{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:12:28Z","timestamp":1767262348993,"version":"3.40.5"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Science, Technology & Innovation Funding Authority"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 \u201cworld in data\u201d website. The suggested model could predict new cases of COVID-19 infection within 3\u20137\u00a0days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3\u00a0days 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.<\/jats:p>","DOI":"10.1007\/s44196-023-00272-z","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T09:04:32Z","timestamp":1685437472000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8614-9057","authenticated-orcid":false,"given":"Nour Eldeen","family":"Khalifa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0617-678X","authenticated-orcid":false,"given":"Ahmed A.","family":"Mawgoud","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9747-6982","authenticated-orcid":false,"given":"Amr","family":"Abu-Talleb","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0200-2918","authenticated-orcid":false,"given":"Mohamed Hamed N.","family":"Taha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-1493","authenticated-orcid":false,"given":"Yu-Dong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"issue":"5","key":"272_CR1","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1021\/acscentsci.0c00489","volume":"6","author":"RT Eastman","year":"2020","unstructured":"Eastman, R.T., et al.: Remdesivir: a review of its discovery and development leading to emergency use authorization for treatment of COVID-19. 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