{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:49:24Z","timestamp":1771026564048,"version":"3.50.1"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":62,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The novel coronavirus disease (COVID\u201019) 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\u2019s 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\u2014for safety and prevention purposes\u2014to 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\u2010series 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\u201019. Long short\u2010term memory (LSTM) and gated recurrent unit (GRU) have been applied on time\u2010series 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.<\/jats:p>","DOI":"10.1155\/2021\/6686745","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T19:05:14Z","timestamp":1614884714000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Applying Deep Learning Methods on Time\u2010Series Data for Forecasting COVID\u201019 in Egypt, Kuwait, and Saudi Arabia"],"prefix":"10.1155","volume":"2021","author":[{"given":"Nahla F.","family":"Omran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8362-4350","authenticated-orcid":false,"given":"Sara F.","family":"Abd-el Ghany","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6184-7107","authenticated-orcid":false,"given":"Hager","family":"Saleh","sequence":"additional","affiliation":[]},{"given":"Abdelmgeid A.","family":"Ali","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8512-9687","authenticated-orcid":false,"given":"Abdu","family":"Gumaei","sequence":"additional","affiliation":[]},{"given":"Mabrook","family":"Al-Rakhami","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40846-020-00529-4"},{"key":"e_1_2_8_2_2","volume-title":"The Coronavirus (COVID-19)","author":"WHO","year":"2021"},{"key":"e_1_2_8_3_2","unstructured":"\u201cPfizer BioNTech COVID-19 Vaccine \u201d 2021 https:\/\/www.idsociety.org\/covid-19-real-time-learning-network\/vaccines\/Pfizer-BioNTech-COVID-19-Vaccine\/."},{"key":"e_1_2_8_4_2","unstructured":"\u201cModerna COVID-19 Vaccine \u201d 2021 https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/vaccines\/different-vaccines\/Moderna.html."},{"key":"e_1_2_8_5_2","volume-title":"Prediction of Criticality in Patients with Severe Covid-19 Infection Using Three Clinical Features: A Machine Learning-Based Prognostic Model with Clinical Data in Wuhan","author":"Yan L.","year":"2020"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2020.02.64"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2020.61"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.30.20047308"},{"key":"e_1_2_8_9_2","first-page":"1","article-title":"Predicting systolic blood pressure in real-time using streaming data and deep learning","author":"Saleh H.","year":"2020","journal-title":"Mobile Networks and Applications"},{"key":"e_1_2_8_10_2","first-page":"209","article-title":"A feature based neural network model for weather forecasting","volume":"4","author":"Paras S. 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