An investigation on the dynamics of a stochastic SEIR epidemic model with psychological effects and Ornstein-Uhlenbeck perturbations

Yongle Ma , Hongxia Liu , Wencai Zhao

Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (8) : 5813 -5841.

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Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (8) :5813 -5841. DOI: 10.3934/era.2026258
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An investigation on the dynamics of a stochastic SEIR epidemic model with psychological effects and Ornstein-Uhlenbeck perturbations
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Abstract

This work proposed a stochastic SEIR epidemic model incorporating psychological effects coupled with an Ornstein-Uhlenbeck (OU) process. The model focuses on the fact that the incidence rate of diseases is disturbed by environmental noise, and it is characterized by a mean-reverting OU process. Unlike traditional white noise, OU noise is consistent with the continuity and stability characteristics of real-world environmental disturbances. First, the existence and stability of equilibria for the corresponding deterministic model were discussed, laying the foundation for the subsequent dynamical analysis. Second, utilizing Itô’s formula, the existence and uniqueness of a globally nonnegative solution for the stochastic model were proven, and the sufficient condition for disease extinction was discussed. Subsequently, by constructing appropriate Lyapunov functions and employing ergodic theory, the sufficient condition for the existence of a stationary distribution was established, namely,Rs 0 > 1, which characterizes the stochastic persistence of the disease. Furthermore, by linearizing the stochastic system around the endemic equilibrium and solving the resulting covariance matrix equation, we derived an explicit expression for the probability density function characterizing the stationary distribution in the vicinity of the endemic equilibrium. Finally, we employed numerical simulations to validate the theoretical findings and to investigate the influence of fluctuation intensity on the system’s dynamics.

Keywords

SEIR model / Ornstein-Uhlenbeck process / extinction / stationary distribution / probability density function

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Yongle Ma, Hongxia Liu, Wencai Zhao. An investigation on the dynamics of a stochastic SEIR epidemic model with psychological effects and Ornstein-Uhlenbeck perturbations. Electronic Research Archive, 2026, 34 (8) : 5813-5841 DOI:10.3934/era.2026258

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References

[1]

W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. London, Ser. A, 115 (1927), 700-721. https://doi.org/10.1098/rspa.1927.0118

[2]

R. M. Anderson, R. M. May, Population Biology of Infectious Diseases , Springer-Verlag, Berlin, Heidelberg; New York, 1982. https://doi.org/10.1007/978-3-642-68635-1

[3]

R. M. Anderson, R. M. May, Infectious Diseases of Humans: Dynamics and Control , Oxford University Press, Oxford, 1991. https://doi.org/10.1093/oso/9780198545996.001.0001

[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. Xiao, S. Ruan, Global analysis of an epidemic model with nonmonotone incidence rate, Math. Biosci., 208 (2007), 419-429. https://doi.org/10.1016/j.mbs.2006.09.025

[6]

Y. Liu, Y. Wang, D. Jiang, Dynamic behaviors of a stochastic virus infection model with Beddington-DeAngelis incidence function, eclipse-stage and Ornstein-Uhlenbeck process, Math. Biosci., 369 (2024), 109154. https://doi.org/10.1016/j.mbios.2024.109154

[7]

M. S. Bartlett, Deterministic and stochastic models for recurrent epidemics, in Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 4 (1956), 81-109. https://doi.org/10.1525/9780520350717-007

[8]

L. J. S. Allen, An Introduction to Stochastic Processes with Applications to Biology , 2nd edition, CRC Press, Boca Raton, 2010. https://doi.org/10.1201/b12537

[9]

R. Kuske, L. F. Gordillo, P. Greenwood, Sustained oscillations via coherence resonance in SIR, J. Theor. Biol., 245 (2007), 459-469. https://doi.org/10.1016/j.jtbi.2006.10.029

[10]

Y. Cai, J. Jiao, Z. Gui, Y. Liu, W. Wang, Environmental variability in a stochastic epidemic model, Appl. Math. Comput., 329 (2018), 210-226. https://doi.org/10.1016/j.amc.2018.02.009

[11]

G. E. Uhlenbeck, L. S. Ornstein, On the theory of the Brownian motion, Phys. Rev., 36 (1930), 823-841. https://doi.org/10.1103/PhysRev.36.823

[12]

E. Allen, Environmental variability and mean-reverting processes, Discrete Contin. Dyn. Syst. -Ser. B, 21 (2016), 2073-2089. https://doi.org/10.3934/dcdsb.2016037

[13]

K. Mamis, M. Farazmand, Modeling correlated uncertainties in stochastic compartmental models, Math. Biosci., 374 (2024), 109226. https://doi.org/10.1016/j.mbs.2024.109226

[14]

M. M. Gao, D. Q. Jiang, J. Y. Ding, Dynamical behavior of a nutrient-plankton model with Ornstein-Uhlenbeck process and nutrient recycling, Chaos Soliton. Fract., 174 (2023), 113763. https://doi.org/10.1016/j.chaos.2023.113763

[15]

X. Mu, D. Jiang, T. Hayat, A. Alsaedi, Y. Liao, A stochastic turbidostat model with Ornstein-Uhlenbeck process: dynamics analysis and numerical simulations, Nonlinear Dyn., 107 (2022), 2805-2817. https://doi.org/10.1007/s11071-021-07093-9

[16]

W. Wang, X. Wang, X. Fan, On the global attractivity of a diffusive viral infection model with spatial heterogeneity, Math. Methods Appl. Sci., 48 (2025), 15656-15660. https://doi.org/10.1002/mma.70040

[17]

K. Mamis, M. Farazmand, Stochastic compartmental models of the COVID-19 pandemic must have temporally correlated uncertainties, Proc. R. Soc. A, 479 (2023), 20220568. https://doi.org/10.1098/rspa.2022.0568

[18]

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

[19]

W. Wang, G. Wu, X. Fan, Global dynamics of a novel viral infection model mediated by pattern recognition receptors, Appl. Math. Lett., 173 (2026), 109757. https://doi.org/10.1016/j.aml.2025.109757

[20]

Y. Q. Song, X. H. Zhang, Stationary distribution and extinction of a stochastic SVEIS epidemic model incorporating Ornstein-Uhlenbeck process, Appl. Math. Lett., 133 (2022), 108284. https://doi.org/10.1016/j.aml.2022.108284

[21]

T. Su, Q. Yang, X. H. Zhang, D. Q. Jiang, Stationary distribution, extinction and probability density function of a stochastic SEIV epidemic model with general incidence and Ornstein-Uhlenbeck process, Physica A, 615 (2023), 128605. https://doi.org/10.1016/j.physa.2023.128605

[22]

A. Gray, D. Greenhalgh, L. Hu, X. R. Mao, J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876-902. https://doi.org/10.1137/10081856X

[23]

Y. Lin, D. Jiang, P. Xia, Long-time behavior of a stochastic SIR model, Appl. Math. Comput., 236 (2014), 1-9. https://doi.org/10.1016/j.amc.2014.03.035

[24]

X. Zhang, Y. Ruan, A stochastic chemostat model with mean-reverting Ornstein-Uhlenbeck process and Monod-Haldane response function, Appl. Math. Comput., 394 (2021), 125833. https://doi.org/10.1016/j.amc.2020.125833

[25]

N. H. Du, D. H. Nguyen, G. Yin, Conditions for permanence and ergodicity of certain stochastic predator-prey models, J. Appl. Probab., 53 (2016), 187-202. https://doi.org/10.1017/jpr.2015.18

[26]

R. Khasminskii, Stochastic Stability of Differential Equations , 2012. https://doi.org/10.1007/978-3-642-23280-0

[27]

Z. F. Shi, D. Q. Jiang, X. H. Zhang, A. Alsaedi, A stochastic SEIRS rabies model with population dispersal stationary distribution and probability density function, Appl. Math. Comput., 427 (2022), 127189. https://doi.org/10.1016/j.amc.2022.127189

[28]

C. W. Gardiner, Stochastic Methods: A Handbook for the Natural and Social Sciences , Springer, Berlin, 2009.

[29]

B. Zhou, D. Liang, Y. Dai, T. Hayat, A. Alsaedi, Stationary distribution and probability density function of a stochastic SVIS epidemic model with standard incidence and vaccination strategies, Chaos, Solitons Fractals, 143 (2021), 110601. https://doi.org/10.1016/j.chaos.2020.110601

[30]

H. Roozen, An asymptotic solution to a two-dimensional exit problem arising in population dynamics, SIAM J. Appl. Math., 49 (1989), 1793-1810. https://doi.org/10.1137/0149110

[31]

B. Q. Zhou, D. Q. Jiang, Y. C. Dai, T. Hayat, Stationary distribution and density function expression for a stochastic SIQRS epidemic model with temporary immunity, Nonlinear Dyn., 105 (2021), 931-955. https://doi.org/10.1007/s11071-020-06151-y

[32]

D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Rev., 43 (2001), 525-546. https://doi.org/10.1137/S0036144500378302

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