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G20 Ülkelerinin Covid-19 Pandemisi ile Mücadele Performanslarının Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi

Yıl 2022, Sayı: 37, 27 - 52, 29.12.2022
https://doi.org/10.26650/ekoist.2022.37.1161945

Öz

Covid-19 pandemisi ilk günden günümüze dünyayı etkisi altına almış ve ülkeleri birçok farklı alanda etkilemiştir. Ülkelerin pandemi ile mücadele performanslarının belirleyicileri arasında mevcut sağlık sistemlerinin gücü, ekonomik yapıları, demografik yapıları, uygulanan önlemler ve yapılan destekler gibi kriterler sayılabilir. Bu süreçte ülkelerin dâhil olduğu uluslararası organizasyonların aldığı ortak kararlar da pandemi ile mücadele aşamasında ülkeleri desteklemektedir. Çalışmanın temel amacı söz konusu uluslararası organizasyonlardan G20 topluluğundaki ülkelerin pandemi ile mücadele performanslarının çok kriterli karar verme yöntemleri (ÇKKV) aracılığıyla değerlendirilmesidir. Çalışmada öncelikle kriterler için CRITIC yöntemi ile ağırlıklandırma işlemi gerçekleştirilmiştir. En önemli kriterler sırasıyla vaka sayısı, ölüm sayısı, likidite destekleri ve sağlık sektörüne yapılan ek harcamalar olarak saptanmıştır. Sonrasında ÇKKV yöntemlerinden TOPSIS, COPRAS, ARAS, WASPAS, MOORA, MABAC yöntemleri ile analiz gerçekleştirilerek ülkelere ilişkin sıralamalar elde edilmiştir. Nihai olarak ortak bir sıralama için COPELAND yöntemi kullanılmıştır. Sonuç olarak en başarılı ülkeler sırasıyla Avusturalya, Japonya ve Çin olarak belirlenirken son sıraları Brezilya, Meksika ve Güney Afrika paylaşmaktadır.

Kaynakça

  • Aksoy, E., Ömürbek, N. ve Karaatlı, M. (2015). AHP temelli MULTİMOORA ve COPRAS yöntemi ile Türkiye Kömür İşletmeleri’nin performans değerlendirmesi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33(4): 1-28.
  • Arslan, R. ve Bircan, H. (2020). Çok kriterli karar verme teknikleriyle elde edilen sonuçların Copeland yöntemiyle birleştirilmesi ve karşılaştırılması. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 27(1): 109-127.
  • Arsu, T. (2021). Ülkelerin Covid-19 pandemisine karşı mücadelesinin çok kriterli karar verme yöntemleri ile değerlendirilmesi. Bitlis Eren Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Akademik İzdüşüm Dergisi, 6(1): 128-140.
  • Aydın, K. ve Sönmüş, A. (2021). Türkiye’de ve G-20 ülkelerinde Covid-19 sürecinde devletlerin politikaları. Al Farabi Uluslararası Sosyal Bilimler Dergisi, 6(1): 90-95.
  • Barua, B., & Barua, S. (2021). COVID-19 implications for banks: evidence from an emerging economy. SN Business & Economics, 1(1): 1-28.
  • Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.
  • Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1), 65-98. https://doi.org/10.1137/141000671
  • Berensmann, K., Shimeles, A., & Ndung’u, N. (2020). Covid-19 crisis: How should the G20 support heavily indebted low-income countries?. T20 Policy Briefing Paper of Task Force, 8.
  • Boyacı, A. Ç. (2021). Which OECD countries are advantageous in fight against Covid-19?. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 37(1): 137-148.
  • Brauers, W. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition Economy. Control and Cybernetics, 35(2): 445-469.
  • Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. The International Journal of Advanced Manufacturing Technology, 54: 1155-1166.
  • Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1): 1-20.
  • Chakraborty, I., & Maity, P. (2020). Covid-19 outbreak: Migration, effects on society, global environment and prevention. The Science of the total environment, 728, 138882. https://doi. org/10.1016/j.scitotenv.2020.138882
  • Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data metods. Materials and Design, 32(2): 851-860.
  • Çakır, E. (2017). Kriter ağırlıklarının SWARA – Copeland yöntemi ile belirlenmesi: Bir üretim işletmesinde uygulama. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4(1): 42-56.
  • Copeland, A. (1951). A reasonable social welfare function. Seminar on Applications of Mathematics to Social Sciences, University of Michigan, Ann Arbor.
  • Dadelo, S., Turskis, Z., Zavadskas, E., & Dadeliene, R. (2012). Multiple criteria assessment of elite security personel on the basis of ARAS and expert methods. Economic Computation and Economic Cybernetics Studies and Research, 46(4): 65-88.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The Critic method. Computers & Operations Research, 22(7): 763- 770.
  • Dünya Sağlık Örgütü (DSÖ). (2022). Global Health Workforce Statistics. [Available online at: https://www.who.int/data/gho/data/themes/topics/health-workforce ], Retrieved on February 11, 2022.
  • Dünya Sağlık Örgütü (DSÖ). (2022). COVID-19 Weekly Epidemiological Update. pp. 1-14. [Available online at: https://www.who.int/publications/m/item/weekly-epidemiological-update- on-covid-19 1-march-2022 ], Retrieved on March 8, 2022
  • Fıshburn, P.C. (1977). Condorcet social choice functions. SIAM Journal of Applied Mathematics, 33(3): 469–489.
  • Gigović, L., Božanić, D., & Ljubojević, S. (2017). Application of the GIS-DANP-MABAC multi- criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia. Renewable Energy, 103: 501-521.
  • G20. (2022). About the G20. [Available online at: https://g20.org/about-the-g20/ ], Retrieved on March 25, 2022.
  • Hezer, S., Gelmez, E. ve Özceylan, E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment. Journal of Infection and Public Health, 14(6): 775-786.
  • Hwang, C.L., & Yoon, K. (1981). Methods for multiple attribute decision making. In: Multiple attribute decision making. Lecture Notes in Economics and Mathematical Systems, vol 186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48318-9_3
  • IMF. (2021). Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic. [Available online at: https://www.imf.org/en/Topics/imf-and-covid19/Fiscal- Policies-Database-in-Response-to-COVID-19], Retrieved on February 11, 2022.
  • Inoue, H. (2020). Japanese strategy to Covid-19: How does it work?. Global Health & Medicine, 2(2): 131-132.
  • İşlek, E., Özatkan, Y., Uslu, M. K. B., Arı, H. O., Çelik, H. ve Yıldırım, H. H. (2021). Türkiye’de COVID-19 pandemisi yönetimi ve sağlık politikası stratejileri. Türkiye Sağlık Enstitüleri Başkanlığı Dergisi, 4(2): 54-65.
  • Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Bahraminasab, M. (2012). A Framework for Weighting of Criteria in Ranking Stage of Material Selection Process. Int. J. Adv. Manufacturing Technology, 58: 411-420.
  • Karabašević, D., Stanujkić, D., Urošević, S., & Maksimović, M. (2016). An approach to personel selection based on SWARA and WASPAS methods. Journal of Economics, Management and Informatics, 7(1): 1-11.
  • Klamler, C. (2005). The Copeland rule and Condorcet’s principle. Economic Theory, 25(3): 745– 749.
  • Macar, O. D. ve Asal, U. Y. (2020). Covid-19 ile uluslararası ilişkileri yeniden düşünmek: Tarih, ekonomi ve siyaset ekseninde bir değerlendirme. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Bahar (Covid19-Özel Ek), 222-239.
  • McKibbin, W., & Vines, D. (2020). Global macroeconomic cooperation in response to the COVID-19 pandemic: a roadmap for the G20 and the IMF. Oxford Review of Economic Policy, 36(S1), S297–S337. graa032. https://doi.org/10.1093/oxrep/graa032.
  • Neogi, D. (2021). Performance appraisal of select nations in mitigation of COVID-19 pandemic using entropy based TOPSIS method. Ciência & saude coletiva, 26: 1419-1428.
  • OECD. (2019). Health at a glance 2019: OECD indicators, OECD Publishing, Paris. [Available online at: https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance- 2019_4dd50c09-en a], Retrieved on March 2, 2022.
  • OECD. (2020). Health at a glance: Europe 2020: State of health in the EU cycle, OECD Publishing, Paris. [Available online at: https://www.oecd-ilibrary.org/social-issues-migration-health/health- at-a-glance-europe-2020_82129230-en ], Retrieved on March 20, 2022.
  • Our World in Data. (2022). Coronavirus pandemic (COVID-19). [Available online at: https:// ourworldindata.org/covid-deaths], Retrieved on February 11, 2022.
  • Özyurda, F. (2021). COVID-19 pandemisinde Avustralya sağlık sistemi. Toplum ve Hekim, 36(6): 412-423.
  • Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6): 3016-3028.
  • Saari, D.G., & Merlin, V.R. (1996). The Copeland method. Economic Theory, 8(1): 51-76.
  • Satman, M. H., Yıldırım, B. F., & Kuruca, E. (2021). JMcDM: A Julia package for multiple-criteria decision-making tools. Journal of Open Source Software, 6(65): 3430.
  • Sauer, M. (2022). Bill Gates: ‘If every country does what Australia did,’ the world could prevent the next pandemic. CNBC. [Available online at: https://www.cnbc.com/2022/02/24/bill-gates- australia-covid-blueprint-could-help-prevent-next-pandemic.html], Retrieved on April 2, 2022.
  • Sel, A. (2021). Covid 19 pandemisinde sağlık sistemi gelişmelerinin etkinliğinin ölçülmesi: G-20 üzerine bir inceleme. Kırklareli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. Cilt 10, Sayı 2, s. 181-202.
  • Seyhan, N. ve Seyhan, B. (2021). COVID-19 salgın sürecinde AB ülkelerindeki yaşam kalitesinin çok kriterli karar verme ile değerlendirilmesi. Journal of Social Research and Behavioral Sciences, 7(13), s. 158-180.
  • Selamzade, F. ve Özdemir, Y. (2020). COVID-19`a karşı OECD ülkelerinin etkinliğinin VZA ile değerlendirilmesi. Turkish Studies, 15(4), 977-991. https://dx.doi.org/10.7827/ TurkishStudies.43718
  • Sliogeriene, J., Turskis, Z., & Streimikiene, D. (2013). Analysis and choice of energy generation technologies: The multiple criteria assessment on the case study of Lithuania. Energy Procedia, 32: 11-20.
  • Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A comparative Study. Boston: Springer.
  • Turan, G. (2018). Çok Kriterli Karar Verme, (Ed.: Yıldırım, F.B. ve Önder E.) İşletmeciler, Mühendisler ve Yöneticiler için Operasyonel, Yönetsel ve Stratejik Problemlerin Çözümünde Çok Kriterli Karar Verme Yöntemleri (3. Baskı), (s.15-20). Bursa, Dora Yayıncılık.
  • Turskis, Z., & Zavadskas, E. K. (2010). A new additive ratio assesment (ARAS) method in multi- criteria decision-making. Technological and Economic Development of Economy, (2): 159-172.
  • Vinodhini, G. A. F. (2020). Country ranking of COVID 19 using MCDM methods. Journal of Critical Reviews, 7(5): 1333-1338.
  • Worldometers. (2022). Covid-19 coronavirus pandemic. [Available online at: https://www. worldometers.info/coronavirus/ ], Retrieved on February 11, 2022.
  • Yiğit, A. (2020). The performance of OECD countries in combating with Covid 19 pandemics: A cross-sectional study. Journal of Current Researches on Social Sciences, 10(2): 399-416.
  • Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2014). Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. Springer.
  • Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method. Archives of Civil and Mechanical Engineering, 10(3): 123-141.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika Ir Elektrotechnika, 122(6): 3-6.

Performance Evaluation of G20 Countries’ Fight Against COVID-19 Using Multiple Criteria Decision-Making Methods

Yıl 2022, Sayı: 37, 27 - 52, 29.12.2022
https://doi.org/10.26650/ekoist.2022.37.1161945

Öz

The COVID-19 pandemic has affected countries around the whole world in many different areas. The main determinants of a country’s performance against the pandemic can be summarized through criteria such as the strength of its current health system, economic structures, demographic structures, restrictions, and support. Countries’ strategies also involve the consensus that has been reached by international organizations. The main purpose of this study is to evaluate the performance of G20 countries using multiple criteria decision-making (MCDM) methods. The criteria were first weighted using the CRITIC method, with the number of cases, number of deaths, liquidity supports, and additional expenditures in the health sector having been determined as the most important criteria. The data were then analyzed using MCDM methods to obtain countries’ rankings. As a result, the most successful countries were respectively determined as Australia, Japan, and China, while Brazil, Mexico, and South Africa came in the respective last three places.

Kaynakça

  • Aksoy, E., Ömürbek, N. ve Karaatlı, M. (2015). AHP temelli MULTİMOORA ve COPRAS yöntemi ile Türkiye Kömür İşletmeleri’nin performans değerlendirmesi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33(4): 1-28.
  • Arslan, R. ve Bircan, H. (2020). Çok kriterli karar verme teknikleriyle elde edilen sonuçların Copeland yöntemiyle birleştirilmesi ve karşılaştırılması. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 27(1): 109-127.
  • Arsu, T. (2021). Ülkelerin Covid-19 pandemisine karşı mücadelesinin çok kriterli karar verme yöntemleri ile değerlendirilmesi. Bitlis Eren Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Akademik İzdüşüm Dergisi, 6(1): 128-140.
  • Aydın, K. ve Sönmüş, A. (2021). Türkiye’de ve G-20 ülkelerinde Covid-19 sürecinde devletlerin politikaları. Al Farabi Uluslararası Sosyal Bilimler Dergisi, 6(1): 90-95.
  • Barua, B., & Barua, S. (2021). COVID-19 implications for banks: evidence from an emerging economy. SN Business & Economics, 1(1): 1-28.
  • Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.
  • Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1), 65-98. https://doi.org/10.1137/141000671
  • Berensmann, K., Shimeles, A., & Ndung’u, N. (2020). Covid-19 crisis: How should the G20 support heavily indebted low-income countries?. T20 Policy Briefing Paper of Task Force, 8.
  • Boyacı, A. Ç. (2021). Which OECD countries are advantageous in fight against Covid-19?. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 37(1): 137-148.
  • Brauers, W. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition Economy. Control and Cybernetics, 35(2): 445-469.
  • Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. The International Journal of Advanced Manufacturing Technology, 54: 1155-1166.
  • Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1): 1-20.
  • Chakraborty, I., & Maity, P. (2020). Covid-19 outbreak: Migration, effects on society, global environment and prevention. The Science of the total environment, 728, 138882. https://doi. org/10.1016/j.scitotenv.2020.138882
  • Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data metods. Materials and Design, 32(2): 851-860.
  • Çakır, E. (2017). Kriter ağırlıklarının SWARA – Copeland yöntemi ile belirlenmesi: Bir üretim işletmesinde uygulama. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4(1): 42-56.
  • Copeland, A. (1951). A reasonable social welfare function. Seminar on Applications of Mathematics to Social Sciences, University of Michigan, Ann Arbor.
  • Dadelo, S., Turskis, Z., Zavadskas, E., & Dadeliene, R. (2012). Multiple criteria assessment of elite security personel on the basis of ARAS and expert methods. Economic Computation and Economic Cybernetics Studies and Research, 46(4): 65-88.
  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The Critic method. Computers & Operations Research, 22(7): 763- 770.
  • Dünya Sağlık Örgütü (DSÖ). (2022). Global Health Workforce Statistics. [Available online at: https://www.who.int/data/gho/data/themes/topics/health-workforce ], Retrieved on February 11, 2022.
  • Dünya Sağlık Örgütü (DSÖ). (2022). COVID-19 Weekly Epidemiological Update. pp. 1-14. [Available online at: https://www.who.int/publications/m/item/weekly-epidemiological-update- on-covid-19 1-march-2022 ], Retrieved on March 8, 2022
  • Fıshburn, P.C. (1977). Condorcet social choice functions. SIAM Journal of Applied Mathematics, 33(3): 469–489.
  • Gigović, L., Božanić, D., & Ljubojević, S. (2017). Application of the GIS-DANP-MABAC multi- criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia. Renewable Energy, 103: 501-521.
  • G20. (2022). About the G20. [Available online at: https://g20.org/about-the-g20/ ], Retrieved on March 25, 2022.
  • Hezer, S., Gelmez, E. ve Özceylan, E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment. Journal of Infection and Public Health, 14(6): 775-786.
  • Hwang, C.L., & Yoon, K. (1981). Methods for multiple attribute decision making. In: Multiple attribute decision making. Lecture Notes in Economics and Mathematical Systems, vol 186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48318-9_3
  • IMF. (2021). Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic. [Available online at: https://www.imf.org/en/Topics/imf-and-covid19/Fiscal- Policies-Database-in-Response-to-COVID-19], Retrieved on February 11, 2022.
  • Inoue, H. (2020). Japanese strategy to Covid-19: How does it work?. Global Health & Medicine, 2(2): 131-132.
  • İşlek, E., Özatkan, Y., Uslu, M. K. B., Arı, H. O., Çelik, H. ve Yıldırım, H. H. (2021). Türkiye’de COVID-19 pandemisi yönetimi ve sağlık politikası stratejileri. Türkiye Sağlık Enstitüleri Başkanlığı Dergisi, 4(2): 54-65.
  • Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Bahraminasab, M. (2012). A Framework for Weighting of Criteria in Ranking Stage of Material Selection Process. Int. J. Adv. Manufacturing Technology, 58: 411-420.
  • Karabašević, D., Stanujkić, D., Urošević, S., & Maksimović, M. (2016). An approach to personel selection based on SWARA and WASPAS methods. Journal of Economics, Management and Informatics, 7(1): 1-11.
  • Klamler, C. (2005). The Copeland rule and Condorcet’s principle. Economic Theory, 25(3): 745– 749.
  • Macar, O. D. ve Asal, U. Y. (2020). Covid-19 ile uluslararası ilişkileri yeniden düşünmek: Tarih, ekonomi ve siyaset ekseninde bir değerlendirme. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Bahar (Covid19-Özel Ek), 222-239.
  • McKibbin, W., & Vines, D. (2020). Global macroeconomic cooperation in response to the COVID-19 pandemic: a roadmap for the G20 and the IMF. Oxford Review of Economic Policy, 36(S1), S297–S337. graa032. https://doi.org/10.1093/oxrep/graa032.
  • Neogi, D. (2021). Performance appraisal of select nations in mitigation of COVID-19 pandemic using entropy based TOPSIS method. Ciência & saude coletiva, 26: 1419-1428.
  • OECD. (2019). Health at a glance 2019: OECD indicators, OECD Publishing, Paris. [Available online at: https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance- 2019_4dd50c09-en a], Retrieved on March 2, 2022.
  • OECD. (2020). Health at a glance: Europe 2020: State of health in the EU cycle, OECD Publishing, Paris. [Available online at: https://www.oecd-ilibrary.org/social-issues-migration-health/health- at-a-glance-europe-2020_82129230-en ], Retrieved on March 20, 2022.
  • Our World in Data. (2022). Coronavirus pandemic (COVID-19). [Available online at: https:// ourworldindata.org/covid-deaths], Retrieved on February 11, 2022.
  • Özyurda, F. (2021). COVID-19 pandemisinde Avustralya sağlık sistemi. Toplum ve Hekim, 36(6): 412-423.
  • Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6): 3016-3028.
  • Saari, D.G., & Merlin, V.R. (1996). The Copeland method. Economic Theory, 8(1): 51-76.
  • Satman, M. H., Yıldırım, B. F., & Kuruca, E. (2021). JMcDM: A Julia package for multiple-criteria decision-making tools. Journal of Open Source Software, 6(65): 3430.
  • Sauer, M. (2022). Bill Gates: ‘If every country does what Australia did,’ the world could prevent the next pandemic. CNBC. [Available online at: https://www.cnbc.com/2022/02/24/bill-gates- australia-covid-blueprint-could-help-prevent-next-pandemic.html], Retrieved on April 2, 2022.
  • Sel, A. (2021). Covid 19 pandemisinde sağlık sistemi gelişmelerinin etkinliğinin ölçülmesi: G-20 üzerine bir inceleme. Kırklareli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. Cilt 10, Sayı 2, s. 181-202.
  • Seyhan, N. ve Seyhan, B. (2021). COVID-19 salgın sürecinde AB ülkelerindeki yaşam kalitesinin çok kriterli karar verme ile değerlendirilmesi. Journal of Social Research and Behavioral Sciences, 7(13), s. 158-180.
  • Selamzade, F. ve Özdemir, Y. (2020). COVID-19`a karşı OECD ülkelerinin etkinliğinin VZA ile değerlendirilmesi. Turkish Studies, 15(4), 977-991. https://dx.doi.org/10.7827/ TurkishStudies.43718
  • Sliogeriene, J., Turskis, Z., & Streimikiene, D. (2013). Analysis and choice of energy generation technologies: The multiple criteria assessment on the case study of Lithuania. Energy Procedia, 32: 11-20.
  • Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A comparative Study. Boston: Springer.
  • Turan, G. (2018). Çok Kriterli Karar Verme, (Ed.: Yıldırım, F.B. ve Önder E.) İşletmeciler, Mühendisler ve Yöneticiler için Operasyonel, Yönetsel ve Stratejik Problemlerin Çözümünde Çok Kriterli Karar Verme Yöntemleri (3. Baskı), (s.15-20). Bursa, Dora Yayıncılık.
  • Turskis, Z., & Zavadskas, E. K. (2010). A new additive ratio assesment (ARAS) method in multi- criteria decision-making. Technological and Economic Development of Economy, (2): 159-172.
  • Vinodhini, G. A. F. (2020). Country ranking of COVID 19 using MCDM methods. Journal of Critical Reviews, 7(5): 1333-1338.
  • Worldometers. (2022). Covid-19 coronavirus pandemic. [Available online at: https://www. worldometers.info/coronavirus/ ], Retrieved on February 11, 2022.
  • Yiğit, A. (2020). The performance of OECD countries in combating with Covid 19 pandemics: A cross-sectional study. Journal of Current Researches on Social Sciences, 10(2): 399-416.
  • Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2014). Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. Springer.
  • Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method. Archives of Civil and Mechanical Engineering, 10(3): 123-141.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika Ir Elektrotechnika, 122(6): 3-6.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Seda Karakaş Geyik 0000-0003-2218-6689

Mehmet Hakan Satman 0000-0002-9402-1982

Gülin Kalyoncu 0000-0002-5460-6468

Yayımlanma Tarihi 29 Aralık 2022
Gönderilme Tarihi 15 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 37

Kaynak Göster

APA 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