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Review

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

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Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Ministry of Health, King Abdulaziz Hospital, Jeddah 22421, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editors: Abolfazl Mollalo and Shang-Ming Zhou
Int. J. Environ. Res. Public Health 2022, 19(9), 5099; https://doi.org/10.3390/ijerph19095099
Received: 25 February 2022 / Revised: 11 April 2022 / Accepted: 20 April 2022 / Published: 22 April 2022
(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)
COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic. View Full-Text
Keywords: epidemiology of COVID-19; basic reproduction rate; machine learning; deep learning epidemiology of COVID-19; basic reproduction rate; machine learning; deep learning
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MDPI and ACS Style

Saleem, F.; AL-Ghamdi, A.S.A.-M.; Alassafi, M.O.; AlGhamdi, S.A. Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. Int. J. Environ. Res. Public Health 2022, 19, 5099. https://doi.org/10.3390/ijerph19095099

AMA Style

Saleem F, AL-Ghamdi ASA-M, Alassafi MO, AlGhamdi SA. Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. International Journal of Environmental Research and Public Health. 2022; 19(9):5099. https://doi.org/10.3390/ijerph19095099

Chicago/Turabian Style

Saleem, Farrukh, Abdullah Saad AL-Malaise AL-Ghamdi, Madini O. Alassafi, and Saad Abdulla AlGhamdi. 2022. "Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review" International Journal of Environmental Research and Public Health 19, no. 9: 5099. https://doi.org/10.3390/ijerph19095099

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