Present status and future forecast of COVID-19 in India using time series modelling - Rohit Patawa; Pramendra Singh Pundir; Puneet Kumar Gupta (Current Medicine Research and Practice, 2020)
Background and Aim: The aim of this study is to analyse and forecast the coronavirus disease 2019 (COVID-19) cases in India, which may help the government and residents of India to mitigate the effect of COVID-19. Material and Methods: Univariate time series modelling has been used to forecast COVID-19 cases in India. The model is built to predict the number of confirmed cases, recovered cases, and death cases based on the data available between 30th June, 2020 and 28th August, 2020. Behaviour of the forecasts has been discussed at each round and also compared with the original values with its error measures such as mean absolute percentage error and root mean squared error. Results: For both the models used for fitting, namely exponential smoothing with multiplicative error and multiplicative trend, the later is found to be more appropriate than others. More importantly, at all stages of the forecast, the overall forecast error was <5%, which seems to be a good forecast. Conclusions: The present study may be valuable for Indian governments in the direction of making policies and accordingly taking suitable actions to mitigate the spread of COVID-19 cases in India.