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Ülkelerin Covid-19 Pandemisine Karşı Mücadelesinin Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi

Yıl 2021, Cilt: 6 Sayı: 1, 128 - 140, 31.03.2021

Talip Arsu

Öz

2019 yılının sonlarında ortaya çıkıp kısa sürede dünyanın hemen hemen tamamını etkisi altına alan Covid-19 pandemisi, ülkeleri hem ekonomik hem de sosyal açıdan zor bir duruma sokmuştur. Ülkelerin pandemi ile mücadelede en önemli silahları mevcut sağlık altyapıları ve nüfusunun özellikleri olarak ön plana çıkmıştır. Bu noktadan hareketle bu çalışmada ülkelerin sağlık altyapıları, nüfus özellikleri ve Covid-19 verileri kullanılarak ülkelerin değerlendirilmesi amaçlanmıştır. Bu amaca uygun değerlendirmeyi yapmak için objektif çok kriterli karar verme yöntemlerinden Entropi ve WASPAS yöntemleri kullanılmıştır. Kriterleri ağırlıklandırmak amacıyla kullanılan Entropi yöntemi sonuçlarına göre en önemli kriter ‘GSYİH’nın yüzdesi olarak sağlık harcamaları’ olmuştur. Entropi ağırlıkları kullanılarak uygulanan WASPAS yöntemi sonuçlarına göre Covid-19 pandemisi ile mücadelede en başarılı ülkeler Rusya, Almanya, Kanada, ABD, Avusturya ve İsviçre olarak bulunmuştur.

Anahtar Kelimeler

Covid-19 Ülkeler Pandemi ile Mücadele Entropi WASPAS

Kaynakça

  • Abbaspour, A., Saremi, M., Alibabaei, A., & Moghanlu, P. S. (2020). Determining the optimal human reliability analysis (HRA) method in healthcare systems using Fuzzy ANP and Fuzzy TOPSIS. Journal of Patient Safety and Risk Management, 25(3), 123-133.
  • Akçakanat, Ö., Eren, H., Aksoy, E., & Ömürbek, V. (2017). Bankacılık sektöründe Entropi ve WASPAS yöntemleri ile performans değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(2), 285-300.
  • Araujo, C. A. S., Wanke, P., & Siqueira, M. M. (2018). A performance analysis of Brazilian public health: TOPSIS and neural networks application. International Journal of Productivity and Performance Management. 67(9), 1526-1549.
  • Asandului, L., Roman, M., & Fatulescu, P. (2014). The efficiency of healthcare systems in Europe: A data envelopment analysis approach. Procedia Economics and Finance, 10, 261-268.
  • Ayçin, E. (2020), Çok kriterli karar verme: Bilgisayar uygulamalı çözümler. Ankara: Nobel Yayınları.
  • Ayçin, E., & Güçlü, P. (2020). BIST Ticaret Endeksinde Yer Alan İşletmelerin Finansal Performanslarının Entropi ve MAIRCA Yöntemleri ile Değerlendirilmesi. Muhasebe ve Finansman Dergisi, (85), 287-312.
  • Bhadra, A., Mukherjee, A., & Sarkar, K. (2020). Impact of population density on Covid-19 infected and mortality rate in India. Modeling Earth Systems and Environment, 7, 623-629.
  • Breitenbach, M. C., Ngobeni, V., & Aye, G. (2020). Efficiency of Healthcare Systems in the first wave of COVID-19-a technical efficiency analysis. Munich Personal RePEc Archive (MPRA) Paper No. 101440, https://mpra.ub.uni-muenchen.de/101440/
  • Carozzi, F. (2020). Urban density and COVID-19. IZA Discussion Papers, No. 13440, Institute of Labor Economics (IZA), Bonn
  • Chen, Z., Fan, H., Cai, J., Li, Y., Wu, B., Hou, Y., ... & Sun, J. (2020). High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. European journal of radiology, 126, 108972.
  • Coşkun, H., Yıldırım, N., & Gündüz, S. (2021). The spread of COVID-19 virus through population density and wind in Turkey cities. Science of the Total Environment, 751, 141663.
  • Dacosta-Claro, I., & Lapierre, S. D. (2003). Benchmarking as a tool for the improvement of health services' supply departments. Health Services Management Research, 16(4), 211-223.
  • Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., ... & Mills, M. C. (2020). Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences, 117(18), 9696-9698.
  • Dünya Bankası (2021). World Bank Open Data. 03.02.2021 tarihinde https://data.worldbank.org/ adresinden alındı
  • DSÖ. (2021). COVID-19 Weekly Epidemiological Update. 04.02.2021 tarihinde https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ adresinden alındı
  • Erol, I., & Ferrell Jr, W. G. (2009). Integrated Approach for Reorganizing Purchasing: Theory and A Case Analysis on A Turkish Company. Computers & Industrial Engineering, 56(4), 1192-1204.
  • Eş, A. & Kök, E. (2020) Banka performanslarının Entropi tabanlı WASPAS yöntemiyle analizi. Düzce Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(2), 233-250.
  • Gezen, A. (2019). Türkiye’de faaliyet gösteren katılım bankalarının Entropi ve WASPAS yöntemleri ile performans analizi. Muhasebe ve Finansman Dergisi, (84), 213-232.
  • Ghasemi, A., Boroumand, Y., & Shirazi, M. (2020). How do governments perform in facing COVID-19?. Munich Personal RePEc Archive (MPRA) Paper No. 99844, https://mpra.ub.uni-muenchen.de/99844/
  • Godaert, L., Proye, E., Demoustier-Tampere, D., Coulibaly, P. S., Hequet, F., & Dramé, M. (2020). Clinical characteristics of older patients: the experience of a geriatric short-stay unit dedicated to patients with COVID-19 in France. Journal of Infection, 81(1), e93-e94.
  • Karaca, C., & Ulutaş, A. (2018). Entropi ve WASPAS Yöntemleri Kullanılarak Türkiye İçin Uygun Yenilenebilir Enerji Kaynağının Seçimi. Ege Akademik Bakış Dergisi, 18(3), 483-494.
  • Karaca, S. S., Altemur, N., & Çevik, M. (2020). Bankacılık sektöründe performans analizi: Entropi ve WASPAS yöntemi uygulaması. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, 1(2), 46-76.
  • Kayapinar Kaya, S. (2020). Evaluation of the Effect of COVID-19 on Countries’ Sustainable Development Level: A comparative MCDM framework. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 101-122.
  • Lescauskiene, I., Bausys, R., Zavadskas, E. K., & Juodagalviene, B. (2020). VASMA weighting: survey-based criteria weighting methodology that combines ENTROPY and WASPAS-SVNS to reflect the psychometric features of the VAS scales. Symmetry, 12(10), 1641-1661.
  • Liu, K., Chen, Y., Lin, R., & Han, K. (2020). Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. Journal of Infection, 80(6), e14-e18.
  • McKibbin, W., & Fernando, R. (2020). The economic impact of COVID-19. Economics in the Time of COVID-19. (Eds. R.Baldwin ve B. Weder di Mauro), ss. 45-51, London: CEPR Press.
  • Niu, S., Tian, S., Lou, J., Kang, X., Zhang, L., Lian, H., & Zhang, J. (2020). Clinical characteristics of older patients infected with COVID-19: A descriptive study. Archives of gerontology and geriatrics, 89, 104058.
  • Orçun, Ç. (2019). Enerji Sektöründe WASPAS Yöntemiyle Performans Analizi. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(2), 439-453.
  • Ozcan, Y. A. (2008). Health care benchmarking and performance evaluation. London: Springer US.
  • Özdağoğlu, A., Yakut, E., & Bahar, S. (2017). Machine Selection in A Dairy Product Company with Entropy and SAW Methods Integration. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 32(1), 341-359.
  • Shirouyehzad, H., Jouzdani, J., & Khodadadi Karimvand, M. (2020). Fight against COVID-19: a global efficiency evaluation based on contagion control and medical treatment. Journal of Applied Research on Industrial Engineering, 7(2), 109-120.
  • Shrestha, N., Shad, M. Y., Ulvi, O., Khan, M. H., Karamehic-Muratovic, A., Nguyen, U. S. D., ... & Haque, U. (2020). The impact of COVID-19 on globalization. One Health, 11, 1-9.
  • Shukla, S., Jain, R., & Mishra, P. K. (2016). An Integrated Approach for Identification of Critical Factors for ERP Implementation using Entropy and WASPAS Method. MIT International Journal of Mechanical Engineering, 6(2), 80-89.
  • Stefko, R., Gavurova, B., & Kocisova, K. (2018). Healthcare efficiency assessment using DEA analysis in the Slovak Republic. Health economics review, 8(1), 1-12.
  • Sun, Z., Zhang, H., Yang, Y., Wan, H., & Wang, Y. (2020). Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Science of The Total Environment, 746, 141347.
  • Ural, M., Demireli, E., & Özçalık, S. G. (2018). Kamu bankalarında performans analizi: Entropi ve WASPAS yöntemleri ile bir uygulama. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (31), 129-141.
  • Wang, T. C., & Lee, H. D. (2009). Developing A Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights. Expert Systems with Applications, 36(5), 8980-8985.
  • Worldometers (2020). COVID-19 coronavirus pandemic. 03.02.2021 tarihinde https://www.worldometers.info/coronavirus/ adresinden alınmıştır.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6.

Evaluation of Countries' Struggle Against Covid-19 Pandemic with Multi Criteria Decision Making Methods

Yıl 2021, Cilt: 6 Sayı: 1, 128 - 140, 31.03.2021

Talip Arsu

Öz

The Covid-19 pandemic, which emerged in late 2019 and affected almost the entire ort in a short time, put countries in a difficult situation both economically and socially. The most important weapons of countries in fighting the pandemic have come to the fore as the existing health infrastructures and the characteristics of the population. From this point of view, in this study, it is aimed to evaluate the countries by using the health infrastructures of the countries, population characteristics and Covid-19 data. Entropy, which are among the objective multi-criteria decision making methods, and WASPAS methods have been used to make an appropriate assessment for this purpose. According to the results of the Entropy method used to weight the criteria, the most important criterion was ‘Current health expenditure (% of GDP)’. According to the results of the WASPAS method applied using entropy weights, the most successful countries in fighting the Covid-19 pandemic were found to be Russia, Germany, Canada, USA, Austria and Switzerland.

Anahtar Kelimeler

Covid-19 Countries Fighting the Pandemic Entropy WASPAS

Kaynakça

  • Abbaspour, A., Saremi, M., Alibabaei, A., & Moghanlu, P. S. (2020). Determining the optimal human reliability analysis (HRA) method in healthcare systems using Fuzzy ANP and Fuzzy TOPSIS. Journal of Patient Safety and Risk Management, 25(3), 123-133.
  • Akçakanat, Ö., Eren, H., Aksoy, E., & Ömürbek, V. (2017). Bankacılık sektöründe Entropi ve WASPAS yöntemleri ile performans değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(2), 285-300.
  • Araujo, C. A. S., Wanke, P., & Siqueira, M. M. (2018). A performance analysis of Brazilian public health: TOPSIS and neural networks application. International Journal of Productivity and Performance Management. 67(9), 1526-1549.
  • Asandului, L., Roman, M., & Fatulescu, P. (2014). The efficiency of healthcare systems in Europe: A data envelopment analysis approach. Procedia Economics and Finance, 10, 261-268.
  • Ayçin, E. (2020), Çok kriterli karar verme: Bilgisayar uygulamalı çözümler. Ankara: Nobel Yayınları.
  • Ayçin, E., & Güçlü, P. (2020). BIST Ticaret Endeksinde Yer Alan İşletmelerin Finansal Performanslarının Entropi ve MAIRCA Yöntemleri ile Değerlendirilmesi. Muhasebe ve Finansman Dergisi, (85), 287-312.
  • Bhadra, A., Mukherjee, A., & Sarkar, K. (2020). Impact of population density on Covid-19 infected and mortality rate in India. Modeling Earth Systems and Environment, 7, 623-629.
  • Breitenbach, M. C., Ngobeni, V., & Aye, G. (2020). Efficiency of Healthcare Systems in the first wave of COVID-19-a technical efficiency analysis. Munich Personal RePEc Archive (MPRA) Paper No. 101440, https://mpra.ub.uni-muenchen.de/101440/
  • Carozzi, F. (2020). Urban density and COVID-19. IZA Discussion Papers, No. 13440, Institute of Labor Economics (IZA), Bonn
  • Chen, Z., Fan, H., Cai, J., Li, Y., Wu, B., Hou, Y., ... & Sun, J. (2020). High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. European journal of radiology, 126, 108972.
  • Coşkun, H., Yıldırım, N., & Gündüz, S. (2021). The spread of COVID-19 virus through population density and wind in Turkey cities. Science of the Total Environment, 751, 141663.
  • Dacosta-Claro, I., & Lapierre, S. D. (2003). Benchmarking as a tool for the improvement of health services' supply departments. Health Services Management Research, 16(4), 211-223.
  • Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., ... & Mills, M. C. (2020). Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences, 117(18), 9696-9698.
  • Dünya Bankası (2021). World Bank Open Data. 03.02.2021 tarihinde https://data.worldbank.org/ adresinden alındı
  • DSÖ. (2021). COVID-19 Weekly Epidemiological Update. 04.02.2021 tarihinde https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ adresinden alındı
  • Erol, I., & Ferrell Jr, W. G. (2009). Integrated Approach for Reorganizing Purchasing: Theory and A Case Analysis on A Turkish Company. Computers & Industrial Engineering, 56(4), 1192-1204.
  • Eş, A. & Kök, E. (2020) Banka performanslarının Entropi tabanlı WASPAS yöntemiyle analizi. Düzce Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(2), 233-250.
  • Gezen, A. (2019). Türkiye’de faaliyet gösteren katılım bankalarının Entropi ve WASPAS yöntemleri ile performans analizi. Muhasebe ve Finansman Dergisi, (84), 213-232.
  • Ghasemi, A., Boroumand, Y., & Shirazi, M. (2020). How do governments perform in facing COVID-19?. Munich Personal RePEc Archive (MPRA) Paper No. 99844, https://mpra.ub.uni-muenchen.de/99844/
  • Godaert, L., Proye, E., Demoustier-Tampere, D., Coulibaly, P. S., Hequet, F., & Dramé, M. (2020). Clinical characteristics of older patients: the experience of a geriatric short-stay unit dedicated to patients with COVID-19 in France. Journal of Infection, 81(1), e93-e94.
  • Karaca, C., & Ulutaş, A. (2018). Entropi ve WASPAS Yöntemleri Kullanılarak Türkiye İçin Uygun Yenilenebilir Enerji Kaynağının Seçimi. Ege Akademik Bakış Dergisi, 18(3), 483-494.
  • Karaca, S. S., Altemur, N., & Çevik, M. (2020). Bankacılık sektöründe performans analizi: Entropi ve WASPAS yöntemi uygulaması. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, 1(2), 46-76.
  • Kayapinar Kaya, S. (2020). Evaluation of the Effect of COVID-19 on Countries’ Sustainable Development Level: A comparative MCDM framework. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 101-122.
  • Lescauskiene, I., Bausys, R., Zavadskas, E. K., & Juodagalviene, B. (2020). VASMA weighting: survey-based criteria weighting methodology that combines ENTROPY and WASPAS-SVNS to reflect the psychometric features of the VAS scales. Symmetry, 12(10), 1641-1661.
  • Liu, K., Chen, Y., Lin, R., & Han, K. (2020). Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. Journal of Infection, 80(6), e14-e18.
  • McKibbin, W., & Fernando, R. (2020). The economic impact of COVID-19. Economics in the Time of COVID-19. (Eds. R.Baldwin ve B. Weder di Mauro), ss. 45-51, London: CEPR Press.
  • Niu, S., Tian, S., Lou, J., Kang, X., Zhang, L., Lian, H., & Zhang, J. (2020). Clinical characteristics of older patients infected with COVID-19: A descriptive study. Archives of gerontology and geriatrics, 89, 104058.
  • Orçun, Ç. (2019). Enerji Sektöründe WASPAS Yöntemiyle Performans Analizi. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(2), 439-453.
  • Ozcan, Y. A. (2008). Health care benchmarking and performance evaluation. London: Springer US.
  • Özdağoğlu, A., Yakut, E., & Bahar, S. (2017). Machine Selection in A Dairy Product Company with Entropy and SAW Methods Integration. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 32(1), 341-359.
  • Shirouyehzad, H., Jouzdani, J., & Khodadadi Karimvand, M. (2020). Fight against COVID-19: a global efficiency evaluation based on contagion control and medical treatment. Journal of Applied Research on Industrial Engineering, 7(2), 109-120.
  • Shrestha, N., Shad, M. Y., Ulvi, O., Khan, M. H., Karamehic-Muratovic, A., Nguyen, U. S. D., ... & Haque, U. (2020). The impact of COVID-19 on globalization. One Health, 11, 1-9.
  • Shukla, S., Jain, R., & Mishra, P. K. (2016). An Integrated Approach for Identification of Critical Factors for ERP Implementation using Entropy and WASPAS Method. MIT International Journal of Mechanical Engineering, 6(2), 80-89.
  • Stefko, R., Gavurova, B., & Kocisova, K. (2018). Healthcare efficiency assessment using DEA analysis in the Slovak Republic. Health economics review, 8(1), 1-12.
  • Sun, Z., Zhang, H., Yang, Y., Wan, H., & Wang, Y. (2020). Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Science of The Total Environment, 746, 141347.
  • Ural, M., Demireli, E., & Özçalık, S. G. (2018). Kamu bankalarında performans analizi: Entropi ve WASPAS yöntemleri ile bir uygulama. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (31), 129-141.
  • Wang, T. C., & Lee, H. D. (2009). Developing A Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights. Expert Systems with Applications, 36(5), 8980-8985.
  • Worldometers (2020). COVID-19 coronavirus pandemic. 03.02.2021 tarihinde https://www.worldometers.info/coronavirus/ adresinden alınmıştır.
  • 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 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri
Yazarlar

Talip Arsu 0000-0002-2580-166X

Yayımlanma Tarihi 31 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 6 Sayı: 1

Kaynak Göster

APA Arsu, T. (2021). Ülkelerin Covid-19 Pandemisine Karşı Mücadelesinin Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi. Akademik İzdüşüm Dergisi, 6(1), 128-140.
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