Research article Special Issues

Analysis of Covid 19 disease with SIR model and Taylor matrix method

  • Received: 11 November 2021 Revised: 16 March 2022 Accepted: 23 March 2022 Published: 11 April 2022
  • MSC : 34A34, 65H10, 92D25, 92D30

  • Covid 19 emerged in Wuhan, China in December 2019 has continued to spread by affecting the whole world. The pandemic has affected over 328 million people with more than 5 million deaths in over 200 countries which has severely disrupted the healthcare system and halted economies of the countries. The aim of this study is to discuss the numerical solution of the SIR model on the spread of Covid 19 by the Taylor matrix and collocation method for Turkey. Predicting COVID-19 through appropriate models can help us to understand the potential spread in the population so that appropriate action can be taken to prevent further transmission and prepare health systems for medical management of the disease. We deal with Susceptible–Infected–Recovered (SIR) model. One of the proposed model's improvements is to reflect the societal feedback on the disease and confinement features. We obtain the time dependent rate of transmission of the disease from susceptible $ \beta(t) $ and the rate of recovery from infectious to recovered $ \gamma $ using Turkey epidemic data. We apply the Taylor matrix and collocation method to the SIR model with $ \gamma $, $ \beta(t) $ and Covid 19 data of Turkey from the date of the first case March 11, 2020 through July 3, 2021. Using this method, we focus on the evolution of the Covid 19 in Turkey. We also show the estimates with the help of graphics and Maple.

    Citation: Deniz UÇAR, Elçin ÇELİK. Analysis of Covid 19 disease with SIR model and Taylor matrix method[J]. AIMS Mathematics, 2022, 7(6): 11188-11200. doi: 10.3934/math.2022626

    Related Papers:

  • Covid 19 emerged in Wuhan, China in December 2019 has continued to spread by affecting the whole world. The pandemic has affected over 328 million people with more than 5 million deaths in over 200 countries which has severely disrupted the healthcare system and halted economies of the countries. The aim of this study is to discuss the numerical solution of the SIR model on the spread of Covid 19 by the Taylor matrix and collocation method for Turkey. Predicting COVID-19 through appropriate models can help us to understand the potential spread in the population so that appropriate action can be taken to prevent further transmission and prepare health systems for medical management of the disease. We deal with Susceptible–Infected–Recovered (SIR) model. One of the proposed model's improvements is to reflect the societal feedback on the disease and confinement features. We obtain the time dependent rate of transmission of the disease from susceptible $ \beta(t) $ and the rate of recovery from infectious to recovered $ \gamma $ using Turkey epidemic data. We apply the Taylor matrix and collocation method to the SIR model with $ \gamma $, $ \beta(t) $ and Covid 19 data of Turkey from the date of the first case March 11, 2020 through July 3, 2021. Using this method, we focus on the evolution of the Covid 19 in Turkey. We also show the estimates with the help of graphics and Maple.



    加载中


    [1] J. M. Last, A dictionary of epidemiology, Oxford University Press, 1988.
    [2] W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A, 115 (1927), 700–721. http://dx.doi.org/10.1098/rspa.1927.0118 doi: 10.1098/rspa.1927.0118
    [3] W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics part Ⅱ–the problem of endemicity, Proc. R. Soc. Lond. A, 138 (1932), 55–83. http://dx.doi.org/10.1098/rspa.1932.0171 doi: 10.1098/rspa.1932.0171
    [4] W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics part Ⅲ, further studies of the problem of endemicity, Proc. R. Soc. Lond. A, 141 (1933), 94–122. http://dx.doi.org/10.1098/rspa.1933.0106 doi: 10.1098/rspa.1933.0106
    [5] F. C. Hoppensteadt, Mathematical methods of population biology, London: Cambridge University Press, 1982. http://dx.doi.org/10.1017/CBO9780511624087
    [6] B. Shulgin, L. Stone, Z. Agur, Pulse vaccination strategy in the SIR epidemic model, Bull. Math. Biol., 60 (1998), 1123–1148. http://dx.doi.org/10.1006/S0092-8240(98)90005-2 doi: 10.1006/S0092-8240(98)90005-2
    [7] M. C. Schuette, H. W. Hethcote, Modeling of the effects of varicella vaccination programs on the incidence of chickenpox and shingles, Bull. Math. Biol., 61 (1999), 1031–1064. http://dx.doi.org/10.1006/bulm.1999.0126 doi: 10.1006/bulm.1999.0126
    [8] D. J. Dalej, J. Gani, Epidemic modelling, London: Cambridge University Press, 1999. http://dx.doi.org/10.1017/CBO9780511608834
    [9] M. Iannelli, The mathematical modeling of epidemics, Summer school on mathematical models in life science: theory and simulation, University of Trento, 2005.
    [10] M. Rafei, D. Ganji, H. Daniali, Solution of the epidemic model by homotopy perturbation method, Appl. Math. Comput., 187 (2007), 1056–1062. http://dx.doi.org/10.1016/j.amc.2006.09.019 doi: 10.1016/j.amc.2006.09.019
    [11] S. Ahmetolan, A. H. Bilge, A. Demirci, A. Peker-Dobie, O. Ergonul, What can we estimate from fatality and infectious casa data using the Susceptible-Infected-Removed (SIR) model? A case study of Covid-19 pandemic, Front. Med., 7 (2020), 556366. http://dx.doi.org/10.3389/fmed.2020.556366 doi: 10.3389/fmed.2020.556366
    [12] U. Nguemdjo, F. Meno, A. Dongfack, B. Ventelou, Simulating the progression of the COVID-19 disease in Cameroon using SIR models, PLoS ONE, 15 (2020), e0237832. http://dx.doi.org/10.1371/journal.pone.0237832 doi: 10.1371/journal.pone.0237832
    [13] E. B. Postnikov, Estimation of COVID-19 dynamics "On a back-of-envelope": Does the simplest SIR model provide quantitative parameters and predictions?, Chaos Soliton. Fract., 135 (2020), 109841. http://dx.doi.org/10.1016/j.chaos.2020.109841 doi: 10.1016/j.chaos.2020.109841
    [14] N. R. Record, A. Pershing, A note on the effects of epidemic forecasts on epidemic dynamics, PeerJ, 8 (2020), e9649. http://dx.doi.org/10.7717/peerj.9649 doi: 10.7717/peerj.9649
    [15] N. S. Barlow, S. J. Weinstein, Accurate closed-form solution of the SIR epidemic model, Physica D, 408 (2020), 132540. http://dx.doi.org/10.1016/j.physd.2020.132540 doi: 10.1016/j.physd.2020.132540
    [16] N. A. Kudryashov, M. A. Chmykhov, M. Vigdorowitsch, Analytical features of the SIR model and their applications to COVID-19, Appl. Math. Model., 90 (2021), 466–473. http://dx.doi.org/10.1016/j.apm.2020.08.057 doi: 10.1016/j.apm.2020.08.057
    [17] N. A. Kudryashov, M. A. Chmykhov, M. Vigdorowitsch, An estimative (warning) model for recognition of pandemic nature of virus infections, Int. J. Nonlinear Sci. Numer. Simul., 2021, in press. http://dx.doi.org/10.1515/ijnsns-2020-0154
    [18] F. Shakeri, M. Dehghan, Solution of delay differential equations via a homotopy perturbation method, Math. Comput. Model., 48 (2008), 486–498. http://dx.doi.org/10.1016/j.mcm.2007.09.016 doi: 10.1016/j.mcm.2007.09.016
    [19] D. J. Evans, K. R. Raslan, The Adomian decomposition method for solving delay differential equation, Int. J. Comput. Math., 82 (2005), 49–54. http://dx.doi.org/10.1080/00207160412331286815 doi: 10.1080/00207160412331286815
    [20] F. Shakeri, M. Dehghan, Application of the decomposition method of adomian for solving the pantograph equation of order m, Zeitschrift für Naturforschung A, 65 (2010), 453–460. http://dx.doi.org/10.1515/zna-2010-0510 doi: 10.1515/zna-2010-0510
    [21] A. Saadatmandi, M. Dehghan, Variational iterion method for solving a generalized pantograph equation, Comput. Math. Appl., 58 (2009), 2190–2196. http://dx.doi.org/10.1016/j.camwa.2009.03.017 doi: 10.1016/j.camwa.2009.03.017
    [22] F. Shakeri, M. Dehghan, Solution of a model describing biological species living together using the variational iteration method, Math. Comput. Model., 48 (2008), 685–699. http://dx.doi.org/10.1016/j.mcm.2007.11.012 doi: 10.1016/j.mcm.2007.11.012
    [23] F. S. Akinboro, Numerical solution of SIR model using differential transformation method and variational iteration method, Gen. Math. Notes, 22 (2014), 82–92.
    [24] M. Kröger, R. Schlickeiser, Analytical solution of the SIR-model for the temporal evolution of epidemics. Part A: time-independent reproduction factor, J. Phys. A: Math. Theor., 53 (2020), 505601. http://dx.doi.org/10.1088/1751-8121/abc65d doi: 10.1088/1751-8121/abc65d
    [25] R. Schlickeiser, M. Kröger, Analytical solution of the SIR-model for the temporal evolution of epidemics: part B. Semi-time case, J. Phys. A: Math. Theor., 54 (2021), 175601. http://dx.doi.org/10.1088/1751-8121/abed66 doi: 10.1088/1751-8121/abed66
    [26] M. Sezer, A. Karamete, M. Gülsu, Taylor polynomial solutions of systems of linear differential equations with variable coefficients, Int. J. Comput. Math., 82 (2005), 755–764. http://dx.doi.org/10.1080/00207160512331323336 doi: 10.1080/00207160512331323336
    [27] M. Sezer, A method for the approximate solution of the second‐order linear differential equations in terms of Taylor polynomials, Int. J. Math. Educ. Sci., 27 (1996), 821–834. http://dx.doi.org/10.1080/0020739960270606 doi: 10.1080/0020739960270606
    [28] A. S. Alshomrani, M. Z. Ullah, D. Baleanu, Caputo SIR model for COVID-19 under optimized fractional order, Adv. Differ. Equ., 2021 (2021), 185. http://dx.doi.org/10.1186/s13662-021-03345-5 doi: 10.1186/s13662-021-03345-5
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2971) PDF downloads(292) Cited by(0)

Article outline

Figures and Tables

Figures(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog