An Accurate Analytical-Numerical Iterative Method for the Susceptible-Infected-Recovered Epidemic Models
DOI:
https://doi.org/10.31764/jtam.v5i2.3876Keywords:
Epidemic Problem, Infectious Disease, Numerical Solution, SIR Model, Vital Dynamics.Abstract
We consider Susceptible-Infected-Recovered (SIR) models of infectious disease spread without and with vital dynamics. We recall some existing analytical approximate iterative methods for solving these models. We observe that all these methods solve the models accurately only for points close to the initialisation. These methods produce inaccurate, and even, unrealistic solutions to the SIR models if the time domain is sufficiently large. In this paper, our research objective is to propose an analytical-numerical iterative method, which is able to solve the SIR models accurately on the whole domain. The research method used is quantitative mathematical modelling with simulation. By implementing this analytical-numerical iterative method into a finite number of small consecutive subintervals of the domain, our research results show that the proposed method produces accurate solutions to the SIR models on the whole domain.References
Alderremy, A. A., Chamekh, M., & Jeday, F. (2020). Semi-analytical solution for a system of competition with production a toxin in a chemostat. Journal of Mathematics and Computer Science, 20(2), 155–160. http://dx.doi.org/10.22436/jmcs.020.02.07
Alenezi, M. N., Al-Anzi, F. S., & Alabdulrazzaq, H. (2021). Building a sensible SIR estimation model for COVID-19 outspread in Kuwait. Alexandria Engineering Journal, 60(3), 3161–3175. https://doi.org/10.1016/j.aej.2021.01.025
Alqahtani, R. T. (2021). Mathematical model of SIR epidemic system (COVID-19) with fractional derivative: stability and numerical analysis. Advances in Difference Equations, 2021, 2. https://doi.org/10.1186/s13662-020-03192-w
Barlow, N. S., & Weinstein, S. J. (2020). Accurate closed-form solution of the SIR epidemic model. Physica D, 408, 132540. https://doi.org/10.1016/j.physd.2020.132540
Biazar, J. (2006). Solution of the epidemic model by Adomian decomposition method. Applied Mathematics and Computation, 173(2), 1101–1106. https://doi.org/10.1016/j.amc.2005.04.036
Biazar, J., & Ghazvini, H. (2007). He’s variational iteration method for solving linear and non-linear systems of ordinary differential equations. Applied Mathematics and Computation, 191(1), 287–297. https://doi.org/10.1016/j.amc.2007.02.153
Brauer, F., & Castillo-Chavez, C. (2012). Mathematical Models in Population Biology and Epidemiology (Second Edi.) Springer. https://doi.org/10.1007/978-1-4614-1686-9
Brauer, F., van den Driessche, P., & Wu, J. (2008). Mathematical Epidemiology. Springer. https://doi.org/10.1007/978-3-540-78911-6
Cadoni, M., & Gaeta, G. (2020). Size and timescale of epidemics in the SIR framework. Physica D, 411, 132626. https://doi.org/10.1016/j.physd.2020.132626
Daley, D. J., & Gani, J. (2001). Epidemic Modelling: An Introduction. Cambridge University Press. https://www.cambridge.org/id/academic/subjects/statistics-probability/applied-probability-and-stochastic-networks/epidemic-modelling-introduction?format=PB (Accessed on 28 April 2021)
Darvishi, M. T., Khani, F., & Soliman, A. A. (2007). The numerical simulation for stiff systems of ordinary differential equations. Computers and Mathematics with Applications, 54(7–8), 1055–1063. https://doi.org/10.1016/j.camwa.2006.12.072
De la Sen, M., Ibeas, A., & Agarwal, R. P. (2020). On confinement and quarantine concerns on an SEIAR epidemic model with simulated parameterizations for the COVID-19 pandemic. Symmetry, 12(10), 1646. https://doi.org/10.3390/sym12101646
Din, R. U., & Algehyne, E. A. (2021). Mathematical analysis of COVID-19 by using SIR model with convex incidence rate. Results in Physics, 23, 103970. https://doi.org/10.1016/j.rinp.2021.103970
Gatto, N. M., & Schellhorn, H. (2021). Optimal control of the SIR model in the presence of transmission and treatment uncertainty. Mathematical Biosciences, 333, 108539. https://doi.org/10.1016/j.mbs.2021.108539
Harko, T., Lobo, F. S. N., & Mak, M. K. (2014). Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates. Applied Mathematics and Computation, 236, 184–194. https://doi.org/10.1016/j.amc.2014.03.030
He, J. H. (2000). Variational iteration method for autonomous ordinary differential systems. Applied Mathematics and Computation, 114(2–3), 115–123. https://doi.org/10.1016/S0096-3003(99)00104-6
He, J. H. (1999). Variational iteration method – a kind of non-linear analytical technique: some examples. International Journal of Non-Linear Mechanics, 34(4), 699–708. https://doi.org/10.1016/S0020-7462(98)00048-1
Heng, K., & Althaus, C. L. (2020). The approximately universal shapes of epidemic curves in the Susceptible-Exposed-Infectious-Recovered (SEIR) model. Scientific Reports, 10, 19365. https://doi.org/10.1038/s41598-020-76563-8
Ifguis, O., El Ghozlani, M., Ammou, F., Moutcine, A., & Abdellah, Z. (2020). Simulation of the final size of the evolution curve of Coronavirus epidemic in Morocco using the SIR model. Journal of Environmental and Public Health, 2020, 9769267. https://doi.org/10.1155/2020/9769267
Jordan, D. W., & Smith, P. (2007). Nonlinear Ordinary Differential Equations (Fourth Edi.) Oxford University Press. https://global.oup.com/academic/product/nonlinear-ordinary-differential-equations-9780199208258?cc=us&lang=en& (Accessed on 28 April 2021)
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118
Kröger, M., & Schlickeiser, R. (2020). Analytical solution of the SIR-model for the temporal evolution of epidemics. Part A: time-independent reproduction factor. Journal of Physics A: Mathematical and Theoretical, 53(50), 505601. https://doi.org/10.1088/1751-8121/abc65d
Mungkasi, S. (2021). Variational Iteration and successive approximation methods for a SIR epidemic model with constant vaccination strategy. Applied Mathematical Modelling, 90, 1–10. https://doi.org/10.1016/j.apm.2020.08.058
Mungkasi, S. (2020a). Improved variational iteration solutions to the SIR model of dengue fever disease for the case of South Sulawesi. Journal of Mathematical and Fundamental Sciences, 52(3), 297–311. https://doi.org/10.5614/j.math.fund.sci.2020.52.3.4
Mungkasi, S. (2020b). Successive approximation, variational iteration, and multistage-analytical methods for a SEIR model of infectious disease involving vaccination strategy. Communication in Biomathematical Sciences, 3(2), 114–126. https://doi.org/10.5614/cbms.2020.3.2.3
Murray, J. D. (2002). Mathematical Biology: I. An Introduction (Third Edi.) Springer. https://doi.org/10.1007/b98868
Rafei, M., Daniali, H., & Ganji, D. D. (2007). Variational iteration method for solving the epidemic model and the prey and predator problem. Applied Mathematics and Computation, 186(2), 1701–1709. https://doi.org/10.1016/j.amc.2006.08.077
Rahimi, I., Gandomi, A. H., Asteris, P. G., & Chen, F. (2021). Analysis and prediction of COVID-19 using SIR, SEIQR, and machine learning models: Australia, Italy, and UK cases. Information, 12(3), 109. https://doi.org/10.3390/info12030109
Rangkuti, Y. M., Novalia, E., Marhaini, S., & Humairah, S. (2016). Variational iteration method with Gauss-Seidel technique for solving avian human influenza epidemic model. Bulletin of Mathematics, 8(1), 29–41. https://talenta.usu.ac.id/bullmath/article/view/12 (Accessed on 28 April 2021)
Salkuyeh, D. K. (2008). Convergence of the variational iteration method for solving linear systems of ODEs with constant coefficients. Computers and Mathematics with Applications, 56(8), 2027–2033. https://doi.org/10.1016/j.camwa.2008.03.030
Tatari, M., & Dehghan, M. (2009). Improvement of He’s variational iteration method for solving systems of differential equations. Computers and Mathematics with Applications, 58(11–12), 2160–2166. https://doi.org/10.1016/j.camwa.2009.03.081
Telles, C. R., Lopes, H., & Franco, D. (2021). SARS-COV-2: SIR model limitations and predictive constraints. Symmetry, 13(4), 676. https://doi.org/10.3390/sym13040676
Turkyilmazoglu, M. (2021). Explicit formulae for the peak time of an epidemic from the SIR model. Physica D: Nonlinear Phenomena, 422, 132902. https://doi.org/10.1016/j.physd.2021.132902
Ucakan, Y., Gulen, S., & Koklu, K. (2021). Analysing of tuberculosis in Turkey through SIR, SEIR and BSEIR mathematical models. Mathematical and Computer Modelling of Dynamical Systems, 27(1), 179–202. https://doi.org/10.1080/13873954.2021.1881560
Wu, S.-L., Chen, L., & Hsu, C.-H. (2021). Traveling wave solutions for a diffusive age-structured SIR epidemic model. Communications in Nonlinear Science and Numerical Simulation, 98, 105769. https://doi.org/10.1016/j.cnsns.2021.105769
Youssef, I. K., & El-Arabawy, H. A. (2007). Picard iteration algorithm combined with Gauss-Seidel technique for initial value problems. Applied Mathematics and Computation, 190(1), 345–355. https://doi.org/10.1016/j.amc.2007.01.058
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