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AVRUPA ÜLKELERİNDE COVID-19'UN YAYILMASI: DEVLET POLİTİKALARI ETKİLİ Mİ?

Year 2023, Volume: 26 Issue: 3, 707 - 722, 26.09.2023

Gizem Kaya Umut Aydın Burç Ülengin Melis Almula Karadayı Füsun Ülengin

Abstract

Avrupa ülkeleri zaman zaman ulusal sınır kısıtlamaları uygulasa da COVID-19 pandemisinin başlangıcından bu yana vatandaşların ulusal sınırlar arasında serbest dolaşımı nedeniyle Avrupa pandemiden en çok etkilenen bölgelerden biri olmuştur. Bu çalışmada, COVID-19 vakalarının ülkeler arasındaki yayılımı ve COVID-19'a karşı ülke içinde ve ülkeler arasında uygulanan kısıtlamaların etkileri, vektör hata düzeltme modeli kullanılarak, COVID-19 vakalarının yoğun olarak görüldüğü beş Avrupa ülkesi olan Fransa, Almanya, İtalya, İspanya ve Birleşik Krallık (İngiltere) göz önünde bulundurularak incelenmiştir. Veri dönemi 27 Mart 2020 ile 4 Haziran 2021 tarihleri arasındaki haftalık verileri kapsamaktadır. Sonuçlara göre, sıkılık endeksindeki artış, Fransa ve İtalya için iki hafta sonra, İspanya için ise üç hafta sonra haftalık COVID-19 vaka sayısını önemli ölçüde azaltmıştır. Başka bir deyişle, COVID-19'a karşı belirli bir politikanın kaydedilen vaka sayısı üzerindeki etkisini gözlemlemek yaklaşık 2-3 hafta sürmektedir. COVID-19'un yayılması açısından, Fransa'daki vakalarda bir şok olduğunda Almanya ve İtalya'daki vakaların en çok etkilendiği tespit edilmiştir. Almanya’daki vakalarda bir şok olduğunda en çok İtalya'daki vakalar etkilenmiştir. İtalya’daki vakalarda bir şok olduğunda en çok Almanya'daki vakalar etkilenmiştir. İspanya’daki vakalarda bir şok olduğunda en çok Almanya'daki vakalar etkilenmiştir. Son olarak, Birleşik Krallık’taki vakalarda bir şok olduğunda en çok Almanya'daki vakalar etkilenmiştir. Özetle, COVID-19 vakaları arttığında Avrupa ülkelerinde en olumsuz etkilenen ülkeler Almanya ve İtalya olarak görünmektedir. Uluslararası seyahat, ülkenin sağlık altyapısı ve insanların maske kullanma alışkanlığı ülkeler arasındaki bu farklılığa neden olabilmektedir.

Keywords

COVID-19 Vektör Hata Düzeltme Etki Tepki Analizi

References

  • Alzahrani, S. M. (2022). A log linear Poisson autoregressive model to understand COVID-19 dynamics in Saudi Arabia. Beni-Suef University Journal of Basic and Applied Sciences, 11(1), 118.
  • Amdaoud, M., Arcuri, G., & Levratto, N. (2021). Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe. The European Journal of Health Economics, 1-14.
  • Bashir, M. F., Benjiang, M. A., & Shahzad, L. (2020). A brief review of socio-economic and environmental impact of COVID-19. Air Quality, Atmosphere & Health, 13(12), 1403-1409.
  • BBC. (2023, 4 June). Dünya Sağlık Örgütü (WHO) COVID-19'un sağlık açısından artık "küresel bir acil durumu" teşkil etmediğini açıkladı. https://www.bbc.com/turkce/articles/cv2k804x19ro.
  • BIAC. (2020, July 1). Key messages on the impact of COVID-19 international travel restrictions on services-trade costs. http://biac.org/wp-content/uploads/2020/06/KM-TAD-2020-03-FIN-COV ID-19-international-travel-restrictions-on-services-trade-costs-public-1.pdf.
  • Bontempi, E. (2021). The Europe second wave of COVID-19 infection and the Italy “strange” situation. Environmental Research, 193, 110476.
  • Britt, T., Nusbaum, J., Savinkina, A., & Shemyakin, A. (2023). Short-term forecast of US COVID mortality using excess deaths and vector autoregression. Model Assisted Statistics and Applications, 18(1), 13-31.
  • Chan, S., Chu, J., Zhang, Y., & Nadarajah, S. (2021). Count regression models for COVID-19. Physica A: Statistical Mechanics and its Applications, 563, 125460.
  • Çöl, M. (2021). İtalya’da sağlık sistemi ve COVID-19 pandemisi yanıtı. Toplum ve Hekim, 36(5), 388-400.
  • Devarakonda, P., Sadasivuni, R., Wu, J., & Shaw, D. (2021). Spatial diffusion of COVID-19: An econometric-based approach. Authorea Preprints.
  • DuPre, N. C., Karimi, S., Zhang, C. H., Blair, L., Gupta, A., Alharbi, L. M. A., ... & Little, B. (2021). County-level demographic, social, economic, and lifestyle correlates of COVID-19 infection and death trajectories during the first wave of the pandemic in the United States. Science of The Total Environment, 786, 147495.
  • EU Commission. (2020, July 1). Guidelines concerning the exercise of the free movement of workers during COVID-19 outbreak. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELE X:52020XC0330(03)&from=EN.
  • Hafner, C. M. (2020). The spread of the COVID-19 pandemic in time and space. International Journal of Environmental Research and Public Health, 17(11), 3827.
  • Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., & Tatlow, H. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature human behaviour, 5(4), 529–538.
  • Huang, R., Liu, M., & Ding, Y. (2020). Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. The Journal of Infection in Developing Countries, 14(03), 246-253.
  • IMF. (2020, July 1). World Economic Outlook, April 2020: The Great Lockdown. https://www.imf.org/en/Publications/WEO/Issues/2020/04/14/weo-april-2020.
  • Jain, M., Sharma, G. D., Goyal, M., Kaushal, R., & Sethi, M. (2021). Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia. Environmental Science and Pollution Research, 28(22), 28518-28534.
  • Johansen, S. (1992). Cointegration in partial systems and the efficiency of single-equation analysis. Journal of Econometrics, 52(3), 389-402.
  • Khan, F., Saeed, A., & Ali, S. (2020). Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan. Chaos, Solitons & Fractals, 140, 110189.
  • Krisztin, T., Piribauer, P., & Wögerer, M. (2020). The spatial econometrics of the coronavirus pandemic. Letters in Spatial and Resource Sciences, 13(3), 209-218.
  • Levendis, J. D. (2018). Time Series Econometrics. Springer International Publishing.
  • Martin, A., Markhvida, M., Hallegatte, S., & Walsh, B. (2020). Socio-economic impacts of COVID-19 on household consumption and poverty. Economics of Disasters and Climate Change, 4(3), 453-479.
  • Mazur, M., Dang, M., & Vega, M. (2021). COVID-19 and the march 2020 stock market crash. Evidence from S&P1500. Finance Research Letters, 38, 101690.
  • Mogi, R., & Spijker, J. (2021). The influence of social and economic ties to the spread of COVID-19 in Europe. Journal of Population Research, 1-17.
  • Monllor, P., Su, Z., Gabrielli, L., & Taltavull de La Paz, P. (2020). COVID-19 infection process in Italy and Spain: Are data talking? Evidence from ARMA and vector autoregression models. Frontiers in Public Health, 8, 784.
  • OECD. (2020, July 1). Managing international migration under COVID-19, OECD Policy Brief. http://www.oecd.org/coronavirus/policy-responses/managing-international-migration-under-co vid-19-6e914d57/#section-d1e175.
  • Oliveira, G. L. A. D., Lima, L., Silva, I., Ribeiro-Dantas, M. D. C., Monteiro, K. H., & Endo, P. T. (2021). Evaluating social distancing measures and their association with the COVID-19 pandemic in South America. ISPRS International Journal of Geo-Information, 10(3), 121.
  • Roy, S., Bhunia, G. S., & Shit, P. K. (2021). Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Modeling Earth Systems and Environment, 7, 1385-1391.
  • Sannigrahi, S., Pilla, F., Basu, B., Basu, A. S., & Molter, A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62, 102418.
  • Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
  • Singh, R. K., Rani, M., Bhagavathula, A. S., Sah, R., Rodriguez-Morales, A. J., Kalita, H., ... & Kumar, P. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance, 6(2), e19115.
  • Wang, Q., Zhou, Y., & Chen, X. (2021). A vector autoregression prediction model for COVID-19 outbreak. ArXiv Preprint.
  • WHO. (2021, June 29). World Health Organization. https://www.who.int/emergencies/ diseases/novel-coronavirus-2019.
  • Worldometer. (2021, June 29). Worldometer. https://www.worldometers.info/coronavirus/#countries.
  • World Trade Organization. (2020, July 1). Report on G20 trade measures, mid-May 2020 to mid-October 2020. https://www.wto.org/english/news_e/news20_e/report_trdev_nov20_e.pdf.
  • Xie, X., Naminse, E. Y., Liu, S., & Yi, Q. (2020). The spatial and temporal pattern of COVID-19 and its effect on humans’ development in China. Global Journal of Environmental Science and Management, 6(Special Issue (Covid-19)), 107-118.
  • Zhu, D., Mishra, S. R., Han, X., & Santo, K. (2020). Social distancing in Latin America during the COVID-19 pandemic: an analysis using the Stringency Index and Google Community Mobility Reports. Journal of Travel Medicine, 27(8).

SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?

Year 2023, Volume: 26 Issue: 3, 707 - 722, 26.09.2023

Gizem Kaya Umut Aydın Burç Ülengin Melis Almula Karadayı Füsun Ülengin

Abstract

Although European countries occasionally impose national border restrictions, Europe was one of the regions that is most affected by the pandemic, owing to the free movement of citizens across national borders since the beginning of the COVID-19 pandemic. In this study, the spread of COVID-19 cases among countries and the effects of the stringencies imposed against COVID-19 within and between countries were investigated with consideration to five European countries with a large amount of COVID-19 cases, namely, France, Germany, Italy, Spain, and the United Kingdom (UK) by using Vector Error Correction Model. The data period covers the weekly data from March 27, 2020, to June 4, 2021. According to the results, the increase in the stringency index significantly reduced the number of COVID-19 cases per week after two weeks for France and Italy, and after three weeks for Spain. In other words, it takes about 2–3 weeks to observe the impact of a certain policy against COVID-19 on the number of recorded cases. In terms of the spread of COVID-19, cases in Germany and Italy were the most affected when there was a shock to cases in France. When there was a shock in cases in Germany, cases in Italy were the most affected. When there was a shock in cases in Italy, cases in Germany were the most affected. When there was a shock in cases in Spain, cases in Germany were the most affected. Finally, when there was a shock in cases in the United Kingdom, cases in Germany were the most affected. In summary, Germany and Italy appear to be the most negatively affected countries in Europe when COVID-19 cases increase. International travel, the health infrastructures of the country, and people's habit of using masks may cause this difference in countries.

Keywords

COVID-19 Vector Error Correction Impulse Response Analysis

References

  • Alzahrani, S. M. (2022). A log linear Poisson autoregressive model to understand COVID-19 dynamics in Saudi Arabia. Beni-Suef University Journal of Basic and Applied Sciences, 11(1), 118.
  • Amdaoud, M., Arcuri, G., & Levratto, N. (2021). Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe. The European Journal of Health Economics, 1-14.
  • Bashir, M. F., Benjiang, M. A., & Shahzad, L. (2020). A brief review of socio-economic and environmental impact of COVID-19. Air Quality, Atmosphere & Health, 13(12), 1403-1409.
  • BBC. (2023, 4 June). Dünya Sağlık Örgütü (WHO) COVID-19'un sağlık açısından artık "küresel bir acil durumu" teşkil etmediğini açıkladı. https://www.bbc.com/turkce/articles/cv2k804x19ro.
  • BIAC. (2020, July 1). Key messages on the impact of COVID-19 international travel restrictions on services-trade costs. http://biac.org/wp-content/uploads/2020/06/KM-TAD-2020-03-FIN-COV ID-19-international-travel-restrictions-on-services-trade-costs-public-1.pdf.
  • Bontempi, E. (2021). The Europe second wave of COVID-19 infection and the Italy “strange” situation. Environmental Research, 193, 110476.
  • Britt, T., Nusbaum, J., Savinkina, A., & Shemyakin, A. (2023). Short-term forecast of US COVID mortality using excess deaths and vector autoregression. Model Assisted Statistics and Applications, 18(1), 13-31.
  • Chan, S., Chu, J., Zhang, Y., & Nadarajah, S. (2021). Count regression models for COVID-19. Physica A: Statistical Mechanics and its Applications, 563, 125460.
  • Çöl, M. (2021). İtalya’da sağlık sistemi ve COVID-19 pandemisi yanıtı. Toplum ve Hekim, 36(5), 388-400.
  • Devarakonda, P., Sadasivuni, R., Wu, J., & Shaw, D. (2021). Spatial diffusion of COVID-19: An econometric-based approach. Authorea Preprints.
  • DuPre, N. C., Karimi, S., Zhang, C. H., Blair, L., Gupta, A., Alharbi, L. M. A., ... & Little, B. (2021). County-level demographic, social, economic, and lifestyle correlates of COVID-19 infection and death trajectories during the first wave of the pandemic in the United States. Science of The Total Environment, 786, 147495.
  • EU Commission. (2020, July 1). Guidelines concerning the exercise of the free movement of workers during COVID-19 outbreak. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELE X:52020XC0330(03)&from=EN.
  • Hafner, C. M. (2020). The spread of the COVID-19 pandemic in time and space. International Journal of Environmental Research and Public Health, 17(11), 3827.
  • Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., & Tatlow, H. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature human behaviour, 5(4), 529–538.
  • Huang, R., Liu, M., & Ding, Y. (2020). Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. The Journal of Infection in Developing Countries, 14(03), 246-253.
  • IMF. (2020, July 1). World Economic Outlook, April 2020: The Great Lockdown. https://www.imf.org/en/Publications/WEO/Issues/2020/04/14/weo-april-2020.
  • Jain, M., Sharma, G. D., Goyal, M., Kaushal, R., & Sethi, M. (2021). Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia. Environmental Science and Pollution Research, 28(22), 28518-28534.
  • Johansen, S. (1992). Cointegration in partial systems and the efficiency of single-equation analysis. Journal of Econometrics, 52(3), 389-402.
  • Khan, F., Saeed, A., & Ali, S. (2020). Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan. Chaos, Solitons & Fractals, 140, 110189.
  • Krisztin, T., Piribauer, P., & Wögerer, M. (2020). The spatial econometrics of the coronavirus pandemic. Letters in Spatial and Resource Sciences, 13(3), 209-218.
  • Levendis, J. D. (2018). Time Series Econometrics. Springer International Publishing.
  • Martin, A., Markhvida, M., Hallegatte, S., & Walsh, B. (2020). Socio-economic impacts of COVID-19 on household consumption and poverty. Economics of Disasters and Climate Change, 4(3), 453-479.
  • Mazur, M., Dang, M., & Vega, M. (2021). COVID-19 and the march 2020 stock market crash. Evidence from S&P1500. Finance Research Letters, 38, 101690.
  • Mogi, R., & Spijker, J. (2021). The influence of social and economic ties to the spread of COVID-19 in Europe. Journal of Population Research, 1-17.
  • Monllor, P., Su, Z., Gabrielli, L., & Taltavull de La Paz, P. (2020). COVID-19 infection process in Italy and Spain: Are data talking? Evidence from ARMA and vector autoregression models. Frontiers in Public Health, 8, 784.
  • OECD. (2020, July 1). Managing international migration under COVID-19, OECD Policy Brief. http://www.oecd.org/coronavirus/policy-responses/managing-international-migration-under-co vid-19-6e914d57/#section-d1e175.
  • Oliveira, G. L. A. D., Lima, L., Silva, I., Ribeiro-Dantas, M. D. C., Monteiro, K. H., & Endo, P. T. (2021). Evaluating social distancing measures and their association with the COVID-19 pandemic in South America. ISPRS International Journal of Geo-Information, 10(3), 121.
  • Roy, S., Bhunia, G. S., & Shit, P. K. (2021). Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Modeling Earth Systems and Environment, 7, 1385-1391.
  • Sannigrahi, S., Pilla, F., Basu, B., Basu, A. S., & Molter, A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62, 102418.
  • Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
  • Singh, R. K., Rani, M., Bhagavathula, A. S., Sah, R., Rodriguez-Morales, A. J., Kalita, H., ... & Kumar, P. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health and Surveillance, 6(2), e19115.
  • Wang, Q., Zhou, Y., & Chen, X. (2021). A vector autoregression prediction model for COVID-19 outbreak. ArXiv Preprint.
  • WHO. (2021, June 29). World Health Organization. https://www.who.int/emergencies/ diseases/novel-coronavirus-2019.
  • Worldometer. (2021, June 29). Worldometer. https://www.worldometers.info/coronavirus/#countries.
  • World Trade Organization. (2020, July 1). Report on G20 trade measures, mid-May 2020 to mid-October 2020. https://www.wto.org/english/news_e/news20_e/report_trdev_nov20_e.pdf.
  • Xie, X., Naminse, E. Y., Liu, S., & Yi, Q. (2020). The spatial and temporal pattern of COVID-19 and its effect on humans’ development in China. Global Journal of Environmental Science and Management, 6(Special Issue (Covid-19)), 107-118.
  • Zhu, D., Mishra, S. R., Han, X., & Santo, K. (2020). Social distancing in Latin America during the COVID-19 pandemic: an analysis using the Stringency Index and Google Community Mobility Reports. Journal of Travel Medicine, 27(8).
There are 37 citations in total.

Details

Primary Language English
Subjects Health Care Administration
Journal Section Makaleler
Authors

Gizem Kaya 0000-0002-6870-7219

Umut Aydın 0000-0003-4802-8793

Burç Ülengin 0000-0001-5276-8861

Melis Almula Karadayı 0000-0002-6959-9168

Füsun Ülengin 0000-0003-1738-9756

Publication Date September 26, 2023
Published in Issue Year 2023 Volume: 26 Issue: 3

Cite

APA Kaya, G., Aydın, U., Ülengin, B., Karadayı, M. A., et al. (2023). SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?. Hacettepe Sağlık İdaresi Dergisi, 26(3), 707-722.
AMA Kaya G, Aydın U, Ülengin B, Karadayı MA, Ülengin F. SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?. HSİD. September 2023;26(3):707-722.
Chicago Kaya, Gizem, Umut Aydın, Burç Ülengin, Melis Almula Karadayı, and Füsun Ülengin. “SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?”. Hacettepe Sağlık İdaresi Dergisi 26, no. 3 (September 2023): 707-22.
EndNote Kaya G, Aydın U, Ülengin B, Karadayı MA, Ülengin F (September 1, 2023) SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?. Hacettepe Sağlık İdaresi Dergisi 26 3 707–722.
IEEE G. Kaya, U. Aydın, B. Ülengin, M. A. Karadayı, and F. Ülengin, “SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?”, HSİD, vol. 26, no. 3, pp. 707–722, 2023.
ISNAD Kaya, Gizem et al. “SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?”. Hacettepe Sağlık İdaresi Dergisi 26/3 (September 2023), 707-722.
JAMA Kaya G, Aydın U, Ülengin B, Karadayı MA, Ülengin F. SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?. HSİD. 2023;26:707–722.
MLA Kaya, Gizem et al. “SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?”. Hacettepe Sağlık İdaresi Dergisi, vol. 26, no. 3, 2023, pp. 707-22.
Vancouver Kaya G, Aydın U, Ülengin B, Karadayı MA, Ülengin F. SPREAD OF COVID-19 IN EUROPEAN COUNTRIES: ARE STRINGENCIES EFFECTIVE?. HSİD. 2023;26(3):707-22.
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