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Malki Z, Atlam ES, Ewis A, Dagnew G, Ghoneim OA, Mohamed AA, Abdel-Daim MM, Gad I. The COVID-19 pandemic: prediction study based on machine learning models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:40496-40506. [PMID: 33840016 PMCID: PMC8035887 DOI: 10.1007/s11356-021-13824-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 04/05/2021] [Indexed: 04/16/2023]
Abstract
COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18th, 2021, just after a year has passed, a total of 110,533,973 confirmed cases of COVID-19 were reported and its death toll reached about 2,443,091. COVID-19 is a new member of the family of corona viruses, its nature, behaviour, transmission, spread, prevention, and treatment are to be investigated. Generally, a huge amount of data is accumulating regarding the COVID-19 pandemic, which makes hot research topics for machine learning researchers. However, the panicked world's population is asking when the COVID-19 will be over? This study considered machine learning approaches to predict the spread of the COVID-19 in many countries. The experimental results of the proposed model showed that the overall R2 is 0.99 from the perspective of confirmed cases. A machine learning model has been developed to predict the estimation of the spread of the COVID-19 infection in many countries and the expected period after which the virus can be stopped. Globally, our results forecasted that the COVID-19 infections will greatly decline during the first week of September 2021 when it will be going to an end shortly afterward.
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Affiliation(s)
- Zohair Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - El-Sayed Atlam
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia.
- Faculty of Science, Tanta University, Tanta, Egypt.
| | - Ashraf Ewis
- Department of Public Health and Occupational Medicine, Faculty of Medicine, Minia University, Minia, Egypt
- Department of Public Health, Faculty of Health Sciences - AlQunfudah, Umm AlQura University, Meccah, Saudi Arabia
| | - Guesh Dagnew
- Department of Computer Science, Institute of Technology, Dire Dawa University, Dire Dawa, Ethiopia
| | - Osama A Ghoneim
- Faculty of Computers and Informatics, Tanta University, Tanta, Egypt
| | - Abdallah A Mohamed
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shibin El Kom, Egypt
| | - Mohamed M Abdel-Daim
- Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
- Pharmacology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522, Egypt
| | - Ibrahim Gad
- Faculty of Science, Tanta University, Tanta, Egypt
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