A Comparative Analysis of Different Regression Models on Predicting the Spread of Covid-19 in India

According to the World Health Organization (WHO) Situation Reports of Corona Virus Disease(Covid-19), as on 15^th May 2020, India has 81,970 totals confirmed cases, 2649 total deaths and is still within the limit of community transmission phase. In this study, we analyze the spread of the disease and the fatalities caused up to 15^th May 2020, as per the data obtained. A granular computing based regression model, namely Granular Box Regression is used along with Linear Regression for comparative analysis to study the increase in the number of confirmed cases and deaths based on days and an increase in the number of samples tested per day. A separate analysis is also conducted to evaluate the performance of Polynomial Regression on the same dataset. The performance of the different models has been evaluated using R-squared, Mean Absolute Error, Root Mean Squared Error, and Mean Squared Error values.