An investigation on the estimation of the impact factors of pandemic deaths with artificial neural network and multiple regression algorithms: Covid-19 case

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dc.contributor.authorDemir, Ibrahim
dc.contributor.authorSari, Murat
dc.contributor.authorGulen, Seda
dc.contributor.authorBalacescu, Aniela
dc.date.accessioned2024-10-29T17:58:53Z
dc.date.available2024-10-29T17:58:53Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThis article aims to successfully estimate the number of deaths in a pandemic, with the appropriate implementation of two new modelling approaches, artificial neural network and multiple regression analysis. Then, these methods have been used comparatively to predict death cases for the future course of the COVID-19 outbreak. These approaches proposed for estimation appear to result in few errors and perform well in providing information on the course of deaths in the epidemic. The agreement between the predicted results by these methods, and the actual data proves the superiority of the proposed ones in forecasting accuracy in future cases. This is expected to provide significant benefits in increasing the effectiveness of health policies to be implemented within the scope of the measures to be taken for the future of this and similar epidemics. As this investigation reveals that the current modelling methods have undeniable advantages in predicting epidemic trends, using our models is believed to provide an accurate estimate of death rates and guide policymakers in formulating research, health, socio-economic and fiscal policies. All these findings can be widely regarded as significant milestones and essential guides for researchers examining potential future epidemic tendencies. In addition, although this epidemic is quite complex and varies from country-to-country and various factors, the proposed approaches offer a great opportunity to model the outbreak in other epidemics as well as in other countries.
dc.identifier.doi10.14744/sigma.2024.00062
dc.identifier.endpage678
dc.identifier.issn1304-7205
dc.identifier.issn1304-7191
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85200589090
dc.identifier.scopusqualityN/A
dc.identifier.startpage667
dc.identifier.urihttps://doi.org/10.14744/sigma.2024.00062
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14541
dc.identifier.volume42
dc.identifier.wosWOS:001315910100005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherYildiz Technical Univ
dc.relation.ispartofSigma Journal of Engineering and Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Intelligence Modelling
dc.subjectCOVID-19
dc.subjectEpidemic Disease
dc.subjectForecasting Model
dc.subjectRegression Model
dc.titleAn investigation on the estimation of the impact factors of pandemic deaths with artificial neural network and multiple regression algorithms: Covid-19 case
dc.typeArticle

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