ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates

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dc.authoridSolyman, Ahmed/0000-0002-2881-8635
dc.authoriddeif, mohanad/0000-0002-4388-1480
dc.authoridE. Hammam, Rania/0000-0002-1460-3115
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorSolyman, Ahmed A. A.
dc.contributor.authorHammam, Rania E.
dc.date.accessioned2024-09-11T19:52:07Z
dc.date.available2024-09-11T19:52:07Z
dc.date.issued2021
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractThis paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model.en_US
dc.identifier.doi10.1142/S0219622021500528
dc.identifier.endpage1798en_US
dc.identifier.issn0219-6220
dc.identifier.issn1793-6845
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85113825507en_US
dc.identifier.startpage1775en_US
dc.identifier.urihttps://doi.org/10.1142/S0219622021500528
dc.identifier.urihttps://hdl.handle.net/11363/7911
dc.identifier.volume20en_US
dc.identifier.wosWOS:000741596400009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherWorld Scientific Publ Co Pte Ltden_US
dc.relation.ispartofInternational Journal of Information Technology & Decision Makingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectCOVID-19 forecastingen_US
dc.subjectARIMA modelen_US
dc.subjectgenetic algorithmen_US
dc.subjecttime-seriesen_US
dc.subjectinfection diseaseen_US
dc.subjectpandemicen_US
dc.titleARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Ratesen_US
dc.typeArticleen_US

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