The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Amir Ahmad; Sunita Garhwal; Santosh Kumar Ray; Gagan Kumar; Sharaf Jameel Malebary; Omar Mohammed Barukab

doi:10.1007/s11831-020-09472-8

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Arch Comput Methods Eng. 2021;28(4):2645-2653. doi: 10.1007/s11831-020-09472-8. Epub 2020 Aug 4.

Authors

Amir Ahmad¹, Sunita Garhwal², Santosh Kumar Ray³, Gagan Kumar⁴, Sharaf Jameel Malebary⁵, Omar Mohammed Barukab⁵

Affiliations

¹ College of Information Technology, United Arab Emirates University, Al Ain, UAE.
² Department of Computer Science and Engineering, Thapar University, Patiala, India.
³ Department of Information Technology, Khawarizmi International College, Al Ain, UAE.
⁴ Department of Physics, Indian Institute of Technology Guwahati, Guwahati, Assam 781039 India.
⁵ Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 411, Rabigh, Jeddah 21911 Saudi Arabia.

Abstract

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.