Research Article
Türkiye’deki COVID-19 Enfeksiyonu: Erken Dönem İstatistikleri ve Hastalık Seyrinin İstatistiksel Olarak Modellenmesi
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.
Keywords
COVID-19 istatistiksel analiz hastalık seyrinin modellenmesi zaman serisi analizi büyüme eğrileri
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.
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- 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)
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COVID-19 Infection in Turkey: Statistical Modeling as the Early Perioed Statistics and Disease Course
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.
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.
Keywords
COVID-19 statistical analysis modelling of the disease course time series analysis growth curves
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)
There are 41 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | ORIGINAL ARTICLE |
| Authors | |
| Publication Date | March 20, 2020 |
| Acceptance Date | April 19, 2020 |
| Published in Issue | Year 2020 Issue: Special Issue on COVID 19 |
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