Title: Analysis of COVID-19 pandemic and forecasting using machine learning models

Authors: Ekansh Chauhan; Manpreet Sirswal; Deepak Gupta; Ashish Khanna; Aditya Khamparia

Addresses: Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India ' Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India ' Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India ' Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India ' School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

Abstract: The coronavirus is menacing thousands of lives and economy. Machine learning is being used in every sphere to fight the coronavirus. It is also critical to predict the pandemic lifetime so to decide on opportune and remedial activities. In this paper, based on public data available of the world and India, the estimation of pandemic parameters and the ten days ahead forecast of the coronavirus cases is proposed using Prophet, Polynomial Regression, Auto Arima and Support Vector Machine (SVM). The performance of all the models was motivating. The four parameters for coronavirus growth in the USA and India, including growth factor, growth ratio, growth rate, and second derivative, are also calculated and compared. This paper also discusses and submits a number of theories about the origins of coronavirus.

Keywords: COVID-19; machine learning; novel coronavirus; classification; technology.

DOI: 10.1504/IJCAT.2021.120456

International Journal of Computer Applications in Technology, 2021 Vol.66 No.3/4, pp.309 - 333

Received: 20 Jun 2020
Accepted: 28 Aug 2020
Published online: 21 Jan 2022 *

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