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

Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/16748

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DC Field	Value	Language
dc.contributor.author	Majhi R.
dc.contributor.author	Thangeda R.
dc.contributor.author	Sugasi R.P.
dc.contributor.author	Kumar N.
dc.date.accessioned	2021-05-05T10:31:32Z	-
dc.date.available	2021-05-05T10:31:32Z	-
dc.date.issued	2020
dc.identifier.citation	Journal of Public Affairs , Vol. , , p. -	en_US
dc.identifier.uri	https://doi.org/10.1002/pa.2537
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/16748	-
dc.description.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. © 2020 John Wiley & Sons Ltd	en_US
dc.title	Analysis and prediction of COVID-19 trajectory: A machine learning approach	en_US
dc.type	Article	en_US
Appears in Collections:	1. Journal Articles

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