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Analysis and prediction of COVID‐19 trajectory: A machine learning approach

Majhi, Ritanjali and Thangeda, Rahul and Sugasi, Renu Prasad and Kumar, Niraj (2021) 'Analysis and prediction of COVID‐19 trajectory: A machine learning approach.' Journal of Public Affairs, 21 (4). e2537-. ISSN 1472-3891

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Abstract

The outbreak of Coronavirus 2019 (COVID‐19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression‐based, Decision tree‐based, and Random forest‐based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real‐time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown.

Item Type: Article
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 22 Nov 2020 19:43
Last Modified: 24 Feb 2022 13:06
URI: http://repository.essex.ac.uk/id/eprint/29138

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