A stochastic SIRS epidemic model with vaccination is discussed. A new stochastic threshold $ R_0^s $ is determined. When the noise is very low ($ R_0^s < 1 $), the disease becomes extinct, and if $ R_0^s > 1 $, the disease persists. Furthermore, we show that the solution of the stochastic model oscillates around the endemic equilibrium point and the intensity of the fluctuation is proportional to the intensity of the white noise. Computer simulations are used to support our findings.
Citation: Tingting Xue, Xiaolin Fan, Zhiguo Chang. Dynamics of a stochastic SIRS epidemic model with standard incidence and vaccination[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 10618-10636. doi: 10.3934/mbe.2022496
A stochastic SIRS epidemic model with vaccination is discussed. A new stochastic threshold $ R_0^s $ is determined. When the noise is very low ($ R_0^s < 1 $), the disease becomes extinct, and if $ R_0^s > 1 $, the disease persists. Furthermore, we show that the solution of the stochastic model oscillates around the endemic equilibrium point and the intensity of the fluctuation is proportional to the intensity of the white noise. Computer simulations are used to support our findings.
[1] | J. Gu, Z. Zhou, Y. Wang, Editorial: evolutionary mechanisms of infectious diseases, Front. Microbiol., 12 (2021), 667561. https://doi.org/10.3389/fmicb.2021.667561 doi: 10.3389/fmicb.2021.667561 |
[2] | W. Garira, M. C. Mafunda, From individual health to community health: towards multiscale modeling of directly transmitted infectious disease systems, J. Biol. Syst., 27 (2019), 131–166. https://doi.org/10.1142/S0218339019500074 doi: 10.1142/S0218339019500074 |
[3] | T. Sawakami, K. Karako, P. P. Song, W. Sugiura, N. Kokudo, Infectious disease activity during the COVID-19 epidemic in Japan: lessons learned from prevention and control measures comment, Biosci. Trends, 15 (2020), 257–261. https://doi.org/10.5582/bst.2021.01269 doi: 10.5582/bst.2021.01269 |
[4] | Y. Takeuchi, T. Sasaki, T. Kajiwara, Construction of Lyapunov functions for some models of infectious diseases in vivo: from simple models to complex models, Math. Biosci. Eng., 12 (2015), 117–133. https://doi.org/10.3934/mbe.2015.12.117 doi: 10.3934/mbe.2015.12.117 |
[5] | J. R. Giles, E. zu Erbach-Schoenberg, A. J. Tatem, L. Gardner, O. N. Bjornstad, C. J. E. Metcalf, et al., The duration of travel impacts the spatial dynamics of infectious diseases, P. Natl. Acad. Sci. U.S.A., 117 (2000), 22572–22579. https://doi.org/10.1073/pnas.1922663117 doi: 10.1073/pnas.1922663117 |
[6] | J. Rossello, M. Santana-Gallego, W. Awan, Infectious disease risk and international tourism demand, Health Policy Plann., 32 (2017), 538–548. https://doi.org/10.1093/heapol/czw177 doi: 10.1093/heapol/czw177 |
[7] | K. E. Jones, N. G. Patel, M. A. Levy, A. Storeygard, D. Balk, J. L. Gittleman, et al., Global trends in emerging infectious diseases, Nature, 451 (2008), 990–993. https://doi.org/10.1038/nature06536 doi: 10.1038/nature06536 |
[8] | S. Hussain, E. N. Madi, H. Khan, S. Etemad, S. Rezapour, T. Sitthiwirattham, et al., Investigation of the stochastic modeling of COVID-19 with environmental noise from the analytical and numerical point of view, Mathematics, 9 (2022), 23. https://doi.org/10.3390/math9233122 doi: 10.3390/math9233122 |
[9] | A. Din, Y. J. Li, Q. Liu, Viral dynamics and control of hepatitis B virus (HBV) using an epidemic model, Alexandria Eng. J., 59 (2020), 667–679. https://doi.org/10.1016/j.aej.2020.01.034 doi: 10.1016/j.aej.2020.01.034 |
[10] | S. Kumar, A. Ahmadian, R. Kumar, D. Kumar, J. Singh, D. Baleanu, et al., An efficient numerical method for fractional SIR epidemic model of infectious disease by using Bernstein wavelets, Mathematics, 8 (2020), 558. https://doi.org/10.3390/math8040558 doi: 10.3390/math8040558 |
[11] | W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. London, 115 (1927), 700–721. https://doi.org/10.1098/rspa.1927.0118 doi: 10.1098/rspa.1927.0118 |
[12] | W. O. Kermack, A. G. A. McKendrick, Contributions to the mathematical theory of epidemics–II. the problem of endemicity, Bull. Math. Biol., 53 (1991), 57–87. https://doi.org/10.1007/BF02464424 doi: 10.1007/BF02464424 |
[13] | 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 doi: 10.1007/s002850000032 |
[14] | X. Z. Meng, S. N. Zhao, T. Feng, T. H. Zhang, Dynamics of a novel nonlinear stochastic SIS epidemic model with double epidemic hypothesis, J. Math. Anal. Appl., 433 (2016), 227–242. https://doi.org/10.1016/j.jmaa.2015.07.056 doi: 10.1016/j.jmaa.2015.07.056 |
[15] | C. C. McCluskey, Complete global stability for an SIR epidemic model with delay-Distributed or discrete, Nonlinear Anal.: Real World Appl., 11 (2010), 55–59. https://doi.org/10.1016/j.nonrwa.2008.10.014 doi: 10.1016/j.nonrwa.2008.10.014 |
[16] | I. Cooper, A. Mondal, C. G. Antonopoulos, A SIR model assumption for the spread of COVID-19 in different communities, Chaos, Solitons Fractals, 139 (2020), 110057. https://doi.org/10.1016/j.chaos.2020.110057 doi: 10.1016/j.chaos.2020.110057 |
[17] | Y. A. Zhao, D. Q. Jiang, X. R. Mao, A. Gray, The threshold of a stochastic SIRS epidemic model in a population with varying size, Discrete Contin. Dyn. Syst. -B, 20 (2015), 1277–1295. https://doi.org/10.3934/dcdsb.2015.20.1277 doi: 10.3934/dcdsb.2015.20.1277 |
[18] | Y. l. Cai, Y. Kang, M. Banerjee, W. M. Wang, A stochastic SIRS epidemic model with infectious force under intervention strategies, J. Differ. Equations, 259 (2015), 7463–7502. https://doi.org/10.1016/j.jde.2015.08.024 doi: 10.1016/j.jde.2015.08.024 |
[19] | Z. F. Yang, Z. Q. Zeng, K. Wang, Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions, J. Thorac. Dis., 13 (2020), 165–174. https://doi.org/10.21037/jtd.2020.02.64 doi: 10.21037/jtd.2020.02.64 |
[20] | J. M. Carcione, J. E. Santos, C. Bagaini, J. Ba, A Simulation of a COVID-19 epidemic based on a deterministic SEIR model, Front. Public Health, 8 (2020), 1–13. https://doi.org/10.3389/fpubh.2020.00230 doi: 10.3389/fpubh.2020.00230 |
[21] | S. Ruschel, T. Pereira, S. Yanchuk, L. S. Young, An SIQ delay differential equations model for disease control via isolation, J. Math. Biol., 79 (2019), 249–279. https://doi.org/10.1007/s00285-019-01356-1 |
[22] | X. B. Zhang, X. H. Zhang, The threshold of a deterministic and a stochastic SIQS epidemic model with varying total population size, Appl. Math. Model., 10 (2021), 749–767. https://doi.org/10.1016/j.apm.2020.09.050 doi: 10.1016/j.apm.2020.09.050 |
[23] | Y. L. Huizer, C. M. Swaan, K. C. Leitmeyer, A. Timen, Usefulness and applicability of infectious disease control measures in air travel: a review, Travel Med. Infect. Dis., 13 (2015), 19–30. https://doi.org/10.1016/j.tmaid.2014.11.008 doi: 10.1016/j.tmaid.2014.11.008 |
[24] | A. Din, Y. J. Li, T. Khan, G. Zaman, Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China, Chaos, Solitons Fractals, 141 (2021), 110286. https://doi.org/10.1016/j.chaos.2020.110286 doi: 10.1016/j.chaos.2020.110286 |
[25] | S. R. Gani, S. V. Halawar, Optimal control for the spread of infectious disease: the role of awareness programs by media and antiviral treatment, Optim. Control. Appl. Methods, 39 (2018), 1407–1430. https://doi.org/10.1002/oca.2418 doi: 10.1002/oca.2418 |
[26] | Z. Jin, The SIR epidemical models with continuous and impulsive vaccinations, J. North China Inst. Technol., 24 (2003), 235–243. |
[27] | A. Gray, D. Greenhalgh, L. Hu, X. Mao, J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876–902. https://doi.org/10.1137/10081856X doi: 10.1137/10081856X |
[28] | Y. N. Zhao, D. Q. Jiang, The threshold of a stochastic SIRS epidemic model with saturated incidence, Appl. Math. Lett., 34 (2014), 90–93. https://doi.org/10.1016/j.aml.2013.11.002 doi: 10.1016/j.aml.2013.11.002 |
[29] | B. Y. Wen, Z. D. Teng, Z. M. Li, The threshold of a periodic stochastic SIVS epidemic model with nonlinear incidence, Phys. A, 508 (2018), 532–549. https://doi.org/10.1016/j.physa.2018.05.056 doi: 10.1016/j.physa.2018.05.056 |
[30] | Q. Liu, Q. M. Chen, D. Q. Jiang, The threshold of a stochastic delayed SIR epidemic model with temporary immunity, Phys. A, 450 (2016), 115–125. https://doi.org/10.1016/j.physa.2015.12.056 doi: 10.1016/j.physa.2015.12.056 |
[31] | M. El Fatini, R. Pettersson, I. Sekkak, R. Taki, A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies, J. Appl. Math. Comput., 64 (2020), 781–805. https://doi.org/10.1007/s12190-020-01380-1 doi: 10.1007/s12190-020-01380-1 |
[32] | X. Mao, Stochastic Differential Equations and Applications, 2nd edition, Chichester: Horwood Publishing Limited, 2008. |
[33] | 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 doi: 10.1137/S0036144500378302 |