Aşı Öncesi Dönemde Türkiye’de Uygulanan Kısıtlama ve Tedbirlerin Analizi: Yönetsel Performans Analizi

Abstract

COVİD-19 pandemisini kontrol altına almada ülkelerin sağlık altyapıları ve yönetsel becerileri belirleyici olmuştur. Bazı ülkeler pandemiye karşı daha sıkı tedbir ve kısıtlama politikaları tercih ederken bazı ülkeler daha serbest ve gevşek politika tercihlerinde bulunmuşlardır. Aynı zamanda söz konusu kısıtlama ve tedbirleri uygulayan hükümetin kararlılığı ve kısıtlamalara maruz kalan insanların kurallara olan bağlılığı pandemiyle olan mücadele sürecinin başarısında etkili olmuştur. Literatürdeki birçok çalışma pandeminin neden olduğu sosyo-ekonomik sorunlara odaklanmaktadır. Bu çalışmada ise ülkelerin pandemi sürecini yönetebilme başarısı ve kamusal tedbir ve kısıtlamaların yönetsel beceri üzerindeki etkisine odaklanmaktadır. İlk olarak seçilmiş 31 ülkenin sağlık altyapılarına bağlı olarak aşı öncesi dönemde pandemiyi kontrol altına alabilme konusundaki nisbi performansı karşılaştırılmıştır. İkinci aşamada ise Türkiye'de aşı öncesi dönemde uygulanan kısıtlama ve tedbirlerin Türkiye'nin COVİD-19 ile mücadele konusundaki nisbi performansına olan etkisi incelenmiştir. Aşı öncesi dönemi ifade eden 2020 Nisan ilk haftası ile 2021Haziran ikinci haftası tarihleri arasındaki 63 haftalık dönem İki Aşmalı Bootstrap Tahminli Veri Zarflama Analizi kullanılarak incelenmiştir. Analiz sonuçlarına göre, Türkiye’nin vaka-ölüm sayılarını minimize edebilme ve iyileşen hasta sayını maksimize edebilme performansına göre 31 ülke arasında 17. sırada yer aldığı (TE: 0.4081) belirlenmiştir. İkinci aşamada ise Türkiye'de pandemi sürecinde uygulanan kamusal tedbir ve kısıtlamaların ülkenin nisbi performan sıralaması üzerinde anlamlı bir değişime yol açmadığı sonucuna ulaşılmıştır.

Keywords

• COVİD-19
• Kamusal kısıtlamalar
• İki aşamalı veri zarflama analizi

Analysis of Restrictions and Measures Implemented in Turkey in the Pre-Vaccine Period: Managing Performance Analysis

Abstract

The health infrastructures and managerial skills of countries have been decisive in controlling the COVİD-19 pandemic. While some countries preferred stricter measures and restrictions against the pandemic, others preferred more liberal and lax policies. Besides determination of the government implementing these restrictions and measures and commitment of the people exposed to the restrictions the and rules have been effective in the success of the pandemic response. Many studies in the literature focus on the socio-economic problems caused by the pandemic. This study focuses on the success of countries in managing the pandemic and the impact of public measures and restrictions on managerial skills. First, we compare the relative performance of 31 selected countries in controlling the pandemic in the pre-vaccine period depending on their health infrastructure. In the second stage, the impact of the restrictions and measures implemented in Turkey in the pre-vaccine period on Turkey's relative performance in the fight against COVID-19 was analyzed. The 63-week period between the first week of April 2020 and the second week of June 2021, which refers to the pre-vaccine period, was analyzed using Data Envelopment Analysis with Two Stage Bootstrap Estimation. According to the results of the analysis, Turkey ranked 17th among 31 countries (TE: 0.4081) in terms of its performance in minimizing the number of case-fatalities and maximizing the number of recovered patients. In the second stage, it was concluded that the public measures and restrictions implemented in Turkey during the pandemic did not lead to a significant change in the relative performance ranking of the country.

Keywords

• COVİD-19
• Public constraints
• Two stage data envelopment analysis

References

1. Al Wahaibi, A., Al Maani, A., Alyaquobi, F., Al Manji, A., Al Harthy, K., Al Rawahi, B., Alqayoudh A., Khalili A. S. Jardani, A. S. ve Al-Abri, S. (2021). The impact of mobility restriction strategies in the control of the COVİD-19 pandemic: Modelling the relation between COVİD-19 health and community mobility data. International Journal of Environmental Research and Public Health, 18(19), 10560.
2. Amin, F., Poespito Hadi, W., Zauhar, S.,ve Santoso Haryono, B. (2022). Determinants of post COVİD-19 food security policy success. International Journal of Disaster Resilience in the Built Environment, 13(4), 440-450.
3. Antman, G., Tiosano, A., ve Bahar, I. (2021). The effect of COVİD-19 on Israeli ophthalmology departments: directors' perspectives. The Israel Medical Association Journal: IMAJ, 23(2), 76-81.
4. Arino, J., ve Portet, S. (2020). A simple model for COVİD-19. Infectious Disease Modelling, 5, 309-315.
5. Augeraud-Véron, E. (2020). Lifting the COVİD-19 lockdown: different scenarios for France. Mathematical Modelling of Natural Phenomena, 15, 40.
6. Badunenko, O., ve Tauchmann, H. (2019). Simar and Wilson two-stage efficiency analysis for Stata. The Stata Journal, 19(4), 950-988.
7. Baqal, O. J., ve Farouk, A. F. (2022). India’s frantic fight against COVİD-19: Rescuing a broken healthcare system by adopting a “aoctor and patient first” approach. Pakistan Journal of Medical Sciences, 38(4Part-II), 1064.
8. Barceló, J., Kubinec, R., Cheng, C., Rahn, T. H., ve Messerschmidt, L. (2022). Windows of repression: Using COVİD-19 policies against political dissidents?. Journal of Peace Research, 59(1), 73-89.

9. Breitenbach, M. C., Ngobeni, V., ve Aye, G. C. (2021). Global healthcare resource efficiency in the management of COVİD-19 death and infection prevalence rates. Frontiers in public health, 9, 638481.
10. Basalom, B., Ismail, A., Sindi, F., Mansouri, F., Kattan, A., Balkhy, E., Alamri, E., Alsurayhi, A., Alhnaidi, F., Morya, N., Alwagdani, R.ve Alrajhi, N. (2021). COVİD-19: The Pediatric Perspective. Journal of Pharmaceutical Research International, 33(57B) 89-97.
11. Bourdin, S., Ben Miled, S., ve Salhi, J. (2022). The drivers of policies to limit the spread of COVİD-19 in Europe. Journal of Risk and Financial Management, 15(2), 67.
12. Charnes, A., Cooper, W. W., ve Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
13. Chen, N., Chen, X., Zhong, Z., ve Pang, J. (2022). Exploring spillover effects for COVİD-19 cascade prediction. Entropy, 24(2), 222.
14. Chowdhury, H., ve Zelenyuk, V. (2016). Performance of hospital services in Ontario: DEA with truncated regression approach. Omega, 63, 111-122.
15. Chowdhury, D. (2020). The Effects of the COVİD-19 pandemic in the UK-at a local, national and international level perspective from the emergency department. Frontiers in Emergency Medicine, 4(2s), e59-e59.
16. Daraio, C., ve Simar, L. (2014). Directional distances and their robust versions: Computational and testing issues. European Journal of Operational Research, 237(1), 358-369.
17. Das, K., Patil, A., Goren, A., Cockerell, C. J., ve Goldust, M. (2022). Androgens and COVID‐19. Journal of cosmetic dermatology, 21(8), 3176-3180.
18. Diallo, A. I., Faye, A., Tine, J. A. D., Ba, M. F., Gaye, I., Bonnet, E., Traore, Z. ve Ridde, V. (2022). Factors associated with the acceptability of government measures to address COVİD-19 in Senegal. Revue D'epidemiologie et de Sante Publique, 70(3), 109-116.
19. Dlouhy, M. (2020). Health System Efficiency and the COVİD-19 Pandemic. In 38th International Conference on Mathematical Methods in Economics, 80-84.
20. Doğan, M. İ., Özsoy, V. S., ve Örkcü, H. H. (2021). Performance management of OECD countries on COVİD-19 pandemic: a criticism using data envelopment analysis models. Journal of Facilities Management, 19(4), 479-499.
21. Duguet, A. M., ve Rial-Sebbag, E. (2020). The fight against the COVID 19 epidemic in France: Health Organisation and legislative adaptation. Med. & L., 39, 173.
22. Dunford, D., Dale, B., Stylianou, N., Lowther, E., Ahmed, M., ve de la Torre Arenas, I. (2020). Coronavirus: The world in lockdown in maps and charts. BBC News, 9, 462.
23. Efron, B., ve Tibshirani, R. J. (1994). An introduction to the bootstrap. Chapman and Hall/CRC.
24. Halkos, G. E., ve Tzeremes, N. G. (2013). Carbon dioxide emissions and governance: a nonparametric analysis for the G-20. Energy Economics, 40, 110-118.
25. Hamzah, N. M., ve See, K. F. (2019). Technical efficiency and its influencing factors in Malaysian hospital pharmacy services. Health care management science, 22, 462-474.
26. Heshmati, A., Tsionas, M., ve Rashidghalam, M. (2023). An assessment of the Swedish health system’s efficiency during the COVİD-19 pandemic. International journal of healthcare management, 16(3), 336-352.
27. Jagadeesan, M., Ganeshkumar, P., Kaur, P., Sriramulu, H. M., Sakthivel, M., Rubeshkumar, Raju, M., Murugesan, L., Ganapathi, R., Srinivasan, M. Sukumar, A., Ilangovan, K., Reddy, M., Shanmugam, D. Govindasamy, P., ve Murhekar, M. (2022). Epidemiology of COVİD-19 and effect of public health interventions, Chennai, India, March–October 2020: an analysis of COVİD-19 surveillance system. BMJ open, 12(3), e052067.
28. Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., ve Kaur, M. (2021). Classification of the COVİD-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics, 39(15), 5682-5689.
29. Kadomatsu, N. (2022). Legal countermeasures against COVİD-19 in Japan: effectiveness and limits of non-coercive measures. China-EU Law Journal, 8(1), 11-32.
30. Klumpp, M., Loske, D., ve Bicciato, S. (2022). COVİD-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach. The European Journal of Health Economics, 23(8), 1263-1285.
31. Kithiia, J., Wanyonyi, I., Maina, J., Jefwa, T., ve Gamoyo, M. (2020). The socio-economic impacts of COVİD-19 restrictions: Data from the coastal city of Mombasa, Kenya. Data in brief, 33, 106317.
32. Krammer, S. M. (2022). Navigating the New Normal: Which firms have adapted better to the COVİD-19 disruption?. Technovation, 110, 102368.
33. Le, T. T., ve Nguyen, V. K. (2022). Effects of quick response to COVİD-19 with change in corporate governance principles on SMEs’ business continuity: evidence in Vietnam. Corporate Governance: The international journal of business in society, 22(5), 1112-1132.
34. Lee, Y. H., ve Lim, S. Y. R. (2022). Efficacy in COVİD-19 management: the case of ASEAN. Transforming Government: People, Process and Policy, 16(4), 613-626.
35. Lupu, D., ve Tiganasu, R. (2022). COVİD-19 and the efficiency of health systems in Europe. Health Economics Review, 12(1), 14.
36. Mahendradhata, Y., Andayani, N. L. P. E., Hasri, E. T., Arifi, M. D., Siahaan, R. G. M., Solikha, D. A., ve Ali, P. B. (2021). The capacity of the Indonesian healthcare system to respond to COVİD-19. Frontiers in Public Health, 9, 649819.
37. Mbunge, E. (2020). Integrating emerging technologies into COVİD-19 contact tracing: Opportunities, challenges and pitfalls. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(6), 1631-1636.
38. Morales de Labra, H. (2021). The contribution of companies to the COVİD-19 pandemic control. Revista Espanola de Salud Publica, 95, e202110170-e202110170.
39. Mourad, N., Habib, A., ve Tharwat, A. (2021). Appraising healthcare systems’ efficiency in facing COVİD-19 through data envelopment analysis. Decision Science Letters, 10(3), 301-310.
40. Obiedat, R., Harfoushi, O., Qaddoura, R., Al-Qaisi, L., ve Al-Zoubi, A. M. (2021). An evolutionary-based sentiment analysis approach for enhancing government decisions during COVİD-19 pandemic: The case of jordan. Applied Sciences, 11(19), 9080.
41. Pell, R., Suvarna, S. K., Cooper, N., Rutty, G., Green, A., Osborn, M., Johnson, P., Hayward, A., Durno, J., Estrin-Serlui, T., Mafham, M. ve Roberts, I. S. (2023). Coronial postmortem reports and indirect COVİD-19 pandemic-related mortality. Journal of Clinical Pathology, 76(7), 457-462.
42. Petrović, D., Petrović, M., Bojković, N., ve Čokić, V. P. (2020). An integrated view on society readiness and initial reaction to COVID–19: A study across European countries. Plos one, 15(11), e0242838.
43. Pinzaru, F., Zbuchea, A., ve Anghel, L. (2020). The Impact of the COVİD-19 Pandemic on Business. A preliminary overview. Strategica. Preparing for Tomorrow, Today, 721-730.
44. Rind, E., Kimpel, K., Preiser, C., Papenfuss, F., Wagner, A., Alsyte, K., Siegel, A., Klink, A., Steinhilber, B., Kauderer, J. ve Rieger, M. A. (2020). Adjusting working conditions and evaluating the risk of infection during the COVİD-19 pandemic in different workplace settings in Germany: A study protocol for an explorative modular mixed methods approach. BMJ open, 10(11), e043908.
45. Sagripanti, J. L. (2021). Seasonal effect of sunlight on COVİD-19 among countries with and without Lock-Downs. Open Journal of Epidemiology, 11(3), 303-325.
46. Simar, L., ve Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management science, 44(1), 49-61.
47. Simar, L., ve Wilson, P. W. (2002). Non-parametric tests of returns to scale. European Journal of Operational Research, 139(1), 115-132.
48. Simar, L. ve Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of econometrics, 136(1), 31-64.
49. Simar, L. & Wilson, P. W . (2008). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In The Measurement of Productive Efficiency and Productivity Change , edited by Fried, Harold O. , C. A. Knox Lovell , and Shelton S. Schmidt , 421–521. New York : Oxford University Press.
50. Simar, L. ve Wilson, P. W. (2011). Performance of the bootstrap for DEA estimators and iterating the principle. In Handbook on data envelopment analysis (pp. 241-271). Springer, Boston, MA.
51. Simar, L. ve Wilson, P. W. (2020). Hypothesis testing in nonparametric models of production using multiple sample splits. Journal of Productivity Analysis, 53(3), 287-303.
52. Song, W., Sawafta, F. J., Ebrahem, B. M. ve Jebril, M. A. (2020). Public attitude towards quarantine during the COVİD-19 outbreak. Epidemiology & Infection, 148, e220.
53. Stanić, I., Hinek, S., ve Lukić, K. (2022). Efficiency of management competencies of directors during the COVİD-19 pandemic. Ekonomski vjesnik: Review of Contemporary Entrepreneurship, Business, and Economic Issues, 35(1), 191-202..
54. Štěpánek, L., Habarta, F., Malá, I., ve Marek, L. (2021, November). Data envelopment analysis models connected in time series: A case study evaluating COVİD-19 pandemic management in some European countries. In 2021 international conference on e-health and bioengineering (EHB) (pp. 1-5). IEEE.
55. Sun, Y. J., Feng, Y. J., Chen, J., Li, B., Luo, Z. C., ve Wang, P. X. (2021). Clinical features of fatalities in patients with COVİD-19. Disaster medicine and public health preparedness, 15(2), e9-e11.
56. Taherinezhad, A., ve Alinezhad, A. (2023). Nations performance evaluation during SARS-CoV-2 outbreak handling via data envelopment analysis and machine learning methods. International Journal of Systems Science: Operations & Logistics, 10(1), 2022243.
57. Velias, A., Georganas, S., ve Vandoros, S. (2022). COVİD-19: Early evening curfews and mobility. Social Science & Medicine, 292, 114538.