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-. DOI https://doi.org/10.1002/pa.2537
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-. DOI https://doi.org/10.1002/pa.2537
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-. DOI https://doi.org/10.1002/pa.2537
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 |
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Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 22 Nov 2020 19:43 |
Last Modified: | 30 Oct 2024 21:19 |
URI: | http://repository.essex.ac.uk/id/eprint/29138 |
Available files
Filename: Final accepted paper.pdf