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



Gupta, Meenu, Jain, Rachna, Arora, Simrann, Gupta, Akash, Awan, Mazhar Javed, Chaudhary, Gopal and Nobanee, Haitham
(2021) AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories. CMC-COMPUTERS MATERIALS & CONTINUA, 67 (1). pp. 933-950.

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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.

Item Type: Article
Additional Information: Source info: Gupta, M., Jain, R., Arora, S., Gupta, A., Awan, M. J., Chaudhary, G., & Nobanee, H. (2021). AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories. Cmc-Computers Materials & Continua, 67(1), 933-950. doi:10.32604/cmc.2021.014221
Uncontrolled Keywords: COVID-19, state-wise analysis, discharges and deaths, SARS CoV-2, root mean square error
Divisions: Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures
Depositing User: Symplectic Admin
Date Deposited: 04 Aug 2021 09:39
Last Modified: 18 Jan 2023 21:34
DOI: 10.32604/cmc.2021.014221
Open Access URL: https://www.techscience.com/cmc/v67n1/41186
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3132358