Research Article
PDF Zotero Mendeley EndNote BibTex Cite

Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi

Year 2020, Volume 25, Issue Special Issue on COVID 19, 130 - 141, 20.03.2020
https://doi.org/10.21673/anadoluklin.719629

Abstract

2019 yılı sonunda Çin de başlayan COVID-19 enfeksiyonu resmi kayıtlara göre 10 Nisan itibariyle 185 ülkede görülen bir pandemi durumundadır. Hastalığın resmi bir tedavisi bulunmadığından, daha önce vakaların görüldüğü ülkelerin bilgilerinden yararlanılmaktadır. 10 Mart 2020 tarihinde Türkiye’de ilk vaka kayıt altına alınmıştır. Önceki ülkelerdeki vaka durumunun seyriden hareketle alınacak tedbirler ve uygulamalara karar verilmektedir. Şu ana kadar birçok resmi kaynak veri kaydını düzenli olarak yapmaktadır. Elde edilen bu verilerden hareketle ülkemizdeki durumun ortaya konması yapılacak çalışmalara ışık tutacaktır. Bu çalışmada Türkiye ve 22 farklı ülke çeşitli istatistikler bakımından karşılaştırılmıştır. Çalışmanın devamında ülkemizdeki seyrin durumunu tahmin etmek için büyüme eğrileri ve zaman serisi analizinden faydalanılmıştır. İlk bir aylık verilerden hareketle elde edilen tahminler sonucunda toplam vaka sayısının ve toplam ölüm sayısının öngörülmesinde Üstel düzleştirme (Box-Cox) yönteminin kullanılmasının uygun olacağı belirlenmiştir.

References

  • Ankaralı H, Ankaralı S, Erarslan, N. COVID-19, SARS-CoV2, Enfeksiyonu: Güncel Epidemiyolojik Analiz ve Hastalık Seyrinin Modellemesi. Anadolu Kliniği Tıp Bilimleri Dergisi Ocak 2020; Cilt 25, Ek Sayı 1
  • Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020 Jan 29; [Epub ahead of print]. doi: 10.1056/NEJMoa2001316.
  • Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020 Feb 7; [Epub ahead of print]. doi: 10.1001/jama.2020. 1585.
  • Chang D, Lin M, Wei L, Xie L, Zhu G, Dela Cruz CS. Epidemiologic and clinical characteristics of novel coronavirus infections involving 13 patients outside Wuhan, China. JAMA 2020 Feb 7; [Epub ahead of print]. doi: 10.1001/ jama.2020.1623.
  • Carlos WG, Dela Cruz CS, Cao B, Pasnick S, Jamil S. Novel Wuhan (2019- nCoV) coronavirus. Am J Respir Crit Care Med 2020; 201:P7–8. doi: 10.1164/ rccm.2014P7.
  • Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh, PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents 2020; 55.
  • Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, 2020; 1–4, doi: 10.1093/jtm/taaa021.
  • Hu, Z, Ge Q, Li S, Jin L, Xiong M. Artificial Intelligence Forecasting of COVID-19 in China. https://towardsdatascience.com/artificial-intelligence-against-COVID-19-an-early-review 92a8360edaba.
  • Chen X, Yu B. First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: realtime surveillance and evaluation with a second derivative model. Global Health Research and Policy 2020; 5:7, https://doi.org/10.1186/s41256-020-00137-4.
  • Fu X, Ying Q, Zeng T, Long T, Wang Y. Simulating and forecasting the cumulative confirmed cases of SARS-CoV-2 in china by Boltzmann function-based regression analyses. Journal of Infection, 2020; doi: 10.1016/j.jinf.2020.02.019.
  • Wu K, Darcet D, Wang Q, Sornette. Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the World. https://www.medrxiv.org/content/10.1101/2020.03.11.20034363v1.
  • Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg A, Hyman JM, Yan P, Chowell G. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th. Infectious Disease Modelling 2020; 5, 256-263.
  • Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. Plos One 2020; https://doi.org/10.1371/journal.pone.0231236
  • Box, J, Jenkins, E. (1976), Time Series Analysis Forecasting and Control. California.
  • Kayım, H. (1985), İstatistiksel Ön Tahmin Yöntemleri. Ankara.
  • Priestley, M.B. (1991), Non-Linear and Non-Stationary Time series Analysis. Academic Press, London.
  • Hamzaçebi C, Kutay F. Yapay sinir ağları ile Türkiye elektrik enerjisi tüketiminin 2010 yılına kadar tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2004; Cilt:19, No.3.
  • Akgül, İ. (2003), Zaman Serilerinin Analizi ve ARIMA Modelleri. Der Yayınları, İstanbul.
  • Kadılar, C. (2005), SPSS Uygulamalı Zaman Serileri Analizine Giriş. Bizim Büro Basımevi, Ankara.
  • Chatfield, C. (1980), The Analysis of the Time Series an Introduction. London.
  • Makridakis, S, Wheelwright, S. (1978) Interactive Forecasting Univariate and Multivariate Methods. Holden Day Inc., San Francisco.
  • Akbaş Y. Büyüme eğrisi modellerinin karşılaştırılması. Hayvansal Üretim, 1995; 36, 73-81.
  • Svetunkov I. (2017), Statistical Models underlying functions of ‘smooth’ package for R. Working Paper, Lancaster University.
  • Svetunkov I, Kourentzes N. (2015), Complex Exponential Smoothing. MPRA Paper no:69394, Lancaster University.
  • Kourentzesa N, Petropoulos F. Forecasting with R. International Symposium on Forecasting 2016 (ISF2016).
  • Bergmeir C, Hyndman RJ, Benitez JM. (2014), Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation. Working Paper, Monash University.
  • https://tr.wikipedia.org/ (Erişim Tarihi: 10 Nisan 2020)
  • https://ourworldindata.org/air-pollution (Erişim Tarihi: 5 Nisan 2020)
  • http://hdr.undp.org/en/content/2019-human-development-index-ranking (Erişim Tarihi: 4 Nisan 2020)
  • https://ourworldindata.org/grapher/hospital-beds-per-1000-people (Erişim Tarihi: 4 Nisan 2020)
  • https://ourworldindata.org/grapher/nurses-and-midwives-per-1000-people (Erişim Tarihi: 4 Nisan 2020)
  • https://systems.jhu.edu/ (Erişim Tarihi: 8 Nisan 2020)
  • https://covid19.saglik.gov.tr/ (Erişim Tarihi: 10 Nisan 2020)
  • https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (Erişim Tarihi: 8 Nisan 2020)
  • https://www.cdc.gov/coronavirus/2019-ncov/index.html (Erişim Tarihi: 9 Nisan 2020)
  • http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml (Erişim Tarihi: 5 Nisan 2020)
  • https://coronavirus.1point3acres.com/ (Erişim Tarihi: 9 Nisan 2020)
  • https://www.worldometers.info/coronavirus/ (Erişim Tarihi: 10 Nisan 2020)
  • https://bnonews.com/index.php/2020/04/the-latest-coronavirus-cases/ (Erişim Tarihi: 11 Nisan 2020)
  • https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 (Erişim Tarihi: 12 Nisan 2020)
  • www.who.int (Erişim Tarihi: 10 Nisan 2020)

COVID-19 Infection in Turkey: Statistical Modeling as the Early Perioed Statistics and Disease Course

Year 2020, Volume 25, Issue Special Issue on COVID 19, 130 - 141, 20.03.2020
https://doi.org/10.21673/anadoluklin.719629

Abstract

The COVID-19 infection, which started in China at the end of 2019, is a pandemic seen in 185 countries as of April 10, according to official records. Since there is no official treatment for the disease, the information of the countries where the cases have been seen is used. In Turkey on March 10, 2020, the first case was taken under record. Measures and practices to be taken from the course of the case in previous countries are decided. So far, many official sources record data regularly. Based on these data obtained, revealing the situation in our country will shed light on the studies to be carried out. This study compared 22 different countries to Turkey and various statistics. In the continuation of the study, growth curves and time series analysis were used to estimate the course of our country. As a result of the estimations obtained from the data of the first month, it was determined that the exponential smoothing (Box-Cox) method would be appropriate to predict the total number of cases and the total number of deaths.

References

  • Ankaralı H, Ankaralı S, Erarslan, N. COVID-19, SARS-CoV2, Enfeksiyonu: Güncel Epidemiyolojik Analiz ve Hastalık Seyrinin Modellemesi. Anadolu Kliniği Tıp Bilimleri Dergisi Ocak 2020; Cilt 25, Ek Sayı 1
  • Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020 Jan 29; [Epub ahead of print]. doi: 10.1056/NEJMoa2001316.
  • Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020 Feb 7; [Epub ahead of print]. doi: 10.1001/jama.2020. 1585.
  • Chang D, Lin M, Wei L, Xie L, Zhu G, Dela Cruz CS. Epidemiologic and clinical characteristics of novel coronavirus infections involving 13 patients outside Wuhan, China. JAMA 2020 Feb 7; [Epub ahead of print]. doi: 10.1001/ jama.2020.1623.
  • Carlos WG, Dela Cruz CS, Cao B, Pasnick S, Jamil S. Novel Wuhan (2019- nCoV) coronavirus. Am J Respir Crit Care Med 2020; 201:P7–8. doi: 10.1164/ rccm.2014P7.
  • Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh, PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents 2020; 55.
  • Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, 2020; 1–4, doi: 10.1093/jtm/taaa021.
  • Hu, Z, Ge Q, Li S, Jin L, Xiong M. Artificial Intelligence Forecasting of COVID-19 in China. https://towardsdatascience.com/artificial-intelligence-against-COVID-19-an-early-review 92a8360edaba.
  • Chen X, Yu B. First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: realtime surveillance and evaluation with a second derivative model. Global Health Research and Policy 2020; 5:7, https://doi.org/10.1186/s41256-020-00137-4.
  • Fu X, Ying Q, Zeng T, Long T, Wang Y. Simulating and forecasting the cumulative confirmed cases of SARS-CoV-2 in china by Boltzmann function-based regression analyses. Journal of Infection, 2020; doi: 10.1016/j.jinf.2020.02.019.
  • Wu K, Darcet D, Wang Q, Sornette. Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the World. https://www.medrxiv.org/content/10.1101/2020.03.11.20034363v1.
  • Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg A, Hyman JM, Yan P, Chowell G. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th. Infectious Disease Modelling 2020; 5, 256-263.
  • Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. Plos One 2020; https://doi.org/10.1371/journal.pone.0231236
  • Box, J, Jenkins, E. (1976), Time Series Analysis Forecasting and Control. California.
  • Kayım, H. (1985), İstatistiksel Ön Tahmin Yöntemleri. Ankara.
  • Priestley, M.B. (1991), Non-Linear and Non-Stationary Time series Analysis. Academic Press, London.
  • Hamzaçebi C, Kutay F. Yapay sinir ağları ile Türkiye elektrik enerjisi tüketiminin 2010 yılına kadar tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2004; Cilt:19, No.3.
  • Akgül, İ. (2003), Zaman Serilerinin Analizi ve ARIMA Modelleri. Der Yayınları, İstanbul.
  • Kadılar, C. (2005), SPSS Uygulamalı Zaman Serileri Analizine Giriş. Bizim Büro Basımevi, Ankara.
  • Chatfield, C. (1980), The Analysis of the Time Series an Introduction. London.
  • Makridakis, S, Wheelwright, S. (1978) Interactive Forecasting Univariate and Multivariate Methods. Holden Day Inc., San Francisco.
  • Akbaş Y. Büyüme eğrisi modellerinin karşılaştırılması. Hayvansal Üretim, 1995; 36, 73-81.
  • Svetunkov I. (2017), Statistical Models underlying functions of ‘smooth’ package for R. Working Paper, Lancaster University.
  • Svetunkov I, Kourentzes N. (2015), Complex Exponential Smoothing. MPRA Paper no:69394, Lancaster University.
  • Kourentzesa N, Petropoulos F. Forecasting with R. International Symposium on Forecasting 2016 (ISF2016).
  • Bergmeir C, Hyndman RJ, Benitez JM. (2014), Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation. Working Paper, Monash University.
  • https://tr.wikipedia.org/ (Erişim Tarihi: 10 Nisan 2020)
  • https://ourworldindata.org/air-pollution (Erişim Tarihi: 5 Nisan 2020)
  • http://hdr.undp.org/en/content/2019-human-development-index-ranking (Erişim Tarihi: 4 Nisan 2020)
  • https://ourworldindata.org/grapher/hospital-beds-per-1000-people (Erişim Tarihi: 4 Nisan 2020)
  • https://ourworldindata.org/grapher/nurses-and-midwives-per-1000-people (Erişim Tarihi: 4 Nisan 2020)
  • https://systems.jhu.edu/ (Erişim Tarihi: 8 Nisan 2020)
  • https://covid19.saglik.gov.tr/ (Erişim Tarihi: 10 Nisan 2020)
  • https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (Erişim Tarihi: 8 Nisan 2020)
  • https://www.cdc.gov/coronavirus/2019-ncov/index.html (Erişim Tarihi: 9 Nisan 2020)
  • http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml (Erişim Tarihi: 5 Nisan 2020)
  • https://coronavirus.1point3acres.com/ (Erişim Tarihi: 9 Nisan 2020)
  • https://www.worldometers.info/coronavirus/ (Erişim Tarihi: 10 Nisan 2020)
  • https://bnonews.com/index.php/2020/04/the-latest-coronavirus-cases/ (Erişim Tarihi: 11 Nisan 2020)
  • https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 (Erişim Tarihi: 12 Nisan 2020)
  • www.who.int (Erişim Tarihi: 10 Nisan 2020)

Details

Primary Language Turkish
Subjects Health Care Sciences and Services
Journal Section ORIGINAL ARTICLE
Authors

Barış ERGÜL (Primary Author)
ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ, FEN-EDEBİYAT FAKÜLTESİ, İSTATİSTİK BÖLÜMÜ
0000-0002-1811-5143
Türkiye


Arzu ALTİN YAVUZ
ESKISEHIR OSMANGAZI UNIVERSITY, FACULTY OF SCIENCE AND LETTERS, DEPARTMENT OF STATISTICS
0000-0002-3277-740X
Türkiye


Ebru GÜNDOĞAN AŞIK
Karadeniz Technical University
0000-0002-9910-6555
Türkiye


Bahadır KALAY
ESKISEHIR OSMANGAZI UNIVERSITY
0000-0003-4473-1529
Türkiye

Publication Date March 20, 2020
Published in Issue Year 2020, Volume 25, Issue Special Issue on COVID 19

Cite

Bibtex @research article { anadoluklin719629, journal = {Anatolian Clinic the Journal of Medical Sciences}, issn = {2149-5254}, eissn = {2458-8849}, address = {}, publisher = {Hayat Sağlık ve Sosyal Hizmetler Vakfı}, year = {2020}, volume = {25}, pages = {130 - 141}, doi = {10.21673/anadoluklin.719629}, title = {Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi}, key = {cite}, author = {Ergül, Barış and Altin Yavuz, Arzu and Gündoğan Aşık, Ebru and Kalay, Bahadır} }
APA Ergül, B. , Altin Yavuz, A. , Gündoğan Aşık, E. & Kalay, B. (2020). Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi . Anatolian Clinic the Journal of Medical Sciences , Anatolian Clinic the Journal of Medical Science (Special Issue on COVID 19) , 130-141 . DOI: 10.21673/anadoluklin.719629
MLA Ergül, B. , Altin Yavuz, A. , Gündoğan Aşık, E. , Kalay, B. "Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi" . Anatolian Clinic the Journal of Medical Sciences 25 (2020 ): 130-141 <https://dergipark.org.tr/en/pub/anadoluklin/issue/53241/719629>
Chicago Ergül, B. , Altin Yavuz, A. , Gündoğan Aşık, E. , Kalay, B. "Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi". Anatolian Clinic the Journal of Medical Sciences 25 (2020 ): 130-141
RIS TY - JOUR T1 - Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi AU - Barış Ergül , Arzu Altin Yavuz , Ebru Gündoğan Aşık , Bahadır Kalay Y1 - 2020 PY - 2020 N1 - doi: 10.21673/anadoluklin.719629 DO - 10.21673/anadoluklin.719629 T2 - Anatolian Clinic the Journal of Medical Sciences JF - Journal JO - JOR SP - 130 EP - 141 VL - 25 IS - Special Issue on COVID 19 SN - 2149-5254-2458-8849 M3 - doi: 10.21673/anadoluklin.719629 UR - https://doi.org/10.21673/anadoluklin.719629 Y2 - 2020 ER -
EndNote %0 Anatolian Clinic the Journal of Medical Sciences Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi %A Barış Ergül , Arzu Altin Yavuz , Ebru Gündoğan Aşık , Bahadır Kalay %T Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi %D 2020 %J Anatolian Clinic the Journal of Medical Sciences %P 2149-5254-2458-8849 %V 25 %N Special Issue on COVID 19 %R doi: 10.21673/anadoluklin.719629 %U 10.21673/anadoluklin.719629
ISNAD Ergül, Barış , Altin Yavuz, Arzu , Gündoğan Aşık, Ebru , Kalay, Bahadır . "Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi". Anatolian Clinic the Journal of Medical Sciences 25 / Special Issue on COVID 19 (March 2020): 130-141 . https://doi.org/10.21673/anadoluklin.719629
AMA Ergül B. , Altin Yavuz A. , Gündoğan Aşık E. , Kalay B. Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi. Anatolian Clin. 2020; 25(Special Issue on COVID 19): 130-141.
Vancouver Ergül B. , Altin Yavuz A. , Gündoğan Aşık E. , Kalay B. Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi. Anatolian Clinic the Journal of Medical Sciences. 2020; 25(Special Issue on COVID 19): 130-141.
IEEE B. Ergül , A. Altin Yavuz , E. Gündoğan Aşık and B. Kalay , "Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi", Anatolian Clinic the Journal of Medical Sciences, vol. 25, no. Special Issue on COVID 19, pp. 130-141, Mar. 2020, doi:10.21673/anadoluklin.719629

13151 This Journal licensed under a CC BY-NC (Creative Commons Attribution-NonCommercial 4.0) International License.