Clustering framework to cope with COVID-19 for cities in Turkey
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.
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MinHealth, “Republic of Turkey Ministry of Health,” 2021. https://covid19.saglik.gov.tr/TR-66494/pandemi.html.
WHO, “WHO Coronavirus (COVID-19) Dashboard,” 2020. https://covid19.who.int/.
JohnsHopkins, “Johns Hopkins University COVID-19 Data,” 2021. https://coronavirus.jhu.edu/map.html.
M. Liu et al., “The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level,” Sci. Total Environ., vol. 777, p. 145992, 2021, doi: 10.1016/j.scitotenv.2021.145992.
D. Guleryuz, “Forecasting Outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s Exponential Smoothing and Long Short-Term Memory Models,” Process Saf. Environ. Prot., 2021, doi: https://doi.org/10.1016/j.psep.2021.03.032.
K. Nikolopoulos, S. Punia, A. Schäfers, C. Tsinopoulos, and C. Vasilakis, “Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions,” Eur. J. Oper. Res., vol. 290, no. 1, pp. 99–115, 2021, doi: 10.1016/j.ejor.2020.08.001.
P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, “Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps,” Chaos, Solitons and Fractals, vol. 138, 2020, doi: 10.1016/j.chaos.2020.109917.
M. R. Mahmoudi, D. Baleanu, Z. Mansor, B. A. Tuan, and K. H. Pho, “Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries,” Chaos, Solitons and Fractals, vol. 140, pp. 1–9, 2020, doi: 10.1016/j.chaos.2020.110230.
A. Olivieri, G. Palù, and G. Sebastiani, “COVID-19 cumulative incidence, intensive care, and mortality in Italian regions compared to selected European countries,” Int. J. Infect. Dis., vol. 102, pp. 363–368, 2021, doi: 10.1016/j.ijid.2020.10.070.
B. Kucukefe, “Covid-19’un OECD Ülkeleri ve Çin’de Makroekonomik Etkisinin Kümeleme Analizi,” Ekon. Polit. Finans Araştırmaları Derg., vol. 5, pp. 280–291, 2020, doi: 10.30784/epfad.811289.
M. Azarafza, M. Azarafza, and H. Akgün, “Clustering method for spread pattern analysis of corona-virus (COVID-19) infection in Iran,” medRxiv, 2020, doi: 10.1101/2020.05.22.20109942.
S. A. Rizvi, M. Umair, and M. A. Cheema, “Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators,” medRxiv, 2021, doi: 10.1101/2021.02.15.21251762.
V. Zarikas, S. G. Poulopoulos, Z. Gareiou, and E. Zervas, “Clustering analysis of countries using the COVID-19 cases dataset,” Data Br., vol. 31, p. 105787, 2020, doi: https://doi.org/10.1016/j.dib.2020.105787.
R. M. Carrillo-Larco and M. Castillo-Cara, “Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach,” Wellcome Open Res., vol. 5, pp. 1–22, 2020, doi: 10.12688/wellcomeopenres.15819.3.
TurkStat, “Turkish Statistical Institute,” 2020. http://www.tuik.gov.tr/Start.do (accessed Jun. 13, 2020).
ILO, “International Labour Organization,” 2020. https://www.ilo.org/.
Worldbank, “World Bank Open Data,” 2021. https://data.worldbank.org/.
D. Guleryuz, “Evaluation of waste management using clustering algorithm in megacity Istanbul,” Environ. Res. Technol., vol. 3, no. 3, pp. 102–112, 2020.
B. Purnima and K. Arvind, “EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN,” Int. J. Comput. Appl., vol. 105, no. 9, pp. 17–24, 2014, [Online]. Available: https://www.ijcaonline.org/archives/volume105/number9/18405-9674.
P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987, doi: https://doi.org/10.1016/0377-0427(87)90125-7.
J. L. Myers and A. D. Well, Research Design and Statistical Analysis, 2nd Ed. Lawrence Erlbaum., 2003.
S. Wright, “Correlation and causation,” J. Agric. Res., vol. 20, no. 7, pp. 557–585, 1921.




