Modeling of the COVID-19 Cases in Gulf Cooperation Council (GCC) countries using ARIMA and MA-ARIMA models.

Abstract

Coronavirus disease 2019 (COVID-19) is still a great pandemic presently spreading all around the world. In Gulf Cooperation Council (GCC) countries, there were 1015269 COVID-19 confirmed cases, 969424 recovery cases, and 9328 deaths as of 30th Nov. 2020. This paper, therefore, subjected the daily reported COVID-19 cases of these three variables to some statistical models including classical ARIMA, kth SMA-ARIMA, kth WMA-ARIMA, and kth EWMA-ARIMA to study the trend and to provide the long-term forecasting of the confirmed, recovery, and death cases of the novel COVID-19 pandemic in the GCC countries. The data analyzed in this study covered the period starting from the first case of coronavirus reported in each GCC country to Nov 30, 2020. To compute the best parameter estimates, each model was fitted for 90% of the available data in each country, which is called the in-sample forecast or training data, and the remaining 10% was used for the out-of-sample forecast or testing model. The AIC was applied to the training data as a criterion method to select the best model. Furthermore, the statistical measure RMSE was utilized for testing data, and the model with the minimum AIC and minimum RMSE was selected. The main finding, in general, is that the two models WMA-ARIMA and EWMA-ARIMA, besides the cubic linear regression model have given better results for in-sample and out-of-sample forecasts than the classical ARIMA models in fitting the confirmed and recovery cases while the death cases haven't specific models.