Clustering Framework to Cope with COVID-19 for Cities in Turkey

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY
D. Guleryuz, Erdemalp Ozden
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引用次数: 0

Abstract

Introduction: 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 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.  
土耳其城市应对新冠肺炎的集群框架
简介:本文是Bayburt大学2021年开发的“土耳其城市应对新冠肺炎的聚类框架”研究的产物。问题:土耳其于2021年1月提出的应对新冠肺炎疫情的地方决策风险图仅基于确诊病例。健康、社会经济和环境指标对于新冠肺炎的管理决策也很重要。通过增加这些指标设计的风险图将支持更有效的决策。目的:本研究旨在提出一种聚类方案,以设计土耳其城市的风险地图。方法:无监督聚类算法建议将土耳其的城市划分为集群,考虑健康、社会经济、环境指标和新冠肺炎的传播模式。结果:我们发现,城市分为五组,仅伊斯坦布尔一个特大城市就形成了一个集群,土耳其三个最大的城市形成了另一个集群。其他集群分别由19个、26个和32个城市组成。确定了具有预测能力的最重要的决定因素。结论:建议的聚类方法可以作为决策者的决策支持系统,以确定城市在疫情后续时期的隔离决策和正常化阶段的差异和相似性。独创性:据我们所知,这项研究与以前的研究不同,因为以前的研究只考虑确诊病例对国家进行分组。在这项研究中,根据健康、社会经济和环境指标对城市进行了聚类,以在当地做出决策。限制:可以增加确诊病例按年龄的分布,特别是在做出教育决策时,但这一数据尚未正式公布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Ingenieria Solidaria
Ingenieria Solidaria ENGINEERING, MULTIDISCIPLINARY-
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