Year 2021, Volume 8 , Issue 1, Pages 356 - 368 2021-06-30

Covid-19 Pandemi Sürecinde Ülkelerin Demokratik Önlemlerinin Etkilerinin Homojen Tekdüze İlişki Modeli ile İncelenmesi
Homogeneous Uniform Association Model for the Effects of Countries' Democratic Measures on the Covid-19 Pandemic Process Modifier

Merve POSLU [1] , Melike BAHÇECİTAPAR [2] , Serpil AKTAŞ [3]


Covid-19 pandemi sürecinde ülkeler kendilerine özgü politikalar uygulamışlardır. Artan vakaların ve ölüm oranlarının ardından ülkelerin uyguladıkları pandemi önleyici politikalar sonucunda ortaya çıkan demokratik gerileme risk düzeyinin yanı sıra, ülkelerdeki virüs yayılım hızını ve vaka ölüm oranlarını incelemek bu çalışmanın amacını oluşturmaktadır. Önceki çalışmalardan farklı olarak, ülkelerin pandemi demokratik risk düzeyleri hesaplanarak, virüs yayılım hızı ve vaka ölüm oranları birlikte ilk defa incelenmiştir. Veriler, toplam 148 ülkenin kamuya açık kaynaklarından elde edilmiştir. Ülkelerin pandemi önleyici politikalarına ve demokratik gerileme risk düzeylerine göre virüs yayılma hızının ve Covid-19 pozitiften ölüm oranlarının incelenmesi amacıyla düzenlenen iki tane üç boyutlu olumsallık tablosu logaritmik doğrusal modellerin özel bir durumu olan Homojen Tekdüze İlişki modeli ile analiz edilmiştir. Homojen Tekdüze İlişki modelinde virüs yayılma hızı ve vaka ölüm oranları dikkate alınarak, ülkelerin pandemi önleyici politikaları ve demokratik gerileme risk düzeyleri karşılaştırılmıştır. Pandemi önleyici politika sıkılaştıkça, Covid-19 koronavirüsünün yayılım hızı azalmaktadır. Bu durum önleyici politikaların sıkılaşmasını daha olası kılacak ve ölüm oranının ortalama altına düşmesiyle birlikte ülkelerdeki sıkı politikaların gevşeme olasılığı artacaktır.
Countries have implemented their own policies during the Covid-19 pandemic process. The aim of this study is to examine the risk of democratic decline as a result of the preventive policies implemented by countries after increasing cases and death rates as well as the rate of virus spread and case fatality rates in the countries. This paper is the first to analyze countries’ virus spread and case fatality rates together with their risk values of democratic decline. Data sets from a total of 148 countries can be accessible from publicly available sources. The variables related to the pandemic process management of the selected countries are taken as the government Covid-19 response stringency index and risk of democratic decline. Three-way contingency tables are generated with coronavirus effective reproduction number and the death rate from Covid-19 positive variables. Homogeneous Uniform Association model, which is a special case of the log-linear model with ordinal variables, is used for the contingency tables arranged from the raw data. As a result of this study, making preventive policies more likely to be tightened causes to reduce coronavirus spread rate. In this case, it will make tightening of preventive policies more likely in the countries and the likelihood of loosening of tight policies in the countries will increase as the death rate falls below the average.
  • Munster, V.J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). A novel coronavirus emerging in China — key questions for impact assessment. New England Journal of Medicine, 382,692-694.
  • World Health Organization. (2020a). Q&A on coronaviruses (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/q-a-coronaviruses, (02.08. 2020).
  • World Health Organization. (2020b). Naming the coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it, (02.08. 2020).
  • Paules, C.I., Marston, H.D., & Fauci, A.S. (2020). Coronavirus infections-more than just the common cold. JAMA, 323 (8), 707-708.
  • Chen N., Zhou M., Dong X, Qu J., Gong F., Han Y., Qiu Y., Wang J., Liu Y., Wei Y., Xia J., Yu T., Zhang X., & Zhang L. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet, 395 (10223), 507-513.
  • Worldometer. (2021). Coronavirus Updates. https://www.worldometers.info/coronavirus/, (09.05.2021).
  • Zhu, H., W ei, L., & Niu, P. (2020). The novel coronavirus outbreak in Wuhan, China. Global Health Research and Policy, 5(6), 1-3.
  • Li, Q., Guan, X., Wu, P., et al. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England Journal of Medicine, 382 (13), 1199-1207.
  • V-Dem Institute. (2020). The Pandemic Backsliding Project (PanDem). https://github.com/vdeminstitute/pandem, (12.08.2020).
  • Taylor, M.R., Kingsley, E.A., Garry, J.S., & Raphael, B. (2008). Factors influencing psychological distress during a disease epidemic: data from Australia’s first outbreak of equine influenza. BMC Public Health, 8, 1-13.
  • Jones, J.H., & Salathe, M. (2009). Early assessment of anxiety and behavioural response to novel swine-origin influenza A (H1N1). Plos One, 4 (12), 1-8.
  • Miglani, A. (2020). Effect of lockdown during COVID-19: An Indian perspective. International Journal of Science and Healthcare Research, 5 (3), 55-61.
  • Zhang, W.R., Wang, K., Yin, L., Zhao, W.F., Xue, Q., Peng, M. et. al. (2020). Mental health and psychosocial problems of medical health workers during the COVID-19 epidemic in China. Psychotherapy and Psychosomatic, 89, 242-250.
  • Bhaskar, A., Ponnuraja, C., Srinivasan, R., & Padmanaban, S. (2020). Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach. Indian Journal of Public Health, 64, 188-191.
  • Saraçbaşı, T., & Aktaş Altunay, S. (2016). Kategorik Veri Analizi. Hacettepe Üniversitesi, Ankara.
  • Oxford Covid-19 Government Response Tracker. (2020). Covid Policy Tracker.https://github.com/OxCGRT/covid-policy-tracker, (11.08.2020).
  • WHO Coronavirus (COVID-19) Dashboard. (2020). Situation by Country, Territory or Area. https://covid19.who.int/ (08.08.2020).
  • Abbott, S., Hellewell, J., Thompson, R.N., Sherratt, K., Gibbs, H.P., Bosse, N.I., et al. (2020). Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts. Well come Open Research, 5,112.
  • Altın, Z. (2020). Covid-19 pandemisinde yaşlılar. Tepecik Eğitim ve Araştırma Hastanesi Dergisi, 30 (2), 49-57.
  • Ishii-Kuntz, M. (1994). Ordinal Log-Linear Models (Quantitative applications in the social sciences). SAGE Publications, USA, 72.
  • Agresti, A. (2002). Categorical Data Analysis. John Wiley & Sons, Inc., New York, 729.
  • Simonoff, J.S. (2003). Analyzing Categorical Data. Springer Verlag Publication, New York, 498.
Primary Language tr
Subjects Basic Sciences
Journal Section Articles
Authors

Orcid: 0000-0003-4940-8675
Author: Merve POSLU
Institution: HACETTEPE ÜNİVERSİTESİ
Country: Turkey


Orcid: 0000-0002-5443-6278
Author: Melike BAHÇECİTAPAR (Primary Author)
Institution: Hacettepe Üniversitesi Fen Fakültesi İstatistik Bölümü
Country: Turkey


Orcid: 0000-0003-3364-6388
Author: Serpil AKTAŞ
Institution: HACETTEPE ÜNİVERSİTESİ
Country: Turkey


Dates

Application Date : March 30, 2021
Acceptance Date : May 11, 2021
Publication Date : June 30, 2021

APA Poslu, M , Bahçecitapar, M , Aktaş, S . (2021). Covid-19 Pandemi Sürecinde Ülkelerin Demokratik Önlemlerinin Etkilerinin Homojen Tekdüze İlişki Modeli ile İncelenmesi . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 8 (1) , 356-368 . DOI: 10.35193/bseufbd.906268