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Dynamics of a multigroup stochastic SIQR epidemic model

  • Sanaz Lamei EMAIL logo and Mozhgan Akbari
Published/Copyright: August 3, 2024

Abstract

In this paper, we consider a multigroup stochastic SIQR epidemic model with varying total population size. After proving the existence and uniqueness of the global solution to the system, we developed sufficient conditions for the existence of an stationary ergodic distribution of the positive solutions. Then we gave sufficient conditions for extinction of the diseases which is based on the basic reproduction number in its corresponding deterministic system.

MSC 2020: 60H10; 92B05; 97K60

References

[1] E. Beretta and V. Capasso, Global stability results for a multigroup SIR epidemic model, Mathematical Ecology, World Scientific, Teaneck (1988), 317–342. Search in Google Scholar

[2] A. Berman and R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences, Comput. Sci. Appl. Math., Academic Press, New York, 1979. 10.1016/B978-0-12-092250-5.50009-6Search in Google Scholar

[3] Y. Chen, B. Wen and Z. Teng, The global dynamics for a stochastic SIS epidemic model with isolation, Phys. A 492 (2018), 1604–1624. 10.1016/j.physa.2017.11.085Search in Google Scholar PubMed PubMed Central

[4] N. Dalal, D. Greenhalgh and X. Mao, A stochastic model for internal HIV dynamics, J. Math. Anal. Appl. 341 (2008), no. 2, 1084–1101. 10.1016/j.jmaa.2007.11.005Search in Google Scholar

[5] J. Danane, K. Allali, Z. Hammouch and K. Sooppy Nisar, Mathematical analysis and simulation of a stochastic COVID-19 Lévy jump model with isolation strategy, Results Phys. 23 (2021), 1–12. 10.1016/j.rinp.2021.103994Search in Google Scholar PubMed PubMed Central

[6] J. Djordjevic and C. J. Silva, A stochastic analysis of the impact of fluctuations in the environment on pre-exposure prophylaxis for HIV infection, Soft Computing 25 (2021), 6731–6743. 10.1007/s00500-019-04611-1Search in Google Scholar

[7] J. Djordjevic, C. J. Silva and D. F. M. Torres, A stochastic SICA epidemic model for HIV transmission, Appl. Math. Lett. 84 (2018), 168–175. 10.1016/j.aml.2018.05.005Search in Google Scholar

[8] R. Z. Has’minskii, Stochastic Stability of Differential Equations, Monogr. Textb. Mech. Solids Fluids 7, Sijthoff and Noordhoff, Alphen aan den Rijn, 1980. Search in Google Scholar

[9] A. Iggidr, G. Sallet and M. O. Souza, On the dynamics of a class of multi-group models for vector-borne diseases, J. Math. Anal. Appl. 441 (2016), no. 2, 723–743. 10.1016/j.jmaa.2016.04.003Search in Google Scholar

[10] C. Ji and D. Jiang, The asymptotic behavior of a stochastic multigroup SIS model, Int. J. Biomath. 11 (2018), no. 3, Article ID 1850037. 10.1142/S1793524518500377Search in Google Scholar

[11] A. Lajmanovich and J. A. Yorke, A deterministic model for gonorrhea in a nonhomogeneous population, Math. Biosci. 28 (1976), no. 3–4, 221–236. 10.1016/0025-5564(76)90125-5Search in Google Scholar

[12] Q. Liu and D. Jiang, Dynamics of a multigroup SIRS epidemic model with random perturbations and varying total population size, Commun. Pure Appl. Anal. 19 (2020), no. 2, 1089–1110. 10.3934/cpaa.2020050Search in Google Scholar

[13] Q. Liu, D. Jiang, T. Hayat and A. Alsaedi, Dynamics of a stochastic multigroup SIQR epidemic model with standard incidence rates, J. Franklin Inst. 356 (2019), no. 5, 2960–2993. 10.1016/j.jfranklin.2019.01.038Search in Google Scholar

[14] X. Mao, Stochastic Differential Equations and Their Applications, Horwood, Chichester, 1997. Search in Google Scholar

[15] Y. Muroya, Y. Enatsu and T. Kuniya, Global stability for a multi-group SIRS epidemic model with varying population sizes, Nonlinear Anal. Real World Appl. 14 (2013), no. 3, 1693–1704. 10.1016/j.nonrwa.2012.11.005Search in Google Scholar

[16] L. Rass and J. Radcliffe, Global asymptotic convergence results for multitype models, Int. J. Appl. Math. Comput. Sci. 10 (2000), 63–79. Search in Google Scholar

[17] V. Volpert, M. Banerjee and S. Petrovskii, On a quarantine model of coronavirus infection and data analysis, Math. Model. Nat. Phenom. 15 (2020), Paper No. 24. 10.1051/mmnp/2020006Search in Google Scholar

Received: 2024-01-12
Revised: 2024-07-11
Accepted: 2024-07-15
Published Online: 2024-08-03
Published in Print: 2024-09-01

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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