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Hükümet Politikalarının COVID-19 Pandemisinin Yayılması Üzerindeki Etkileri

Year 2023, Volume: 14 Issue: 37, 24 - 36, 25.02.2023
https://doi.org/10.21076/vizyoner.1069827

Abstract

Çalışmanın amacı, devletlerin insan hareketliliğini kontrol etme politikalarının COVID-19 pandemisiyle mücadelede başarılı olup olmadığını belirlemektir. Hükümetler tarafından insan hareketliliğini kontrol etmek için uygulanan politikalar Oxford Üniversitesi tarafından oluşturulan Sıkılık Endeksi ile belirlenmiştir. İnsan hareketliliği, Google Topluluk Hareket Raporu tarafından sağlanan verilerle gözlemlenmiştir. Ülkelerin COVID-19 pandemisi ile mücadeledeki başarısı Çoğalma Oranı ile ölçülmüştür. 104 ülkeyi kapsayan veriler, ülkelerin 100. kümülatif vakaya ulaşma tarihleri ile 360 gün sonraki tarih arasındaki süre için ilgili resmi web sitelerinin veri tabanlarından toplanmıştır. Hipotezleri test etmek için veriler, panel veri analiz yöntemi ile analiz edilmiştir. Sonuçlar, ülkeler tarafından pandeminin yayılmasını önlemek için uygulanan hükümet politikalarının sıkılığını gösteren Sıkılık Endeksi'nin insan hareketliliği boyutlarını anlamlı ve ters yönde etkilediğini göstermiştir. İnsan hareketliliği boyutları evde kalma üzerinde %95 güven aralığında farklı düzeylerde ters ve anlamlı bir etkiye sahiptir. Ayrıca, evde kalma ile Çoğalma Oranı arasında ters yönde çok küçük bir bi değeri (-0,00008) ile anlamlı bir ilişki ortaya çıkmıştır. 

References

  • Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), 931-934. https://doi:10.1016/S0140-6736(20)30567-5
  • Bai, J., Choi, S. H., & Liao, Y. (2021). Feasible generalized least squares for panel data with cross-sectional and serial correlations. Empirical Economics, 60(1), 309-326.
  • Baltagi, B. H. (2005). Econometric analysis of panel data (Third Edition). John Wiley & Sons Ltd.
  • Banholzer, N., van Weenen, E., Lison, A., Cenedese, A., Seeliger, A., Kratzwald, B., .Vach, W. (2020). Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave. PLoS ONE, 16(6), e0252827.
  • Brzezinski, A., Deiana, G., Kecht, V., & Van, D. (2020). The COVID-19 pandemic: government vs. community action across the United States. Covid Economics: Vetted and Real-Time Papers, 7, 115-156.
  • Chaudhur, S., Basu, S., Kabi, P., Unni, V. R., & Saha, A. (2020). Modeling the role of respiratory droplets in Covid-19 type pandemics. Physics of Fluids, 32(6), 063309. https://doi:10.1063/5.0015984
  • Chen, X., & Qiu, Z. (2020). Scenario analysis of non-pharmaceutical interventions on global COVID-19 transmissions. arXiv:2004.04529.
  • Davies, N. G., Kucharski, A. J., Eggo, R. M., Gimma, A., & Edmunds, W. J. (2020). Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. Lancet Public Health, 5(7), e375-e385. https://doi:10.1016/S2468-2667(20)30133-X
  • Dietz, K. (1993). The estimation of the basic reproduction number for infectious diseases. Statistical Methods in Medical Research, 2(1), 23-41.
  • Engle, S., Stromme, J., & Zhou, A. (2020). Staying at home: mobility effects of COVID-19. SSRN. http://dx.doi.org/10.2139/ssrn.3565703
  • Google. (2020). COVID-19 community mobility reports. Retrieved October 1, 2020 from https://www.google.com/covid19/mobility/?hl=en
  • Hansen, C. B. (2007). Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects. Journal of Econometrics, 140(2), 670-694.
  • Horton, R. (2020). Offline: COVID-19 is not a pandemic. The Lancet, 396(10255), 874.
  • ILO. (2021). ILO Monitor: COVID-19 and the world of work. Retrieved May 20, 2022 from https://www.ilo.org/global/topics/coronavirus/impacts-and-responses/WCMS_767028/lang--en/index.htm%20a
  • ILO. (2022). World employment and social outlook – Trends 2022. Retrieved May 20, 2022 from https://www.ilo.org/global/research/global-reports/weso/trends2022/lang--en/index.htm
  • Im, K. S., Pesaran, H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74. https://doi:10.1016/S0304-4076(03)00092-7
  • IMF. (2020). World economic outlook, April 2020: The great lockdown. Retrieved October 6, 2020 from https://www.imf.org/en/Publications/WEO/Issues/2020/04/14/weo-april-2020
  • Jamison, J., Bundy, D., Jamison, D., Spitz, J., & Verguet, S. (2020). Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries. medRxiv. https://doi:10.1101/2020.08.02.20166793
  • Levin, A., Lin, C. F., & Chu, C. S. (2002). Unit root test in panel data: asymptotic and finite sample properties. Journal of Econometrics, 108, 1-24. https://doi:10.1016/S0304-4076(01)00098-7
  • Linka, K., Peirlinck, M., & Kuhl, E. (2020). The reproduction number of COVID-19 and its correlation with public health interventions. Computational Mechanics, 66(4), 1035-1050. https://doi:10.1101/2020.05.01.20088047
  • Mamun, M. A., & Griffiths, M. D. (2020). First COVID-19 suicide case in Bangladesh due to fear of COVID-19 and xenophobia: possible suicide prevention strategies. Asian Journal of Psychiatry, 51, 102073. https://doi:10.1016/j.ajp.2020.102073
  • Moritz, S., Gottschick, C., Horn, J., Popp, M., Langer, S., Klee, B., Mikolajczyk, R. (2020). The risk of indoor sports and culture events for the transmission of COVID-19 (Restart-19). medRxiv. https://doi:10.1101/2020.10.28.20221580
  • Noland, R. B. (2021). Mobility and the effective reproduction rate of COVID-19. Journal of Transport & Health, 20, 101016. https://doi:10.1016/j.jth.2021.101016
  • Ornell, F., Schuch, J. B., Sordi, A. O., & Kessler, F. H. (2020). ‘‘Pandemic fear’’ and COVID-19: mental health burden and strategies. Braz J Psychiatry, 42(3), 232-235. https://doi:10.1590/1516-4446-2020-0008
  • Our World in Data. (2020). Coronavirus source data. Retrieved October 1, 2020 from https://ourworldindata.org/coronavirus-source-data
  • Oxford University. (2020). Coronavirus government response tracker. Retrieved October 1, 2020 from https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker
  • UNESCO. (2022). Education: from disruption to recovery. Retrieved May 19, 2022 from https://en.unesco.org/covid19/educationresponse
  • UN. (2022). World economic situation prospects. Retrieved May 20, 2022 from https://www.un.org/development/desa/dpad/publication/world-economic-situation-and-prospects-2022/
  • WHO. (2019). Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza. Retrieved October 20, 2020 from https://www.who.int/influenza/publications/public_health_measures/publication/en/
  • Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), 1-4.

The Effects of the Government Policies on the Spread of the COVID-19 Pandemic

Year 2023, Volume: 14 Issue: 37, 24 - 36, 25.02.2023
https://doi.org/10.21076/vizyoner.1069827

Abstract

The study aims to determine whether government policies to control population mobility have been successful in the fight against the coronavirus disease 2019 (COVID-19) pandemic. Policies implemented by governments for controlling population mobility are identified with the Stringency Index prepared by Oxford University. Population mobility is observed through data provided by Google Community Mobility Report. The success of countries in the fight against the COVID-19 pandemic is measured by the Reproduction Rate. The intersection of valid data covering 104 countries is gathered from databases of relevant official websites for the period between the date of reaching the 100th cumulative case and the date 360 days later. The data is analyzed by conducting panel data analysis method to test the hypothesis. Results show that the Stringency Index demonstrating the stringency of government policies implemented by countries to prevent the spreading of pandemic affected human mobility dimensions significantly and reversely. Human mobility dimensions have a reverse and significant impact on staying at home at different levels at the 95% confidence interval. Furthermore, a significant relationship with a very small bi value (-0.00008) emerges between staying at home and the Reproduction Rate in the reverse direction. 

References

  • Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), 931-934. https://doi:10.1016/S0140-6736(20)30567-5
  • Bai, J., Choi, S. H., & Liao, Y. (2021). Feasible generalized least squares for panel data with cross-sectional and serial correlations. Empirical Economics, 60(1), 309-326.
  • Baltagi, B. H. (2005). Econometric analysis of panel data (Third Edition). John Wiley & Sons Ltd.
  • Banholzer, N., van Weenen, E., Lison, A., Cenedese, A., Seeliger, A., Kratzwald, B., .Vach, W. (2020). Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave. PLoS ONE, 16(6), e0252827.
  • Brzezinski, A., Deiana, G., Kecht, V., & Van, D. (2020). The COVID-19 pandemic: government vs. community action across the United States. Covid Economics: Vetted and Real-Time Papers, 7, 115-156.
  • Chaudhur, S., Basu, S., Kabi, P., Unni, V. R., & Saha, A. (2020). Modeling the role of respiratory droplets in Covid-19 type pandemics. Physics of Fluids, 32(6), 063309. https://doi:10.1063/5.0015984
  • Chen, X., & Qiu, Z. (2020). Scenario analysis of non-pharmaceutical interventions on global COVID-19 transmissions. arXiv:2004.04529.
  • Davies, N. G., Kucharski, A. J., Eggo, R. M., Gimma, A., & Edmunds, W. J. (2020). Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. Lancet Public Health, 5(7), e375-e385. https://doi:10.1016/S2468-2667(20)30133-X
  • Dietz, K. (1993). The estimation of the basic reproduction number for infectious diseases. Statistical Methods in Medical Research, 2(1), 23-41.
  • Engle, S., Stromme, J., & Zhou, A. (2020). Staying at home: mobility effects of COVID-19. SSRN. http://dx.doi.org/10.2139/ssrn.3565703
  • Google. (2020). COVID-19 community mobility reports. Retrieved October 1, 2020 from https://www.google.com/covid19/mobility/?hl=en
  • Hansen, C. B. (2007). Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects. Journal of Econometrics, 140(2), 670-694.
  • Horton, R. (2020). Offline: COVID-19 is not a pandemic. The Lancet, 396(10255), 874.
  • ILO. (2021). ILO Monitor: COVID-19 and the world of work. Retrieved May 20, 2022 from https://www.ilo.org/global/topics/coronavirus/impacts-and-responses/WCMS_767028/lang--en/index.htm%20a
  • ILO. (2022). World employment and social outlook – Trends 2022. Retrieved May 20, 2022 from https://www.ilo.org/global/research/global-reports/weso/trends2022/lang--en/index.htm
  • Im, K. S., Pesaran, H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74. https://doi:10.1016/S0304-4076(03)00092-7
  • IMF. (2020). World economic outlook, April 2020: The great lockdown. Retrieved October 6, 2020 from https://www.imf.org/en/Publications/WEO/Issues/2020/04/14/weo-april-2020
  • Jamison, J., Bundy, D., Jamison, D., Spitz, J., & Verguet, S. (2020). Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries. medRxiv. https://doi:10.1101/2020.08.02.20166793
  • Levin, A., Lin, C. F., & Chu, C. S. (2002). Unit root test in panel data: asymptotic and finite sample properties. Journal of Econometrics, 108, 1-24. https://doi:10.1016/S0304-4076(01)00098-7
  • Linka, K., Peirlinck, M., & Kuhl, E. (2020). The reproduction number of COVID-19 and its correlation with public health interventions. Computational Mechanics, 66(4), 1035-1050. https://doi:10.1101/2020.05.01.20088047
  • Mamun, M. A., & Griffiths, M. D. (2020). First COVID-19 suicide case in Bangladesh due to fear of COVID-19 and xenophobia: possible suicide prevention strategies. Asian Journal of Psychiatry, 51, 102073. https://doi:10.1016/j.ajp.2020.102073
  • Moritz, S., Gottschick, C., Horn, J., Popp, M., Langer, S., Klee, B., Mikolajczyk, R. (2020). The risk of indoor sports and culture events for the transmission of COVID-19 (Restart-19). medRxiv. https://doi:10.1101/2020.10.28.20221580
  • Noland, R. B. (2021). Mobility and the effective reproduction rate of COVID-19. Journal of Transport & Health, 20, 101016. https://doi:10.1016/j.jth.2021.101016
  • Ornell, F., Schuch, J. B., Sordi, A. O., & Kessler, F. H. (2020). ‘‘Pandemic fear’’ and COVID-19: mental health burden and strategies. Braz J Psychiatry, 42(3), 232-235. https://doi:10.1590/1516-4446-2020-0008
  • Our World in Data. (2020). Coronavirus source data. Retrieved October 1, 2020 from https://ourworldindata.org/coronavirus-source-data
  • Oxford University. (2020). Coronavirus government response tracker. Retrieved October 1, 2020 from https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker
  • UNESCO. (2022). Education: from disruption to recovery. Retrieved May 19, 2022 from https://en.unesco.org/covid19/educationresponse
  • UN. (2022). World economic situation prospects. Retrieved May 20, 2022 from https://www.un.org/development/desa/dpad/publication/world-economic-situation-and-prospects-2022/
  • WHO. (2019). Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza. Retrieved October 20, 2020 from https://www.who.int/influenza/publications/public_health_measures/publication/en/
  • Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), 1-4.
There are 30 citations in total.

Details

Primary Language English
Subjects Health Policy
Journal Section Research Articles
Authors

Çiğdem Baskıcı 0000-0003-0712-1481

Yunus Gokmen This is me 0000-0002-6107-0577

Yavuz Ercil 0000-0003-2016-7329

Early Pub Date February 23, 2023
Publication Date February 25, 2023
Submission Date February 8, 2022
Published in Issue Year 2023 Volume: 14 Issue: 37

Cite

APA Baskıcı, Ç., Gokmen, Y., & Ercil, Y. (2023). The Effects of the Government Policies on the Spread of the COVID-19 Pandemic. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 14(37), 24-36. https://doi.org/10.21076/vizyoner.1069827

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