Using Multi-Criteria Decision Methods Evaluation of The Fight Against the Covid-19 Pandemic: The Case Of N-11 Countries

Yıl 2025, Cilt: 24 Sayı: 1, 169 - 189, 29.01.2025

Tuğba Gökdemir , Gül Emel

https://doi.org/10.17755/esosder.1514293

Öz

The recent COVID-19 pandemic has posed a global threat to the human population, severely influencing countries' health systems and economies. In this process, countries' policymakers have used all available means to cope with this unpredictable crisis, taking various measures and developing response strategies. Although some of these measures and strategies were similar, the impact of the COVID-19 pandemic varied significantly across countries, especially regarding the number of cases and deaths. To analyze this difference, studies need to evaluate the strength of the possibilities, limitations, and measures taken by countries in the fight against the COVID-19 pandemic. In this study, a study within the scope of N-11 countries, including Turkey, is discussed and the performances of these 11 countries in the fight against COVID-19 are compared. Multi-Criteria Decision Making methods were used for performance analysis and health and economic evaluation criteria were taken as the basis. ENTROPI was used to assign weights to the criteria and TOPSIS, VIKOR, and COPRAS methods were preferred for performance ranking. The BORDA Counting method was used to obtain an integrated ranking. According to the study results, South Korea ranked first, Turkey ranked second and the Philippines ranked eleventh in terms of performance in the fight against COVID-19.
The most important criteria in determining performance are Case/Death, Tests per Million, and Deaths per Million.

Anahtar Kelimeler

COVID-19 Struggle, N-11 Countries, Multi-Criteria Decision Analyses

Çok Kriterli Karar Yöntemleri Kullanılarak Covıd-19 Pandemisiyle Mücadelenin Değerlendirilmesi: N-11 Ülkeleri Örneği

Öz

Yakın zamanda meydana gelen COVID-19 pandemisi insan nüfusu için küresel bir tehdit oluşturmuş, ülkelerin sağlık sistemleri ve ekonomileri üzerinde ciddi baskılar yaratmıştır. Bu süreçte ülkelerin politika yapıcıları, bu öngörülemez krizle başa çıkmak için mevcut tüm imkanlarını kullanmış, çeşitli önlemler almış ve mücadele stratejileri oluşturmuşlardır. Bu önlem ve stratejilerin bir kısmının benzer olmasına rağmen COVID-19 pandemisinin etkisi, özellikle vaka ve ölüm sayıları açısından ülkeler arasında önemli farklılık göstermiştir. Bu farklılığı analiz etmek için, ülkelerin COVID-19 pandemisi ile mücadelede ellerindeki imkanların gücünü, kısıtlamaları ve aldıkları önlemleri değerlendiren çalışmalara ihtiyaç doğmuştur. Bu çalışmada, Türkiye'nin de içinde bulunduğu N-11 ülkeleri kapsamında bir araştırma ele alınmış ve söz konusu 11 ülkenin COVID-19 ile mücadele sürecindeki performansları karşılaştırılmıştır. Performans analizi için Çok Kriterli Karar Verme yöntemleri kullanılmış ve sağlık ile ekonomik değerlendirme kriterleri temel alınmıştır. Kriterlere ağırlık atamak için ENTROPİ, performans sıralaması için TOPSIS, COPRAS ve VIKOR yöntemleri tercih edilmiştir. Bütünleşik bir sıralama elde etmek için ise Borda yöntemi kullanılmıştır. Çalışmanın sonuçlarına göre; COVID-19 ile mücadele performansında Güney Kore birinci sırada, Türkiye ikinci sırada ve Filipinler ise onbirinci sırada yer almıştır. Performans belirlemede en önemli kriterler ise Vaka/Ölüm, Milyon başına test ve Milyon başına ölüm kriterleri elde edilmiştir.

Anahtar Kelimeler

COVID-19 Mücadelesi, N-11 Ülkeleri, Çok Kriterli Karar Analizi

Kaynakça

• Alamoodi, A. H., Zaidan, B. B., Albahri, O. S., Garfan, S., Ahmaro, I. Y., Mohammed, R. T., & Malik, R. Q. (2023). A systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. Complex & Intelligent Systems, 9(4), 4705-4731.
• Amoozad, M. H., Arzaghi, S., Stauskis, G., & Zavadskas, E. K. (2018). A hybrid fuzzy BWM-COPRAS method for analyzing key factors of sustainable architecture. Sustainability, 10(5), 1626.
• Baysan, C. (2021). Sağlık sistemi bağlamında COVID-19 salgını: Güney Kore. Toplum ve Hekim Dergisi, 36(6), Kasım-Aralık.
• Breitenbach, M.C., Ngobeni, V., & Aye, G.C. (2021). Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates. Frontiers in Public Health, 9, 638481. https://doi.org/10.3389/fpubh.2021.638481.
• Bretschger, L., Grieg, E., Welfens, P.J.J. & Xiong, T. (2020), “COVID-19 Infections and Fatalities Developments: Empirical Evidence for OECD Countries and Newly Industrialized Economies”, Int Econ Econ Policy, https://doi.org/10.1007/s10368-020-00487-x (Erişim Tarihi:15.08.2021)
• Daglar Macar, O., & Asal, U. Y. (2020). COVID-19 İLE ULUSLARARASI İLİŞKİLERİ YENİDEN DÜŞÜNMEK: TARİH, EKONOMİ VE SİYASET EKSENİNDE BİR DEĞERLENDİRME. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 19(37), 222-239.
• Duro, J. A., Perez-Laborda, A., Turrion-Prats, J., & Fernández-Fernández, M. (2021). COVID-19 and tourism vulnerability. Tourism Management Perspectives, 38, 100819.
• Ecer, F., & Pamucar, D. (2021). MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services. Applied Soft Computing, Article 107199.
• Gökdemir, T., & Emel, G.G. (2023). BIST BANKA’da işlem gören bankaların COVID-19 Pandemi dönemindeki finansal performanslarının farklı kriter ağırlıklandırma yöntemleri ile analizi. BMIJ Dergisi, 11(3), 1163-1190.
• Göztepe, B. H., & Çetin, E. (2016). OECD’ye Üye Ülkelerin Sağlık Sisteminin Vıkor Yöntemiyle Değerlendirilmesi. Uluslararası Yönetim, Ekonomi Ve Politika Kongresi Bildiriler Kitabı, Cilt 2 (s. 1466-1480). İstanbul-Türkiye: International Congress of Management Economy and Policy | Proceedings Book.
• Hamzah, N., Yu, M-M., & See, K.F. (2021). Assessing the efficiency of Malaysia health system in COVID-19 prevention and treatment response. Health Care Management Science, 24, 273-285, https://doi.org/10.1007/s10729-020-09539-9
• Hamzelou, J. (2020). WHO Expert: We need more testing to beat coronavirus. New Scientist. https://www.newscientist.com/article/2237544-who-expert-we-need-more-testing-to-beat-coronavirus/.
• Hasell, J. (2020). What can data on testing tell us about the pandemic? Our World in Data, https://ourworldindata.org/what-can-data-on-testing-tell-us-about-the-pandemic.
• Hensher, M. (2020). COVID-19, unemployment, and health: time for deeper solutions? BMJ, 371, 1-4.
• Jeon J., Krishnan S., Manirathinam T., Narayanamoorthy S., Nazir Ahmad M., Ferrara M., & Ahmadian, A. (2023). An innovative probabilistic hesitant fuzzy set MCDM perspective for selecting flexible packaging bags after the prohibition on single-use plastics Scientific Reports, 13 (1), p. 10206, 10.1038/s41598-023-37200-2
• Ishizaka A., & Nemery P., (2013). Multi-Criteria Decision Analysis: Methods and Software. John Wiley & Sons Ltd, p. 210.
• Karakaş Geyik, S., Satman, M. H., & Kalyoncu, G. (2022). G20 Ülkelerinin Covid-19 Pandemisi ile Mücadele Performanslarının Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi. EKOIST Journal of Econometrics and Statistics(37), 27-52. https://doi.org/10.26650/ekoist.2022.37.1161945
• Khan, A., Abosuliman S.S., Ashraf, S., & Abdullah, S. (2021). Hospital admission and care of COVID-19 patients problem based on spherical hesitant fuzzy decision support system. International Journal of Intelligent Systems, 36(8), 4167–4209. https://doi.org/10.1002/int.22455
• Kheybari, S., Ishizaka, A., & Salamirad, A. (2023). A new hybrid risk-averse best-worst method and portfolio optimization to select temporary hospital locations for COVID-19 patients. Journal of the Operational Research Society, 74(2), 509-526.
• Kruse, F. M., & Jeurissen, P.P.T. (2020). For-Profit Hospitals Out of Business? Financial Sustainability During the COVID-19 Epidemic Emergency Response. International Journal of Health Policy and Management, 9(10), 423-428. https://doi.org/10.34172/ijhpm.2020.67.
• Korzeb, Z., & Niedziółka, P. (2020). Resistance of commercial banks to the crisis caused by the COVID-19 pandemic: the case of Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 205–234. https://doi.org/10.24136/eq.2020.010
• Kuosmanen, T., Tan, Y. & Dai, S. (2023). Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic. Health Care Manag Sci 26, 447–460. https://doi.org/10.1007/s10729-023-09634-7
• Lupu, D., & Tiganasu, R. (2022). COVID-19 and the efficiency of health systems in Europe. Health Economic Review, https://doi.org/10.1186/s13561-022-00358-y.
• Mateo, J. R. S. C. (2012). Multi-criteria analysis in the renewable energy industry. Springer Science & Business Media.
• Majumder, P., Biswas, P., Majumder, S. (2020). Application of New TOPSIS Approach to Identify the Most Significant Risk Factor and Continuous Monitoring of Death of COVID-19. Electron J Gen Med.; 17(6):em234), https://doi.org/10.29333/ejgm/7904
• Neogi, D. (2020). Performance Appraisal of Select Nations in Mitigation of COVID-19 Pandemic using Entropy-based TOPSIS Method. Cien Saude Colet. 2021 Apr; 26(4):1419-1428. doi: 10.1590/1413-81232021264.43132020. Epub Dec 18. PMID: 33886770.
• OECD. (2021, August 23). A number of medical doctors and nurses, https://www.oecd.org/coronavirus/en/data-insights/number-of-medical-doctors-and-nurses.
• Oomman, S., & Todd, E. (2021). Impact of COVID-19 lockdown on A&E performances in an NHS Foundation Trust. Postgraduate Medical Journal, J 97(1143), 48-50, https://doi.org/10.1136/postgradmedj-2020-138894.
• Opricovic, S., & Tzeng, G. (2004 ). Compromise Solution by MCDM Methods: A Comparative Analysis, of VIKOR and TOPSIS. Eur J Oper Res, 156:445–55.
• Özdemir, M. (2018). Çok Kriterli Karar Verme Yöntemleri Açıklamalı ve Karşılaştırmalı Sağlık Bilimleri Uygulamaları ile. Ankara: Atlas Akademik Basım Yayın Dağıtım
• Pavić, Zlatko, and Vedran Novoselac (2013). "Notes on TOPSIS method." International Journal of Research in Engineering and Science 1.2 5-12.
• Sałabun W, Wątróbski J, & Shekhovtsov, A. (2020). Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods. Symmetry.; 12(9):1549. https://doi.org/10.3390/sym12091549
• Salehi, V., Moradi, G., Omidi, L., & Rahimi, E. (2023). An MCDM approach to assessing influential factors on healthcare providers’ safe performance during the COVID-19 pandemic: Probing into demographic variables. Journal of Safety Science and Resilience. Sep;4(3):274–83. doi: 10.1016/j.jnlssr.2023.05.002. Epub 2023 Jun 8. PMCID: PMC10249363.
• Shirazi, H., Kia, R., & Ghasemi, P. (2020). Ranking of hospitals in the case of COVID-19 outbreak: a new integrated approach using patient satisfaction criteria. International Journal of Healthcare Management, 13(4), 312–324.
• Silva, M. D., Costa, H. G., & Gomes, C. F. (2020). Multi-criteria decision choices for investment in innovative upper-middle income countries. Innovation & Management Review, 17(3), 321-347.
• Spronk, J., Steuer, R. E., & Zopounidis, C. (2016). Multicriteria decision aid/analysis in finance. Multiple criteria decision analysis: State of the art surveys, 1011-1065.
• Tae, P., Woo , L., & Yang, Z. (2018). Multi-Criteria Decision Making in Maritime Studies and Logistics Applications and Cases. USA,: International Series in Operations Research & Management Science,
• WHO. (2022, December). Global spending on health: rising to the pandemic’s challenges, https://www.who.int/publications/i/item/9789240064911.
• Vinodhini, G.. F. (2020). Country ranking of COVID-19 using MCDM methods. Journal of Critical Reviews, 7(5), 1333-1338.
• Xiaozhen, Z., Mao, J., & Yanan, L. (2020). A new computational method based on probabilistic linguistic Z-number with unbalanced semantics and its application to multi-criteria group decision-making. IEEE Access, 9, 2950–2965.
• Xu, M., Bai, C., Shi, L., Puška, A., Štilić, A., & Stević, Ž. (2023). Assessment of Mountain Tourism Sustainability Using Integrated Fuzzy MCDM Model. Sustainability, 15(19), 14358.
• Wikipedia, https://en.wikipedia.org/wikipedia
• Worldometers. (2023, April 24). https://www.worldometers.info/coronavirus/.
• Worldometer, C. U. (2023). Cases and deaths from COVID-19 virus pandemic.
• Zavadskas, E. K., Sušinskas, S., Daniūnas, A., Turskis, Z., & Sivilevičius, H. (2012). Multiple criteria selection of pile-column construction technology. Journal of Civil Engineering and Management, 18(6), 834-842. https://doi.org/10.3846/13923730.2012.744537.
• Zhu, Y., Tian, D., & Yan, F. (2020). Effectiveness of entropy weight method in decision-making. Mathematical Problems in Engineering, 2020, 1-5.