Analysis of COVID-19 pandemic and forecasting using machine learning models
by Ekansh Chauhan; Manpreet Sirswal; Deepak Gupta; Ashish Khanna; Aditya Khamparia
International Journal of Computer Applications in Technology (IJCAT), Vol. 66, No. 3/4, 2021

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.

Online publication date: Fri, 21-Jan-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com