Forecasting and analysis of COVID-19 pandemic

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Authors
Ali, Mohammad Danish
Issue Date
2020
Degree
MSc in Data Analytics
Publisher
Dublin Business School
Rights
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Abstract
Coronaviruses are a group of viruses that cause various diseases in mammals and birds. In humans, they cause a range of respiratory disorders. This paper presents the analysis of the transmission of COVID19 disease and predicts the scale of the pandemic, the recovery rate as well as the fatality rate. We have used some of the well-known machine learning techniques as well as mathematical modeling techniques such as Support Vector Machine (SVM), Bayesian Ridge and Polynomial Regression and SIR model. Machine learning algorithms like Support Vector Regressor have the lowest R2 score = 0.8273 among Polynomial Regression, and Bayesian Ridge Regression, and the highest RMSE value = 124328.5297 amongst the three models, which tells us that the Support Vector Regressor is the least preferred model to choose. Bayesian Ridge Regression has R2 score = 0.9321 and the lowest RMSE value = 71920.7332 to be the best model among the three.