Bifurcation analysis of an SIS model with a modified nonlinear incidence rate

Jianzhi Cao , Xuhong Ji , Pengmiao Hao , Peiguang Wang

Electronic Research Archive ›› 2025, Vol. 33 ›› Issue (6) : 3901 -3930.

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Electronic Research Archive ›› 2025, Vol. 33 ›› Issue (6) :3901 -3930. DOI: 10.3934/era.2025173
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Bifurcation analysis of an SIS model with a modified nonlinear incidence rate
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Abstract

A modified nonlinear incidence rate in an SIS epidemic model was investigated. When a new disease emerged or an old one resurged, the infectivity was initially high. Subsequently, the psychological effect attenuated the infectivity. Eventually, due to the crowding effect, the infectivity reached a saturation state. The nonlinearity of the functional form of the infection incidence was modified to enhance its biological plausibility. The stability of the associated equilibria was examined, and the basic reproduction number and the critical value that governed the dynamics of the model were deduced. Bifurcation analyses were presented, encompassing backward bifurcation, saddle-node bifurcation, Bogdanov-Takens bifurcation of codimension two, and Hopf bifurcation. Numerical simulations were conducted to validate our findings.

Keywords

SIS model / stability / saddle-node bifurcation / Bogdanov-Takens bifurcation / Hopf bifurcation

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Jianzhi Cao, Xuhong Ji, Pengmiao Hao, Peiguang Wang. Bifurcation analysis of an SIS model with a modified nonlinear incidence rate. Electronic Research Archive, 2025, 33 (6) : 3901-3930 DOI:10.3934/era.2025173

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References

[1]

M. De la Sen, R. Nistal, S. Alonso-Quesada, A. Ibeas, Some formal results on positivity, stability, and endemic steady-state attainability based on linear algebraic tools for a class of epidemic models with eventual incommensurate delays, Discrete Dyn. Nat. Soc., 2019 (2019), 8959681. https://doi.org/10.1155/2019/8959681

[2]

M. De la Sen, A. Ibeas, S. Alonso-Quesada, R. Nistal, On a new epidemic model with asymptomatic and dead-infective subpopulations with feedback controls useful for ebola disease, Discrete Dyn. Nat. Soc., 2017 (2017), 4232971. https://doi.org/10.1155/2017/4232971

[3]

W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics. II. - The problem of endemicity, Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci., 138 (1932), 55-83. https://doi.org/10.1098/rspa.1932.0171

[4]

V. Capasso, G. Serio, A generalization of the Kermack-Mckendrick deterministic epidemic model, Math. Biosci., 42 (1978), 43-61. https://doi.org/10.1016/0025-5564(78)90006-8

[5]

D. M. Xiao, Y. G. Zhou, Qualitative analysis of an epidemic model, Can. Appl. Math. Q., 14 (2006), 469-492.

[6]

Y. G. Zhou, D. M. Xiao, Y. L. Li, Bifurcations of an epidemic model with non-monotonic incidence rate of saturated mass action, Chaos, Solitons Fractals, 32 (2007), 1903-1915. https://doi.org/10.1016/j.chaos.2006.01.002

[7]

M. Lu, J. C. Huang, S. G. Ruan, P. Yu, Bifurcation analysis of an SIRS epidemic model with a generalized nonmonotone and saturated incidence rate, J. Differ. Equations, 267 (2019), 1859-1898. https://doi.org/10.1016/j.jde.2019.03.005

[8]

P. van den Driessche, J. Watmough, A simple SIS epidemic model with a backward bifurcation, J. Math. Biol., 40 (2000), 525-540. https://doi.org/10.1007/s002850000032

[9]

M. H. Alharbi, Global investigation for an ”SIS” model for COVID-19 epidemic with asymptomatic infection, Math. Biosci. Eng., 20 (2023), 5298-5315. https://doi.org/10.3934/mbe.2023245

[10]

Z. Shuai, P van den Driessche, Global stability of infectious disease models using Lyapunov functions, SIAM J. Appl. Math., 73 (2013), 1513-1532. https://doi.org/10.1137/120876642

[11]

P van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29-48. https://doi.org/10.1016/S0025-5564(02)00108-6

[12]

Y. A. Kuznetsov, Numerical Analysis of Bifurcations , Cambridge University Press, 2004.

[13]

Z. Zhang, Qualitative Theory of Differential Equations , Science Press, 1992.

[14]

S. Wiggins, Introduction to Applied Nonlinear Dynamical Systems and Chaos , 2nd edition, Springer, 2003.

[15]

J. C. Huang, Y. J. Gong, S. G. Ruan, Bifurcation analysis in a predator-prey model with constant-yield predator harvesting, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 2101-2121. https://doi.org/10.3934/dcdsb.2013.18.2101

[16]

Y. L. Tang, D. Q. Huang, S. G. Ruan, W. N. Zhang, Coexistence of limit cycles and homoclinic loops in a SIRS model with a nonlinear incidence rate, SIAM J. Appl. Math., 69 (2008), 621-639. https://doi.org/10.1137/070700966

[17]

B. D. Hassard, N. D. Kazarinoff, Y. H. Wan, Theory and Applications of Hopf bifurcation , Cambridge University Press, 1981.

[18]

J. E. Marsden, M. McCracken, The Hopf Bifurcation and its Applications , Springer-Verlag, 2012.

[19]

R. I. Bogdanov, Versal deformations of a singular point of a vector field on the plane in the case of zero eigenvalues, Funct. Anal. Appl., 9 (1975), 144-145. https://doi.org/10.1007/BF01075453

[20]

F. Takens, Forced oscillations and bifurcations, in Global Analysis of Dynamical Systems, CRC Press, (2001), 11-71.

[21]

L. Perko, Differential Equations and Dynamical Systems , Springer Science & Business Media, 2013.

[22]

S. G. Ruan, W. D. Wang, Dynamical behavior of an epidemic model with a nonlinear incidence rate, J. Differ. Equations, 188 (2003), 135-163. https://doi.org/10.1016/S0022-0396(02)00089-X

[23]

C. Castillo-Chavez, B. J. Song, Dynamical models of tuberculosis and their applications, Math. Biosci. Eng., 1 (2004), 361-404. https://doi.org/10.3934/mbe.2004.1.361

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