Clustering framework to cope with COVID-19 for cities in Turkey

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dc.authoridOZDEN, ERDEMALP/0000-0001-5019-1675
dc.contributor.authorGuleryuz, Didem
dc.contributor.authorOzden, Erdemalp
dc.date.accessioned2024-10-04T18:48:33Z
dc.date.available2024-10-04T18:48:33Z
dc.date.issued2021
dc.departmentBayburt Üniversitesien_US
dc.description.abstractIntroduction: This article is the product of the research Clustering Framework to Cope with COVID-19 for Cities in Turkey, developed at Bayburt University in 2021. Problem: Turkey's risk map, presented in January 2021, to take local decisions in tackling the COVID-19 pandemic, was based on confirmed cases only. Health, socio-economic and environmental indicators are also important for management decisions of COVID-19. The risk map to be designed by adding these indicators will support more effective decisions. Objective: The research aims to propose a clustering scheme to design a risk map of cities for Turkey. Methodology: The unsupervised clustering algorithm suggested dividing the cities of Turkey into clusters, considering health, socio-economic, environmental indicators, and the spread pattern of COVID-19. Results: We found that cities are clustered into five groups while megacity Istanbul alone formed a cluster, three of Turkey's largest cities formed another cluster. Other clusters consist of 19, 26, and 32 cities, respectively. The most important determinants which have predictive power are identified. Conclusion: The suggested clustering method can be a decision support system for policymakers to determine the differences and similarities of cities in quarantine decisions and normalization phases for the following periods of the pandemic. Originality: To the best of our knowledge, this study differs from previous studies because countries were grouped in previous studies by only considering the confirmed cases. In this study, cities were clustered in terms of the health, socio-economic, and environmental indicators to make decisions locally. Limitations: The distribution of confirmed cases by age could be added, especially to make decisions about education, but this data is not officially announced.en_US
dc.identifier.doi10.16925/2357-6014.2021.03.06
dc.identifier.issn1900-3102
dc.identifier.issn2357-6014
dc.identifier.issue3en_US
dc.identifier.urihttps://doi.org/10.16925/2357-6014.2021.03.06
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3087
dc.identifier.volume17en_US
dc.identifier.wosWOS:000747977300004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherUniv Cooperative Colombia, Fac Engineeringen_US
dc.relation.ispartofIngenieria Solidariaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectUnsupervised Learningen_US
dc.subjectClustering Algorithmen_US
dc.subjectDecision Support Systemen_US
dc.titleClustering framework to cope with COVID-19 for cities in Turkeyen_US
dc.typeArticleen_US

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