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

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 repo...

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Autores principales:	Rahmatalla Yagoub, Hussein Eledum
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Probabilities. Mathematical statistics QA273-280
Acceso en línea:	https://doaj.org/article/2d506a51464d4dea9804a778c41ab950
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spelling	oai:doaj.org-article:2d506a51464d4dea9804a778c41ab9502021-11-08T02:37:15ZModeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models1687-953810.1155/2021/1623441https://doaj.org/article/2d506a51464d4dea9804a778c41ab9502021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1623441https://doaj.org/toc/1687-9538Coronavirus 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.Rahmatalla YagoubHussein EledumHindawi LimitedarticleProbabilities. Mathematical statisticsQA273-280ENJournal of Probability and Statistics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Probabilities. Mathematical statistics QA273-280
spellingShingle	Probabilities. Mathematical statistics QA273-280 Rahmatalla Yagoub Hussein Eledum Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models
description	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.
format	article
author	Rahmatalla Yagoub Hussein Eledum
author_facet	Rahmatalla Yagoub Hussein Eledum
author_sort	Rahmatalla Yagoub
title	Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models
title_short	Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models
title_full	Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models
title_fullStr	Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models
title_full_unstemmed	Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models
title_sort	modeling of the covid-19 cases in gulf cooperation council countries using arima and ma-arima models
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2d506a51464d4dea9804a778c41ab950
work_keys_str_mv	AT rahmatallayagoub modelingofthecovid19casesingulfcooperationcouncilcountriesusingarimaandmaarimamodels AT husseineledum modelingofthecovid19casesingulfcooperationcouncilcountriesusingarimaandmaarimamodels
_version_	1718442985293611008

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

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