Forecasting outbreak of COVID-19 in Turkey; Comparison of Box?Jenkins, Brown?s exponential smoothing and long short-term memory models

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dc.authoridGULERYUZ, DIDEM/0000-0003-4198-9997
dc.contributor.authorGuleryuz, Didem
dc.date.accessioned2024-10-04T18:51:01Z
dc.date.available2024-10-04T18:51:01Z
dc.date.issued2021
dc.departmentBayburt Üniversitesien_US
dc.description.abstractThe new coronavirus disease (COVID-19), which first appeared in China in December 2019, has pervaded throughout the world. Because the epidemic started later in Turkey than other European countries, it has the least number of deaths according to the current data. Outbreak management in COVID-19 is of great importance for public safety and public health. For this reason, prediction models can decide the precautionary warning to control the spread of the disease. Therefore, this study aims to develop a forecasting model, considering statistical data for Turkey. Box-Jenkins Methods (ARIMA), Brown's Exponential Smoothing model and RNN-LSTM are employed. ARIMA was selected with the lowest AIC values (12.0342,-2.51411, 12.0253, 3.67729,-4.24405, and 3.66077) as the best fit for the number of total case, the growth rate of total cases, the number of new cases, the number of total death, the growth rate of total deaths and the number of new deaths, respectively. The forecast values of the number of each indicator are stable over time. In the near future, it will not show an increasing trend in the number of cases for Turkey. In addition, the pandemic will become a steady state and an increase in mortality rates will not be expected between 17-31 May. ARIMA models can be used in fresh outbreak situations to ensure health and safety. It is vital to make quick and accurate decisions on the precautions for epidemic preparedness and management, so corrective and preventive actions can be updated considering obtained values. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.psep.2021.03.032
dc.identifier.endpage935en_US
dc.identifier.issn0957-5820
dc.identifier.issn1744-3598
dc.identifier.pmid33776248en_US
dc.identifier.scopus2-s2.0-85104076262en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage927en_US
dc.identifier.urihttps://doi.org/10.1016/j.psep.2021.03.032
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3331
dc.identifier.volume149en_US
dc.identifier.wosWOS:000646139800006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofProcess Safety and Environmental Protectionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBox-Jenkins methoden_US
dc.subjectBrown?s exponential smoothing modelen_US
dc.subjectLSTMen_US
dc.subjectCOVID-19 forecastingen_US
dc.titleForecasting outbreak of COVID-19 in Turkey; Comparison of Box?Jenkins, Brown?s exponential smoothing and long short-term memory modelsen_US
dc.typeArticleen_US

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