Ir al contenido

Documat


Analysis of Stochastic SIRC Model with Cross Immunity Based on Ornstein–Uhlenbeck Process

  • Zhiming Ni [1] ; Daqing Jiang [1] ; Zhongwei Cao [2] ; Xiaojie Mu [1]
    1. [1] China University of Petroleum

      China University of Petroleum

      China

    2. [2] Jilin University of Finance and Economics

      Jilin University of Finance and Economics

      China

  • Localización: Qualitative theory of dynamical systems, ISSN 1575-5460, Vol. 22, Nº 3, 2023
  • Idioma: inglés
  • Enlaces
  • Resumen
    • In this paper, we analyze a stochastic SIRC model with Ornstein–Uhlenbeck process.

      Firstly, we give the existence and uniqueness of global solution of stochastic SIRC model and prove it. In addition, the existence of ergodic stationary distributions for stochastic SIRC system is proved by constructing a suitable series of Lyapunov functions. A quasi-endemic equilibrium related to endemic equilibrium of deterministic systems is defined by considering randomness. And we obtain the probability density function of the linearized system near the equilibrium point. After the proof of probability density function, the sufficient condition of disease extinction is given and proved. We prove the theoretical results in the paper by numerical simulation at the end of the paper.

  • Referencias bibliográficas
    • 1. Organization WH, et al.. Coronavirus disease (COVID-2019). Situation reports, 73 (2020)
    • 2. Mamo, D.K.: Model the transmission dynamics of COVID-19 propagation with public health intervention. Results Appl. Math. 7, 100123 (2020)
    • 3. Kermark, W.O., Mckendrick, A.G.: Contributions to the mathematical theory of epidemics. Bull. Math. Biol. 53, 33–55 (1991)
    • 4. Farood, J., Bazaz, M.A.: A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India. Alex....
    • 5. Neufeld, Z., Khataee, H., Czirok, A.: Targeted adaptive isolation strategy for COVID-19 pandemic. Infect. Dis. Model. 5, 357–361 (2020)
    • 6. Casagrandi, R., Bolzoni, L., Levin, S., Andreasen, V.: The SIRC model and influenza A. Math. Biosci. 200, 152–169 (2006)
    • 7. Rihan, F.A., Baleanu, D., Lakshmanan, S., Rakkiyappan, R.: On fractional SIRC model with Salmonella bacterial infection. Abstract Appl....
    • 8. Ahn, K.W., Kosoy, M., Chan, K.: An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms...
    • 9. Shrock, E., et al.: Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity. Science 370, eabd4250...
    • 10. Rihan, F.A., Alsakaji, H.J., Rajivganthi, C.: Stochastic SIRC epidemic model with time-delay for COVID-19. Adv. Differ. Equ. 2020, 502...
    • 11. Jodar, L., Villanueva, R.J., Arenas, A.J., Gonzalez, G.C.: Nonstandard numerical methods for a mathematical model for influenza disease....
    • 12. Zhou, B., Zhang, X., Jiang, D.: Dynamics and density function analysis of a stochastic SVI epidemic model with half saturated incidence...
    • 13. Kadi, N., Khelfaoui, M.: Population density, a factor in the spread of COVID-19 in Algeria: statistic study. Kadi Khelfaoui Bull. Natl....
    • 14. Allaerts, W.: The too obvious, uncontrolled controlling factors in the spread of COVID-19 infection: the roles of school openings and...
    • 15. Zhou, B., Han, B., Jiang, D.: Ergodic property, extinction and density function of a stochastic SIR epidemic model with nonlinear incidence...
    • 16. Shi, Z., Jiang, D., Shi, N., Hayat, T., Alsaedi, A.: The impact of nonlinear perturbation to the dynamics of HIV model. Math. Methods...
    • 17. Du, N.H., Dieu, N.T., Nhu, N.N.: Conditions for permanence and ergodicity of certain SIR epidemic models. Acta Appl. Math. 160, 81–99...
    • 18. Mao, X., et al.: Environmental Brownian noise suppresses explosions in population dynamics. Stochastic Process. Appl. 97(1), 95–110 (2002)
    • 19. Zhang, Y., Fan, K., Gao, S., et al.: Ergodic stationary distribution of a stochastic SIRS epidemic model incorporating media coverage...
    • 20. Meng, X., Li, F., Gao, S.: Global analysis and numerical simulations of a novel stochastic ecoepidemiological model with time delay. Appl....
    • 21. Zhang, X., Yuan, R.: A stochastic chemostat model with mean-reverting Ornstein–Uhlenbeck process and Monod-Haldane response function....
    • 22. Cai, Y., Jiao, J., Gui, Z., Liu, Y., Wang, W.: Environmental variability in a stochastic epidemic model. Appl. Math. Comput. 329, 210–226...
    • 23. Zhao, Y., Yuan, S., Ma, J.: Survival and stationary distribution analysis of a stochastic competitive model of three species in a polluted...
    • 24. Wang, W., Cai, Y., Ding, Z., Gui, Z.: A stochastic differential equation SIS epidemic model incorporating Ornstein–Uhlenbeck process....
    • 25. Yang, Q., Zhang, X., Jiang, D.: Dynamical Behaviors of a Stochastic Food Chain System with OrnsteinUhlenbeck Process. J. Nonlinear Sci....
    • 26. Dixit, A.K., Pindyck, R.S.: Investment under Uncertainty. Princeton University Press, Princeton, 39(5), 659–681 (1994)
    • 27. Zhou, B., Jiang, D., Hayat, T.: Analysis of a stochastic population model with mean-reverting Ornstein– Uhlenbeck process and Allee effects....
    • 28. Song, Y., Zhang, X.: Stationary distribution and extinction of a stochastic SVEIS epidemic model incorporating Ornstein–Uhlenbeck process....
    • 29. Mao, X.: Stochastic Differential Equations and Applications. Horwood Publishing, Chichester (1997)
    • 30. Mao, X.: Stochastic Differential Equations and Applications, 2nd edn. Horwood, Chichester (2008)
    • 31. Mao, X., Marion, G., Renshaw, E.: Environmental Brownian noise suppresses explosions in population dynamics. Stochastic Process. Appl....
    • 32. Liu, Q., Jiang, D.: Analysis of a stochastic logistic model with diffusion and Ornstein–Uhlenbeck process. J. Math. Phys. 63, 053505 (2022)
    • 33. Mao, X., Marion, G., Renshaw, E.: Environmental noise suppresses explosion in population dynamics. Stochast. Process Appl. 97, 95–110...
    • 34. Khasminskii, R.: Stochastic Stability of Differential Equations. Springer, Berlin (2011)
    • 35. Han, B., Jiang, D., Zhou, B., Hayat, T., Alsaedi, A.: Stationary distribution and probability density function of a stochastic SIRSI epidemic...
    • 36. Zhou, B., Zhang, X., Jiang, D.: Dynamics and density function analysis of a stochastic SVI epidemic model with half saturated incidence...
    • 37. Gardiner, C.W.: Handbook of Stochastic Methods for Physics. Chemistry and the Natural Sciences. Springer, Berlin (1893)
    • 38. Roozen, H.: An asymptotic solution to a two-dimensional exit problem arising in population dynamics. SIAM J. Appl. Math. 49, 1793 (1989)
    • 39. Tian, X., Ren, C.: Linear equations, superposition principle and complex exponential notation. College Physica 23, 23–5 (2004)
    • 40. Zhou, B., Jiang, D., Dai, Y., Hayat, T.: Stationary distribution and density function expression for a stochastic SIQRS epidemic model...
    • 41. Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. Ser. A 115, 700–21 (1927)
    • 42. Higham, D.J.: An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Rev. 43, 525–546 (2001)
    • 43. Gao, H.: Applied Multivariate Statistical Analysis. Peking University Press, Beijing (2005)
    • 44. Mao, X., Marion, G., Renshaw, E.: Environmental Brownian noise suppresses explosions in population dynamics. Stochastic Process. Appl....

Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno