Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia
The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6686745 |
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| author | Nahla F. Omran Sara F. Abd-el Ghany Hager Saleh Abdelmgeid A. Ali Abdu Gumaei Mabrook Al-Rakhami |
| author_facet | Nahla F. Omran Sara F. Abd-el Ghany Hager Saleh Abdelmgeid A. Ali Abdu Gumaei Mabrook Al-Rakhami |
| author_sort | Nahla F. Omran |
| collection | DOAJ |
| description | The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait. |
| format | Article |
| id | doaj-art-c7f6a65636f94323a949112b7902c9a4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c7f6a65636f94323a949112b7902c9a42025-02-03T06:43:55ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66867456686745Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi ArabiaNahla F. Omran0Sara F. Abd-el Ghany1Hager Saleh2Abdelmgeid A. Ali3Abdu Gumaei4Mabrook Al-Rakhami5Faculty of Computers and Information, South Valley University, Qena, EgyptComputer Science Department, Faculty of Science, South Valley University, Qena, EgyptFaculty of Computers and Artificial Intelligence, South Valley University, Hurghada, EgyptFaculty of Computers and Information, Minia University, Minya, EgyptResearch Chair of Pervasive and Mobile Computing, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaResearch Chair of Pervasive and Mobile Computing, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaThe novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait.http://dx.doi.org/10.1155/2021/6686745 |
| spellingShingle | Nahla F. Omran Sara F. Abd-el Ghany Hager Saleh Abdelmgeid A. Ali Abdu Gumaei Mabrook Al-Rakhami Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia Complexity |
| title | Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia |
| title_full | Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia |
| title_fullStr | Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia |
| title_full_unstemmed | Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia |
| title_short | Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia |
| title_sort | applying deep learning methods on time series data for forecasting covid 19 in egypt kuwait and saudi arabia |
| url | http://dx.doi.org/10.1155/2021/6686745 |
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