Open Access

ARTICLE

AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories

by Meenu Gupta¹, Rachna Jain², Simrann Arora², Akash Gupta², Mazhar Javed Awan³, Gopal Chaudhary^2,*, Haitham Nobanee^4,5,6

1 Chandigarh University, Punjab, India
2 Bharati Vidyapeeth’s College of Engineering, New Delhi, India
3 Department of Software Engineering, University of Management and Technology, Lahore, Pakistan
4 Collage of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates
5 Oxford Center for Islamic Studies, The University of Oxford, Oxford, UK
6 Management School, The University of Liverpool, Liverpool, UK

* Corresponding Author: Gopal Chaudhary. Email:

(This article belongs to the Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)

Computers, Materials & Continua 2021, 67(1), 933-950. https://doi.org/10.32604/cmc.2021.014221

Received 07 September 2020; Accepted 12 November 2020; Issue published 12 January 2021

Abstract

The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of a regression model to predict the number of discharge patients and deaths in each state. The performance of the proposed prediction framework is determined by using three machine learning regression algorithms, namely Polynomial Regression (PR), Decision Tree Regression, and Random Forest (RF) Regression. The results show a comparative analysis of the states and union territories having more than 1000 cases, and the trained model is validated by testing it on further dates. The performance is evaluated using the RMSE metrics. The results show that the Polynomial Regression with an RMSE value of 0.08, shows the best performance in the prediction of the discharged patients. In contrast, in the case of prediction of deaths, Random Forest with a value of 0.14, shows a better performance than other techniques.

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