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

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  • Farrukh Saleem

    (Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Abdullah Saad AL-Malaise AL-Ghamdi

    (Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Madini O. Alassafi

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Saad Abdulla AlGhamdi

    (Ministry of Health, King Abdulaziz Hospital, Jeddah 22421, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Farrukh Saleem & Abdullah Saad AL-Malaise AL-Ghamdi & Madini O. Alassafi & Saad Abdulla AlGhamdi, 2022. "Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review," IJERPH, MDPI, vol. 19(9), pages 1-18, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5099-:d:799529
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    References listed on IDEAS

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    1. Roberto Vega & Leonardo Flores & Russell Greiner, 2022. "SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting," Forecasting, MDPI, vol. 4(1), pages 1-23, January.
    2. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    4. Yadav, Milind & Perumal, Murukessan & Srinivas, M, 2020. "Analysis on novel coronavirus (COVID-19) using machine learning methods," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    1. Suya Jin & Guiyan Liu & Qifeng Bai, 2023. "Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    2. Shiyang Lyu & Oyelola Adegboye & Kiki Adhinugraha & Theophilus I. Emeto & David Taniar, 2023. "Unlocking Insights: Analysing COVID-19 Lockdown Policies and Mobility Data in Victoria, Australia, through a Data-Driven Machine Learning Approach," Data, MDPI, vol. 9(1), pages 1-20, December.

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