A python based support vector regression model for prediction of COVID19 cases in India. (September 2020)

• Record Type: Journal Article
• Authors: Parbat, Debanjan; Chakraborty, Monisha
• Is Part Of: Chaos, solitons and fractals. Volume 138(2020)
• Publication Date: 2020-09
• Subjects: COVID19 -- India -- Support vector regression -- Machine learining -- Python -- RBF -- Data analysis; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.109942
• Languages: English
• ISSNs: 0960-0779

Abstract

Abstract: The proposed work utilizes support vector regression model to predict the number of total number of deaths, recovered cases, cumulative number of confirmed cases and number of daily cases. The data is collected for the time period of 1 st March, 2020 to 30 th April, 2020 (61 Days). The total number of cases as on 30 th April is found to be 35043 confirmed cases with 1147 total deaths and 8889 recovered patients. The model has been developed in Python 3.6.3 to obtain the predicted values of aforementioned cases till 30 th June, 2020. The proposed methodology is based on prediction of values using support vector regression model with Radial Basis Function as the kernel and 10% confidence interval for the curve fitting. The data has been split into train and test set with test size 40% and training 60%. The model performance parameters are calculated as mean square error, root mean square error, regression score and percentage accuracy. The model has above 97% accuracy in predicting deaths, recovered, cumulative number of confirmed cases and 87% accuracy in predicting daily new cases. The results suggest a Gaussian decrease of the number of cases and could take another 3 to 4 months to come down the minimum level with no new cases being reported. The method is very efficient and has higher accuracy than linear or polynomial regression.