Predicting the Pandemic COVID-19 Using ARIMA Model

Duong, Nguyen Quoc; Thao, Le Phuong; Quynh, Dinh Thi Nhu; Bình, Lê Thanh; Loan, Cao Thi Ai; Diem, Phung Thi Hong

doi:10.25073/2588-1124/vnumap.4492

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…He inferred that ARIMA (1,2,4) and 5th EWMA-ARIMA (2,2,3) were the best models. Duong et al (2020) applied the ARIMA model for the total daily confirmed cases worldwide from Jan 21, 2020, to Mar 16, 2020. They found that ARIMA(1,2,1) could describe and predict the epidemiological trend of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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

Eledum

Yagoub

2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Eledum

Yagoub

2021

Preprint

View full text Add to dashboard Cite

show abstract

Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models

Yagoub

Eledum

2021

Journal of Probability and Statistics

View full text Add to dashboard Cite

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 30 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 Jan 31, 2021. 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 data. The AIC was applied to the training data as a criterion method to select the best model. Furthermore, the statistical measure RMSE and MAPE were utilized for testing data, and the model with the minimum RMSE and MAPE was selected for future forecasting. The main finding, in general, is that the two models WMA-ARIMA and EWMA-ARIMA, besides the cubic and 4th degree polynomial regression, 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 SMA-ARIMA and WMA-ARIMA were suitable to model the recovery and death cases in the GCC countries.

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