Brazil, [26] in Romania, and [27] in Egypt, Kuwait, and Saudi Arabia.…”

Section: Introductionsupporting

confidence: 91%

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Al-Shargabi

Selmi

2021

IEEE Access

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Twitter data have been used widely in times of pandemics and crises. Currently, the world is suffering from the outbreak of new coronavirus disease 2019 , in which the COVID-19 virus has infected more than one hundred and twenty million people worldwide with more than two million deaths. Consequently, people tend to share COVID-19-related content on social media extensively. As a new pandemic, only a small number of studies have been conducted to analyze COVID-19-related tweets, and even fewer were meant for Arabic tweets. This research explores the influence of the COVID-19 pandemic on Saudi users' tweeting behavior. In particular, the research adopts a social network analysis (SNA) for COVID-19 Arabic tweets. This approach is interesting, as it is based on analysis of social structures, such as Twitter users and the relationships among them, through the use of networks and graph theory without the contents of the tweets themselves. Based on 8905 collected Arabic tweets, this research resulted in three main contributions: 1) a visualization of the social network for COVID-19 tweets of Saudi users, 2) an identification of information sources that Twitter users employ during the COVID-19 pandemic, and 3) an identification of the most popular influencers among users of COVID-19 tweets. The results of this study may help identify the most popular Twitter influencers and those who deliver the information to Twitter users, utilizing them to increase awareness and deliver information and instructions to overcome the COVID-19 pandemic.

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Section: Introductionsupporting

confidence: 91%

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Al-Shargabi

Selmi

2021

IEEE Access

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“…Their application of an ARIMA model with different lags (1,0,2), (1,1,1), (1,1,3) showed that an ARIMA model with lag (1,1,1) was better than the others and gave scores of R2 (0.96) and RMSE (341) in that period. Nahla et al [68] presented two deep learning approaches (LSTM and GRU) to predict COVID-19 infection in Egypt, Kuwait, and Saudi Arabia from 1 May 2020 to 12 June 2020. The empirical results showed that the LSTM model achieved the highest percentages for the evaluation metrics MAPE (0.445), RMSE (29.8), and MAE (28.0).…”

Section: Related Workmentioning

confidence: 99%

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

Aldhyani

Alkahtani

2021

Life

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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.

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“…Coronavirus disease (COVID-19) is considered one of the most confrontational pandemics causing severe danger to humanity in the twenty-first century due to its progression, infection, spread, and mortality rate worldwide [28] . Till July 2021, the COVID-19 confirmed cases over the world were 190 million cases including 4.1 million deaths.…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 lockdown on small-scale farming in Northeastern Nile Delta of Egypt and learned lessons for water conservation potentials

Selim

Eltarabily

2022

Ain Shams Engineering Journal

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Applying Deep Learning Methods on Time‐Series Data for Forecasting COVID‐19 in Egypt, Kuwait, and Saudi Arabia

Cited by 44 publications

References 17 publications

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

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

Impact of COVID-19 lockdown on small-scale farming in Northeastern Nile Delta of Egypt and learned lessons for water conservation potentials

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