Modeling and Forecasting of Covid-19 growth curve in India

Abstract

AbstractIn this article, we analyze the growth pattern of Covid-19 pandemic in India from March 4th to July 11th using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt-Winters smoothing models. We found that the growth of Covid-19 cases follows a power regime of (t2,t,..) after the exponential growth. We found the optimal change points from where the Covid-19 cases shift their course of growth from exponential to quadratic and then from quadratic to linear. After that, we saw a sudden spike in the course of the spread of Covid-19 and the growth moved from linear to quadratic and then to quartic, which is alarming. We have also found the best fitted regression models using the various criteria such as significant p-values, coefficients of determination and ANOVA etc. Further, we search the best fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and provide the forecast of Covid-19 cases for future days. It was noticed that the ARIMA model fits better the Covid-19 cases for small regions. ARIMA (5, 2, 5) and ARIMA (5, 2, 3) are the best possible models for modeling Covid-19 cases for March 4th to July 10th and June 1th to July 10th, respectively.