Prediction of COVID-19 Pandemic Before The Latest Restrictions in Turkey by Using SIR Model

Year 2021, , 77 - 85, 27.05.2021

Efehan Ulaş

https://doi.org/10.29233/sdufeffd.852222

Cited By: 4

Abstract

The ongoing CoVID-19 pandemic affected our lives dramatically. Many epidemiological models are developed by scientists to estimate the number of infected individuals and the transmission rate of the CoVID-19 pandemic. In this paper, we analyze the evolution of COVID-19 in Turkey over the period November 16 and December 9, 2020, using the SIR model. The estimation of the reproduction number is found as 1.38. The peak day of the pandemic based on the period used in the SIR model is estimated as the 13th of January. By that date, around a total number of 3530000 individuals would be affected according to the SIR model and among them, approximately 141000 people would be active cases. In total, approximately 35000 people would die, based on a mortality rate of 1%. These predictions are made according to the scenario, which assumes, the latest restrictions weren't announced by the Turkish Ministry of Health. The findings of this study can be used to understand the characteristics of the pandemic at a certain time and estimate the distribution of the disease but are not suggested for any policy change and strategies.

Keywords

SIR model, Covid-19, Pandemic, Epidemic prediction model

Türkiye'deki Son Kısıtlamalardan Önce COVID-19 Pandemisi’nin SIR Modeli Kullanılarak Tahmin Edilmesi

Abstract

CoVID-19 salgını hayatımızı dramatik bir şekilde etkilemeye devam etmektedir. Birçok epidemiyolojik model, günlük vaka sayısını ve CoVID-19 pandemisi’nin bulaşma oranını tahmin etmek için bilim adamları tarafından geliştirilmiştir. Bu makalede, COVID-19 pandemisi’nin Türkiye'deki, 16 Kasım - 9 Aralık 2020 dönemindeki gelişimini baz alan SIR modeli kullanılarak pandemi analiz edilmiştir. Çalışmada üreme hızı 1.38 olarak bulunmuştur. SIR modelinde kullanılan döneme göre toplam vaka sayısının pik yapacağı tarih 13 Ocak 2020 olarak tahmin edilmektedir. O tarihe kadar, SIR modeline göre yaklaşık 3530000 kişi etkilenecek ve bunların arasında yaklaşık 141000’ini aktif vaka olacaktır. Toplamda, %1'lik bir ölüm oranı baz alındığında, yaklaşık olarak 35000 kişi vefat edecektir. Bu tahminler, son kısıtlamaların Türkiye Cumhuriyeti Sağlık Bakanlığı tarafından açıklanmadığını varsayan senaryoya göre yapılmıştır. Bu çalışmanın bulguları, belirli bir zamanda pandeminin özelliklerini anlamak ve hastalığın dağılımını tahmin etmek için kullanılabilir ancak herhangi bir politika değişikliği ve strateji için önerilmemektedir.

Keywords

SIR model, Covid-19, Pandemi, Salgın tahmin modeli

References

• K. D. Patterson and G. F. Pyle, “The geography and mortality of the 1918 influenza pandemic,” Bull. Hist. Med., vol. 65, no. 1, pp. 4–21, Spring 1991.
• World Health Organization, “Coronavirus disease 2019 (‎COVID-19)‎: situation report, 82,” 2020.
• Y.-C. Chen, P.-E. Lu, C.-S. Chang, and T.-H. Liu, “A time-dependent SIR model for COVID-19 with undetectable infected persons,” arXiv [q-bio.PE], 2020.
• C. Qi, D. Karlsson, K. Sallmen, and R. Wyss, “Model studies on the COVID-19 pandemic in Sweden,” arXiv [q-bio.PE], 2020.
• R. Ranjan, “Predictions for COVID-19 Outbreak in India using epidemiological models,” bioRxiv, 2020.
• C. Çakmaklı, S. Demiralp, Ṣebnem Kalemli-Özcan, S. Yesiltas, and M. Yildirim, “COVID-19 and emerging markets: An epidemiological model with international production networks and capital flows,” National Bureau of Economic Research, Cambridge, MA, 2020.
• M. Özdi̇nç, K. Şenel, S. Öztürkcan, and A. Akgül, “Predicting the progress of COVID-19: The case for turkey,” Turk. Klin. J. Med. Sci., vol. 40, no. 2, pp. 117–119, 2020.
• S. V. Scarpino and G. Petri, “On the predictability of infectious disease outbreaks,” Nat. Commun., vol. 10, no. 1, p. 898, 2019.
• M. Chinazzi et al., “The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak,” Science, vol. 368, no. 6489, pp. 395–400, 2020.
• A. J. Kucharski et al., “Early dynamics of transmission and control of COVID-19: a mathematical modelling study,” Lancet Infect. Dis., vol. 20, no. 5, pp. 553–558, 2020.
• D. Fanelli and F. Piazza, “Analysis and forecast of COVID-19 spreading in China, Italy and France,” Chaos Solitons Fractals, vol. 134, no. 109761, p. 109761, 2020.
• L. Xue et al., “A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy,” Math. Biosci., vol. 326, no. 108391, p. 108391, 2020.
• I. Cooper, A. Mondal, and C. G. Antonopoulos, “A SIR model assumption for the spread of COVID-19 in different communities,” Chaos Solitons Fractals, vol. 139, no. 110057, p. 110057, 2020.
• Q. Li, B. Tang, N. L. Bragazzi, Y. Xiao, and J. Wu, “Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability,” Math. Biosci., vol. 325, p. 108378, 2020.
• H. W. Hethcote, “The basic epidemiology models: Models, expressions for r0, parameter estimation, and applications,” in Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore, WORLD SCIENTIFIC, 2008, pp. 1–61.
• H. (howie) Weiss, “The SIR model and the Foundations of Public Health,” Materials matemàtics, pp. 0001–0017, 2013.
• F. Ndaïrou, I. Area, J. J. Nieto, and D. F. M. Torres, “Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan,” Chaos Solitons Fractals, vol. 135, no. 109846, p. 109846, 2020.
• B. D. Ripley, “The R project in statistical computing,” MSOR connect., vol. 1, no. 1, pp. 23–25, 2001.
• H. Wickham, “ggplot2: Ggplot2,” Wiley Interdiscip. Rev. Comput. Stat., vol. 3, no. 2, pp. 180–185, 2011.
• K. Soetaert, T. Petzoldt, and R. W. Setzer, “Solving Differential Equations inR: PackagedeSolve,” J. Stat. Softw., vol. 33, no. 9, pp. 1–25, 2010.