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Ülkelerin Covid-19 Pandemisi Dönemindeki Ekonomik ve Epidemik Performanslarının Analizi

Year 2022, Volume: 10 Issue: 2, 729 - 747, 30.04.2022
https://doi.org/10.29130/dubited.934715

Abstract

Bu çalışma, ülkelerin COVID-19 dönemindeki performansını analiz etmeyi amaçlamaktadır. Çalışmanın temel motivasyonu, salgın bilgileri ve sağlık sisteminin özelliklerinin yanı sıra hükümet tedbiri (Kısıtlama İndeksi) ve ekonomik kriterleri de dikkate alarak daha gerçekçi bir değerlendirme yapmaktır. Böylelikle pandemiyle mücadelede öne çıkan ülkelerin özellikleri analiz edilmeye çalışılmıştır. Çalışma kapsamında kriterlerin ağırlıklandırılmasında yaygın olarak kullanılan ve nesnel bir yöntem olarak öne çıkan CRITIC yöntemi tercih edilmiştir. Ülke performansları ise ağırlıklı ve ağırlıksız kriterler kullanılarak ayrı ayrı analiz edilmiştir. Ülke sıralamalarını belirlemek için ağırlıklı ve ağırlıksız kriterlerle birlikte Gri İlişkisel Analiz (GİA) yöntemi kullanılmıştır. Sonuçlar incelendiğinde, ekonomik refah düzeyinin ve pandemiye karşı alınan önlemlerin ülkeleri doğrudan avantajlı bir noktaya getirdiği görülmüştür. Diğer ülkelere kıyasla nispeten düşük bir ekonomik refah seviyesine sahip ülkeler, maalesef sıralamada daha alt sıralarda yer aldı. Öte yandan yaşlı nüfusa sahip ülkeler, geniş ekonomik fırsatlarına rağmen yüksek ölüm oranları nedeniyle alt sıralarda yer bulabildiler. Kriterlerin ağırlıklandırılması ülke sıralamalarını etkilemekle birlikte ilk ikide yer alan ülkelerde herhangi bir değişiklik olmamıştır.

References

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  • [15] E. K. Peker, G. Bektemur, K. N. Baydili, and M. Aktaş, “Comparison of COVID-19 Case-Fatality-Rates By Socio-Demographic Factors”, Orginal Investigation, Vol. 10, No. 3, pp. 246-251, 2020.
  • [16] B. Tekin, “Classification of Countries in the Context of COVID-19, Health and Financial Indicators During the COVID-19 Pandemic Period: Hierarchical Clustering Analysis”, Journal of Finance Economy and Social Research, Vol. 5, No. 2, pp. 336-349, 2020.
  • [17] V. Jain, and L. Singh, “Global Spread and Socio-Economic Determinants of COVID-19 Pandemic”, Seoul Journal of Economics, Vol. 33, No. 4, pp. 561-599, 2020.
  • [18] S. Sannigrahi, F. Pilla, B. Basu, A. S. Basu, and A. Molter, “Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach”, Sustainable cities and society, Vol. 62, pp. 102418, 2020.
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  • [23] D. Diakoulaki, G. Mavrotas, and L. Papayannakis, “Determining Objective Weights in Multiple Criteria Problems: The Critic Method”, Computers & Operations Research, Vol. 22, No. 7, pp. 763-770, 1995.
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  • [26] Y. Şahin, and E. Aydemir, “An AHP-Weighted Gray Relational Analysis Method to Determine the Technical Characteristics' Importance Levels of the Smartphone”, Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, Vol. 14, No. 1, pp. 225-238, 2019.
  • [27] M. S. Yurtyapan, and E. Aydemir, “ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method”, Grey Systems: Theory and Application, doi: https://doi.org/10.1108/GS-01-2021-0002, 2021.
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  • [29] Y. Ayrıçay, M. Özçalıcı, and K. Ahmet, “Using Grey Relational Analysis as a Financial Benchmarking Tool: An Implementation for ISE-30 Non-Financial Firms”, Kahramanmaraş Sütçü İmam University Journal of Social Sciences, Vol. 10, No. 1; pp. 219-238, 2013.
  • [30] E. Aydemir, F. Bedir, and G. Özdemir, “Grey System Theory And Applications: A Literature Review”, Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, Vol. 18, No. 3, pp. 187-200, 2013.
  • [31] R. S. He, and S. F. Hwang, “Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 7, pp. 980-992, 2007.
  • [32] Z. Song, and X. Yan, “Critical Path for a Grey Interval Project Network”. Liu, S. and Forres J.Y.L (Eds), Advances in Grey Systems Research, Springer, Berlin, Heidelberg, pp 37-46, 2010.
  • [33] M. L. Tseng, “A causal and Effect Decision Making Model of Service Quality Expectation Using Grey-Fuzzy DEMATEL Approach”, Expert Systems with Applications, Vol. 36, No. 4, pp. 7738-7748, 2009.
  • [34] E. K. Zavadskas, Z. Turskis, and J. Tamošaitiene, “Risk assessment of construction projects”, Journal of civil engineering and management, Vol. 16, No. 1, pp. 33-46, 2010.
  • [35] C. C. Yang, and B. S. Chen, “Supplier selection using combined analytical hierarchy process and grey relational analysis”, Journal of Manufacturing Technology Management, Vol. 17, No. 7, pp. 926-941, 2006.
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  • [40] E. Yılmaz, and F. Güngör, (2010), Gri İlişkisel Analiz Yöntemine Göre Farklı Sertliklerde Optimum Takım Tutucusunun Belirlenmesi. Tasarım İmalat ve Analiz Kongresi ve CAD-CAM Günleri 2012, Balıkesir, Turkiye. [Online]. Available: http://timak.balikesir.edu.tr/pdf2010/1-9.pdf .
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Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period

Year 2022, Volume: 10 Issue: 2, 729 - 747, 30.04.2022
https://doi.org/10.29130/dubited.934715

Abstract

This study aims to analyze the performance of countries in the COVID-19 period. The main motivation of the study is to make a more realistic assessment by taking into account the epidemic information and health system-related features, as well as government precaution (Stringency Index) and economic criteria. In this way, the characteristics of the countries that stand out in the fight against the pandemic were tried to be determined. Within the scope of the study, the CRITIC method, which is widely used and stands out as an objective method, was preferred for weighting the criteria. Country performances were analyzed separately using weighted and unweighted criteria. The Grey Relational Analysis (GRA) method, together with weighted and unweighted criteria, was used to determine country rankings. When the results are examined, it has been observed that the level of economic prosperity and the measures taken against the pandemic has brought countries directly to an advantageous point. Countries with a relatively low level of economic prosperity compared to other countries, unfortunately, ranked lower in the ranking. On the other hand, countries with elderly populations were able to find a place in the lower ranks due to high mortality rates despite their extensive economic opportunities. Although the weighting of the criteria affects the country rankings, there has been no change in the countries in the top two.

References

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  • [2] N. Aydın, and G. Yurdakul, “Assessing Countries’ Performances Against COVID-19 via WSIDEA and Machine Learning Algorithms”, Applied Soft Computing Journal. Vol. 97, p. 106792, 2020.
  • [3] S. Kabadayi, G. E. O’Connor, and S. Tuzovic, “The impact of coronavirus on service ecosystems as service mega-disruptions”, Journal of Services Marketing, Vol. 34, No. 6, pp. 809-817, 2020.
  • [4] Coronavirus (COVID-19) Vaccinations 2020 to 2021. Our World in Data. April 2021. [online]. Available: https://ourworldindata.org/covid-vaccinations.
  • [5] J. H. Tanne, E. Hayasaki, M. Zastrow, P. Pulla, P. Smith, and A. G Rada, “Covid-19: how doctors and healthcare systems are tackling coronavirus worldwide”, Bmj, Vol. 368, 2020.
  • [6] M. Di Marco, M. L. Baker, P. Daszak, P. De Barro, E. A. Eskew, C. M. Godde,... and S. Ferrier, “Opinion: Sustainable development must account for pandemic risk”, Proceedings of the National Academy of Sciences, Vol. 117, No. 8, pp. 3888-3892, 2020.
  • [7] V. Stojkoski, Z. Utkovski, P. Jolakoski, D. Tevdovski, and L. Kocarev, “The socio-economic determinants of the coronavirus disease (COVID-19) pandemic”, arXiv preprint arXiv:2004.07947, 2020.
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  • [9] N. Fernandes, “Economic effects of coronavirus outbreak (COVID-19) on the world economy”. Available at SSRN 3557504, 2020.
  • [10] Latest World Economic Outlook Growth Projections 2020. International Monetary Fund. April 2021. [online]. Available: https://www.imf.org/en/Publications/WEO/Issues/2021/03/23/world-economic-outlook-april-2021.
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  • [12] Unemployment, total (% of total labor force) (modeled ILO estimate). The World Bank. April 2021. [online]. Available: https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS.
  • [13] İ. Koç, and F. Yardımcıoğlu, “COVID-19 Process Application Standing Pandemic Measures and Comparative Analysis of Financial Incentives: Turkey and Selected EU Countries Comparison”, Journal of Politics Economics and Management Research, Vol. 8, No. 2, pp. 123-152, 2020.
  • [14] A. Bilinski, and E. J. Manuel, “COVID-19 and Excess All-Cause Mortality in the US and 18 Comparison Countries”, Jama, Vol. 324, No. 20, pp. 2100-2102, 2020.
  • [15] E. K. Peker, G. Bektemur, K. N. Baydili, and M. Aktaş, “Comparison of COVID-19 Case-Fatality-Rates By Socio-Demographic Factors”, Orginal Investigation, Vol. 10, No. 3, pp. 246-251, 2020.
  • [16] B. Tekin, “Classification of Countries in the Context of COVID-19, Health and Financial Indicators During the COVID-19 Pandemic Period: Hierarchical Clustering Analysis”, Journal of Finance Economy and Social Research, Vol. 5, No. 2, pp. 336-349, 2020.
  • [17] V. Jain, and L. Singh, “Global Spread and Socio-Economic Determinants of COVID-19 Pandemic”, Seoul Journal of Economics, Vol. 33, No. 4, pp. 561-599, 2020.
  • [18] S. Sannigrahi, F. Pilla, B. Basu, A. S. Basu, and A. Molter, “Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach”, Sustainable cities and society, Vol. 62, pp. 102418, 2020.
  • [19] Y. Cao, A. Hiyoshi, and S. Montgomery, “COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data”, BMJ open, Vol 10, No. 11, pp. e043560, 2020.
  • [20] R. A. Middelburg, and F. R. Rosendaal, “COVID-19: How to Make Between-Country Comparisons”, International Journal of Infectious Diseases, Vol. 96, pp. 477-481, 2020.
  • [21] B. F. Dağcıoğlu, and A. Keskin, “COVID-19 Pandemic Process in Turkey, Europe and America Comparison of Data: A Cross-Sectional Study”, Ankara Medical Journal, Vol. 2, pp. 360-369, 2020.
  • [22] F. Selamzade, and Y. Özdemir, “Evaluation of the Effectiveness of OECD Countries Against COVID-19 with DEA”, Turkish Studies, Vol. 15, No. 4, pp. 977-991, 2020.
  • [23] D. Diakoulaki, G. Mavrotas, and L. Papayannakis, “Determining Objective Weights in Multiple Criteria Problems: The Critic Method”, Computers & Operations Research, Vol. 22, No. 7, pp. 763-770, 1995.
  • [24] A. Jahan, F. Mustapha, S. M. Sapuan, M. Y. Ismail, and M. Bahraminasab, “A Framework for Weighting of Criteria in Ranking Stage of Material Selection Process”, The International Journal of Advanced Manufacturing Technology, Vol. 58, No. 1, pp. 411-420, 2012.
  • [25] J. L. Deng, “Control problems of grey systems”, Systems & Control Letters, Vol. 1, No. 5, pp. 288-294, 1982.
  • [26] Y. Şahin, and E. Aydemir, “An AHP-Weighted Gray Relational Analysis Method to Determine the Technical Characteristics' Importance Levels of the Smartphone”, Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, Vol. 14, No. 1, pp. 225-238, 2019.
  • [27] M. S. Yurtyapan, and E. Aydemir, “ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method”, Grey Systems: Theory and Application, doi: https://doi.org/10.1108/GS-01-2021-0002, 2021.
  • [28] J. Deng, “Introduction to Grey System Theory”, The Journal of Grey System, Vol. 1, pp. 1-24, 1989.
  • [29] Y. Ayrıçay, M. Özçalıcı, and K. Ahmet, “Using Grey Relational Analysis as a Financial Benchmarking Tool: An Implementation for ISE-30 Non-Financial Firms”, Kahramanmaraş Sütçü İmam University Journal of Social Sciences, Vol. 10, No. 1; pp. 219-238, 2013.
  • [30] E. Aydemir, F. Bedir, and G. Özdemir, “Grey System Theory And Applications: A Literature Review”, Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, Vol. 18, No. 3, pp. 187-200, 2013.
  • [31] R. S. He, and S. F. Hwang, “Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 7, pp. 980-992, 2007.
  • [32] Z. Song, and X. Yan, “Critical Path for a Grey Interval Project Network”. Liu, S. and Forres J.Y.L (Eds), Advances in Grey Systems Research, Springer, Berlin, Heidelberg, pp 37-46, 2010.
  • [33] M. L. Tseng, “A causal and Effect Decision Making Model of Service Quality Expectation Using Grey-Fuzzy DEMATEL Approach”, Expert Systems with Applications, Vol. 36, No. 4, pp. 7738-7748, 2009.
  • [34] E. K. Zavadskas, Z. Turskis, and J. Tamošaitiene, “Risk assessment of construction projects”, Journal of civil engineering and management, Vol. 16, No. 1, pp. 33-46, 2010.
  • [35] C. C. Yang, and B. S. Chen, “Supplier selection using combined analytical hierarchy process and grey relational analysis”, Journal of Manufacturing Technology Management, Vol. 17, No. 7, pp. 926-941, 2006.
  • [36] A. I. Ozdemir, and M. Deste, “Multicriteria supplier selection by gray relational analysis: a case study in automotive industry”, Istanbul University Journal of the School of Business Administration, Vol. 38, No. 2, pp. 147-156, 2009.
  • [37] E. Çakır, and G. Akel, “Evaluation of Service Quality of Hotel And Holiday Reservation Web Sites In Turkey By Integrated Swara-Gray Relationship Analysis Method”, PressAcademia Procedia, Vol. 3, No. 1, pp. 81-95, 2017.
  • [38] Liu, D. “E-commerce system security assessment based on grey relational analysis comprehensive evaluation”, International Journal of Digital Content Technology and its Applications, Vol. 5, No. 10, pp. 279-284, 2011.
  • [39] E. Aydemir, and Y. Şahin, “Evaluation of healthcare service quality factors using grey relational analysis in a dialysis center”, Grey Systems: Theory and Application, Vol. 9, No. 4, pp. 432-448, 2019.
  • [40] E. Yılmaz, and F. Güngör, (2010), Gri İlişkisel Analiz Yöntemine Göre Farklı Sertliklerde Optimum Takım Tutucusunun Belirlenmesi. Tasarım İmalat ve Analiz Kongresi ve CAD-CAM Günleri 2012, Balıkesir, Turkiye. [Online]. Available: http://timak.balikesir.edu.tr/pdf2010/1-9.pdf .
  • [41] L. Y. Zhai, L. P. Khoo, and Z. W. Zhong, “Design concept evaluation in product development using rough sets and grey relation analysis”, Expert systems with applications, Vol. 36, No. 3, pp. 7072-7079, 2009.
  • [42] Z. C. Lin, and C. Y. Ho, “Analysis and application of grey relation and ANOVA in chemical–mechanical polishing process parameters”, The International Journal of Advanced Manufacturing Technology, Vol. 21, No. 1, pp. 10-14, 2003.
  • [43] C. L. Lin, J. L. Lin, and T. C. Ko, “Optimization of the EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method”, The International Journal of Advanced Manufacturing Technology, Vol. 19, No. 4, pp. 271-277, 2002.
  • [44] Per capita GDP at current prices-US dollars. Undata. April 2021. [Online]. Available: https://data.un.org/Data.aspx?q=GDP+Per+Capita+(%24)&d=SNAAMA&f=grID%3a101%3bcurrID%3aUSD%3bpcFlag%3a1.
  • [45] Per capita total expenditure on health. Undata. April 2021. [Online]. Available: http://data.un.org/Data.aspx?d=WHO&f=MEASURE_CODE%3aWHS7_105.
  • [46] Unemployment rate. Undata. April 2021. [Online]. Available: http://data.un.org/Data.aspx?q=Unemployment+rate+&d=GenderStat&f=inID%3a121.
  • [47] Consumer prices. Undata. April 2021. [Online]. Available: http://data.un.org/Data.aspx?q=Consumer+price+index+&d=IFS&f=SeriesCode%3a64.
There are 47 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Yusuf Şahin 0000-0002-3862-6485

Merve Kılınç 0000-0001-6455-5644

Publication Date April 30, 2022
Published in Issue Year 2022 Volume: 10 Issue: 2

Cite

APA Şahin, Y., & Kılınç, M. (2022). Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 10(2), 729-747. https://doi.org/10.29130/dubited.934715
AMA Şahin Y, Kılınç M. Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period. DUBİTED. April 2022;10(2):729-747. doi:10.29130/dubited.934715
Chicago Şahin, Yusuf, and Merve Kılınç. “Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 10, no. 2 (April 2022): 729-47. https://doi.org/10.29130/dubited.934715.
EndNote Şahin Y, Kılınç M (April 1, 2022) Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 10 2 729–747.
IEEE Y. Şahin and M. Kılınç, “Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period”, DUBİTED, vol. 10, no. 2, pp. 729–747, 2022, doi: 10.29130/dubited.934715.
ISNAD Şahin, Yusuf - Kılınç, Merve. “Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 10/2 (April 2022), 729-747. https://doi.org/10.29130/dubited.934715.
JAMA Şahin Y, Kılınç M. Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period. DUBİTED. 2022;10:729–747.
MLA Şahin, Yusuf and Merve Kılınç. “Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, vol. 10, no. 2, 2022, pp. 729-47, doi:10.29130/dubited.934715.
Vancouver Şahin Y, Kılınç M. Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period. DUBİTED. 2022;10(2):729-47.