A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports
Abstract The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-05-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00272-z |
| _version_ | 1797811264637894656 |
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| author | Nour Eldeen Khalifa Ahmed A. Mawgoud Amr Abu-Talleb Mohamed Hamed N. Taha Yu-Dong Zhang |
| author_facet | Nour Eldeen Khalifa Ahmed A. Mawgoud Amr Abu-Talleb Mohamed Hamed N. Taha Yu-Dong Zhang |
| author_sort | Nour Eldeen Khalifa |
| collection | DOAJ |
| description | Abstract The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily “world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. |
| first_indexed | 2024-03-13T07:21:08Z |
| format | Article |
| id | doaj.art-59585e8a3e99458795258c667a36ce93 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-13T07:21:08Z |
| publishDate | 2023-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-59585e8a3e99458795258c667a36ce932023-06-04T11:38:14ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-05-0116111110.1007/s44196-023-00272-zA COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility ReportsNour Eldeen Khalifa0Ahmed A. Mawgoud1Amr Abu-Talleb2Mohamed Hamed N. Taha3Yu-Dong Zhang4Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo UniversityInformation Technology Department, Faculty of Computers and Artificial Intelligence, Cairo UniversityComputer Science Department, Faculty of Computers Science, University of PeopleInformation Technology Department, Faculty of Computers and Artificial Intelligence, Cairo UniversitySchool of Computing and Mathematic Sciences, University of LeicesterAbstract The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily “world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates.https://doi.org/10.1007/s44196-023-00272-zDeep learningCOVID-19Prediction modelRegression modelEgypt |
| spellingShingle | Nour Eldeen Khalifa Ahmed A. Mawgoud Amr Abu-Talleb Mohamed Hamed N. Taha Yu-Dong Zhang A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports International Journal of Computational Intelligence Systems Deep learning COVID-19 Prediction model Regression model Egypt |
| title | A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports |
| title_full | A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports |
| title_fullStr | A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports |
| title_full_unstemmed | A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports |
| title_short | A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports |
| title_sort | covid 19 infection prediction model in egypt based on deep learning using population mobility reports |
| topic | Deep learning COVID-19 Prediction model Regression model Egypt |
| url | https://doi.org/10.1007/s44196-023-00272-z |
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