Modelling of Covid-19 cases in India using Regression and Time Series models

Sharma, V. K.; Nigam, U.

In this article, we analyze the growth pattern of Covid-19 pandemic in India from March 4th to May 15th using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as Exponential Smoothing and Holt-Winters models. We found that the growth of Covid-19 cases follows a power regime of (t2,t..)after the exponential growth. We have found the optimal change points from where the covid-19 cases shifts their course of growth from exponential to quadratic and then quadratic to linear. We have also found the best fitted regression models using the various criteria like- significant p-values, coefficients of determination R2 values and ANOVA etc. Further, we have searched the best fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and CAIC (Consistent Akaike Information Criterion) and forecasted the number of cases for future days. We have used the usual exponential smoothing and Holt-Winters models for the data. We further found that the ARIMA(2,2,0) model is the best-fitting model for Covid-19 cases in India.