SARS-CoV-2 Future Forecasting Using Multi-Linear Regression Model

Authors

Keywords:

Linear Regression, LASSO, Support Vector Machine(SVM), Random Forest, Exponential Smoothing, Regression Tree

Abstract

The 2019 pandemic in Wuhan, China caused a devastating global outbreak of the Coronavirus Disease (SARSCoV-2). Machine
learning offers a number of prediction models for future events that are based on training and testing, including conventional machine
learning and Deep Learning. This study shows that machine-learning models can anticipate the number of future SARS-CoV-2 patients
that are currently seen as a possible risk to the human race. Supervised machine learning models like linear regression, vector support and
regression tree are used for prediction. Data on the total cases and recovery cases are based on two types of predictions: new infections and
recovery situations. The machine-learning regression model is used to generate the outcome. In this paper, we present prediction of future
forecasting of Covid cases based on current situation by applying dataset of before and after pre-trial vaccine.

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Published

27.05.2022

How to Cite

[1]
P. J. Alpeshbhai and S. . Vohra, “SARS-CoV-2 Future Forecasting Using Multi-Linear Regression Model”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 159–165, May 2022.

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Research Article