May  2020, 25(5): 1985-1997. doi: 10.3934/dcdsb.2020012

On the threshold dynamics of the stochastic SIRS epidemic model using adequate stopping times

1. 

Department of Mathematics, Faculty of Sciences and Technics, Abdelmalek Essaâdi University, BP 416, Tangier, Morocco

2. 

Department of Mathematics, Faculty of Sciences, Ibn Tofail University, BP 133 Kénitra, Morocco

3. 

Department of Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China

*Correponding author (melfatini@gmail.com)

Received  April 2019 Revised  July 2019 Published  December 2019

As it is well known, the dynamics of the stochastic SIRS epidemic model with mass action is governed by a threshold $ \mathcal{R_S} $. If $ \mathcal{R_S}<1 $ the disease dies out from the population, while if $ \mathcal{R_S}> 1 $ the disease persists. However, when $ \mathcal{R_S} = 1 $, classical techniques used to study the asymptotic behaviour do not work any more. In this paper, we give answer to this open problem by using a new approach involving some adequate stopping times. Our results show that if $ \mathcal{R_S} = 1 $ then, small noises promote extinction while the large one promote persistence. So, it is exactly the opposite role of the noises in case when $ \mathcal{R_S}\neq 1 $.

Citation: Adel Settati, Aadil Lahrouz, Mustapha El Jarroudi, Mohamed El Fatini, Kai Wang. On the threshold dynamics of the stochastic SIRS epidemic model using adequate stopping times. Discrete & Continuous Dynamical Systems - B, 2020, 25 (5) : 1985-1997. doi: 10.3934/dcdsb.2020012
References:
[1]

E. Allen, Modeling with Itô Stochastic Differential Equations, Springer, The Netherlands, 2007. Google Scholar

[2]

B. BerrhaziM. El FatiniT. Caraballo and R. Pettersson, A stochastic SIRI epidemic model with Lévy noise, Discrete Contin. Dyn. Syst. Ser. B, 23 (2018), 2415-2431.  doi: 10.3934/dcdsb.2018057.  Google Scholar

[3]

B. BerrhaziM. El FatiniA. LaaribiR. Pettersson and R. Taki, A stochastic SIRS epidemic model incorporating media coverage and driven by Lévy noise, Chaos Solitons Fractals, 105 (2017), 60-68.  doi: 10.1016/j.chaos.2017.10.007.  Google Scholar

[4]

B. BerrhaziM. El FatiniA. LahrouzA. Settati and R. Taki, A stochastic SIRS epidemic model with a general awareness-induced incidence, Phys. A, 512 (2018), 968-980.  doi: 10.1016/j.physa.2018.08.150.  Google Scholar

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V. Capasso, Mathematical Structures of Epidemic Systems, Lecture Notes in Biomathematics, 97. Springer-Verlag, Berlin, 1993. doi: 10.1007/978-3-540-70514-7.  Google Scholar

[6]

T. CaraballoM. El FatiniR. Pettersson and R. Taki, A stochastic SIRI epidemic model with relapse and media coverage, Discrete Contin. Dyn. Syst. Ser. B, 23 (2018), 3483-3501.  doi: 10.3934/dcdsb.2018250.  Google Scholar

[7]

T. Caraballo and R. Colucci, A comparison between random and stochastic modeling for a SIR model, Commun. Pure Appl. Anal., 16 (2017), 151-162.  doi: 10.3934/cpaa.2017007.  Google Scholar

[8]

C. Castillo-ChavezZ. L. Feng and W. Z. Huang, On the computation of $\mathcal{R}_0$ and its role on global stability, Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction (Minneapolis, MN, 1999), IMA Vol. Math. Appl., Springer, New York, 125 (2002), 229-250.   Google Scholar

[9]

O. DiekmannJ. A. P. Heesterbeek and J. A. J. Metz, On the definition and the computation of the basic reproduction ratio $\mathcal{R}_0$ in models for infectious diseases in heterogeneous population, J. Math. Biol., 28 (1990), 365-382.  doi: 10.1007/BF00178324.  Google Scholar

[10]

M. El Fatini, B. Boukanjime, Stochastic analysis of a two delayed epidemic model incorporating Lévy processes with a general non-linear transmission, Stoch. Anal. Appl, (Published online: 26 Oct 2019) https://doi.org/10.1080/07362994.2019.1680295. doi: 10.1080/07362994.2019.1680295.  Google Scholar

[11]

M. El FatiniA. LaaribiR. Pettersson and R. Taki, Lévy noise perturbation for an epidemic model with impact of media coverage, Stochastics, 91 (2019), 998-1019.  doi: 10.1080/17442508.2019.1595622.  Google Scholar

[12]

M. El FatiniA. LahrouzR. PetterssonA. Settati and R. Taki, Stochastic stability and instability of an epidemic model with relapse, Appl. Math. Comput., 316 (2018), 326-341.  doi: 10.1016/j.amc.2017.08.037.  Google Scholar

[13]

A. GrayD. GreenhalghL. HuX. Mao and J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876-902.  doi: 10.1137/10081856X.  Google Scholar

[14]

H. W. Hethcote, Qualitative analyses of communicable disease models, Math. Biosci., 28 (1976), 335-356.  doi: 10.1016/0025-5564(76)90132-2.  Google Scholar

[15]

C. Y. Ji and D. Q. Jiang, Threshold behaviour of a stochastic SIR model, Appl. Math. Model, 38 (2014), 5067-5079.  doi: 10.1016/j.apm.2014.03.037.  Google Scholar

[16]

C. Y. JiD. Q. Jiang and N. Z. Shi, Multigroup SIR epidemic model with stochastic perturbation, Physica A, 390 (2011), 1747-1762.   Google Scholar

[17]

P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Applications of Mathematics (New York), 23. Springer-Verlag, Berlin, 1992. doi: 10.1007/978-3-662-12616-5.  Google Scholar

[18]

A. Lahrouz and L. Omari, Extinction and stationary distribution of a stochastic SIRS epidemic model with non-linear incidence, Statist. Probab. Lett., 83 (2013), 960-968.  doi: 10.1016/j.spl.2012.12.021.  Google Scholar

[19]

A. Lahrouz and A. Settati, Necessary and sufficient condition for extinction and persistence of SIRS system with random perturbation, Appl. Math. Comput., 233 (2014), 10-19.  doi: 10.1016/j.amc.2014.01.158.  Google Scholar

[20]

A. Lahrouz and A. Settati, Asymptotic properties of switching diffusion epidemic model with varying population size, Appl. Math. Comput., 219 (2013), 11134-11148.  doi: 10.1016/j.amc.2013.05.019.  Google Scholar

[21]

Y. G. Lin and D. Q. Jiang, Long-time behavior of a perturbed SIR model by white noise, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 1873-1887.  doi: 10.3934/dcdsb.2013.18.1873.  Google Scholar

[22]

Q. Y. Lu, Stability of SIRS system with random perturbations, Physica A, 388 (2009), 3677-3686.  doi: 10.1016/j.physa.2009.05.036.  Google Scholar

[23]

A. SettatiA. LahrouzM. El Jarroudi and M. El Jarroudi, Dynamics of hybrid switching diffusions SIRS model, J. Appl. Math. Comput., 52 (2016), 101-123.  doi: 10.1007/s12190-015-0932-4.  Google Scholar

[24]

E. TornatoreS. M. Buccellato and P. Vetro, Stability of a stochastic SIR system, Physica A, 35 (2005), 4111-4126.   Google Scholar

[25]

P. Van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180 (2002), 29-48.  doi: 10.1016/S0025-5564(02)00108-6.  Google Scholar

[26]

Y. N. Zhao and D. Q. Jiang, The threshold of a stochastic SIS epidemic model with vaccination, Appl. Math. Comput., 243 (2014), 718-727.  doi: 10.1016/j.amc.2014.05.124.  Google Scholar

show all references

References:
[1]

E. Allen, Modeling with Itô Stochastic Differential Equations, Springer, The Netherlands, 2007. Google Scholar

[2]

B. BerrhaziM. El FatiniT. Caraballo and R. Pettersson, A stochastic SIRI epidemic model with Lévy noise, Discrete Contin. Dyn. Syst. Ser. B, 23 (2018), 2415-2431.  doi: 10.3934/dcdsb.2018057.  Google Scholar

[3]

B. BerrhaziM. El FatiniA. LaaribiR. Pettersson and R. Taki, A stochastic SIRS epidemic model incorporating media coverage and driven by Lévy noise, Chaos Solitons Fractals, 105 (2017), 60-68.  doi: 10.1016/j.chaos.2017.10.007.  Google Scholar

[4]

B. BerrhaziM. El FatiniA. LahrouzA. Settati and R. Taki, A stochastic SIRS epidemic model with a general awareness-induced incidence, Phys. A, 512 (2018), 968-980.  doi: 10.1016/j.physa.2018.08.150.  Google Scholar

[5]

V. Capasso, Mathematical Structures of Epidemic Systems, Lecture Notes in Biomathematics, 97. Springer-Verlag, Berlin, 1993. doi: 10.1007/978-3-540-70514-7.  Google Scholar

[6]

T. CaraballoM. El FatiniR. Pettersson and R. Taki, A stochastic SIRI epidemic model with relapse and media coverage, Discrete Contin. Dyn. Syst. Ser. B, 23 (2018), 3483-3501.  doi: 10.3934/dcdsb.2018250.  Google Scholar

[7]

T. Caraballo and R. Colucci, A comparison between random and stochastic modeling for a SIR model, Commun. Pure Appl. Anal., 16 (2017), 151-162.  doi: 10.3934/cpaa.2017007.  Google Scholar

[8]

C. Castillo-ChavezZ. L. Feng and W. Z. Huang, On the computation of $\mathcal{R}_0$ and its role on global stability, Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction (Minneapolis, MN, 1999), IMA Vol. Math. Appl., Springer, New York, 125 (2002), 229-250.   Google Scholar

[9]

O. DiekmannJ. A. P. Heesterbeek and J. A. J. Metz, On the definition and the computation of the basic reproduction ratio $\mathcal{R}_0$ in models for infectious diseases in heterogeneous population, J. Math. Biol., 28 (1990), 365-382.  doi: 10.1007/BF00178324.  Google Scholar

[10]

M. El Fatini, B. Boukanjime, Stochastic analysis of a two delayed epidemic model incorporating Lévy processes with a general non-linear transmission, Stoch. Anal. Appl, (Published online: 26 Oct 2019) https://doi.org/10.1080/07362994.2019.1680295. doi: 10.1080/07362994.2019.1680295.  Google Scholar

[11]

M. El FatiniA. LaaribiR. Pettersson and R. Taki, Lévy noise perturbation for an epidemic model with impact of media coverage, Stochastics, 91 (2019), 998-1019.  doi: 10.1080/17442508.2019.1595622.  Google Scholar

[12]

M. El FatiniA. LahrouzR. PetterssonA. Settati and R. Taki, Stochastic stability and instability of an epidemic model with relapse, Appl. Math. Comput., 316 (2018), 326-341.  doi: 10.1016/j.amc.2017.08.037.  Google Scholar

[13]

A. GrayD. GreenhalghL. HuX. Mao and J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876-902.  doi: 10.1137/10081856X.  Google Scholar

[14]

H. W. Hethcote, Qualitative analyses of communicable disease models, Math. Biosci., 28 (1976), 335-356.  doi: 10.1016/0025-5564(76)90132-2.  Google Scholar

[15]

C. Y. Ji and D. Q. Jiang, Threshold behaviour of a stochastic SIR model, Appl. Math. Model, 38 (2014), 5067-5079.  doi: 10.1016/j.apm.2014.03.037.  Google Scholar

[16]

C. Y. JiD. Q. Jiang and N. Z. Shi, Multigroup SIR epidemic model with stochastic perturbation, Physica A, 390 (2011), 1747-1762.   Google Scholar

[17]

P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Applications of Mathematics (New York), 23. Springer-Verlag, Berlin, 1992. doi: 10.1007/978-3-662-12616-5.  Google Scholar

[18]

A. Lahrouz and L. Omari, Extinction and stationary distribution of a stochastic SIRS epidemic model with non-linear incidence, Statist. Probab. Lett., 83 (2013), 960-968.  doi: 10.1016/j.spl.2012.12.021.  Google Scholar

[19]

A. Lahrouz and A. Settati, Necessary and sufficient condition for extinction and persistence of SIRS system with random perturbation, Appl. Math. Comput., 233 (2014), 10-19.  doi: 10.1016/j.amc.2014.01.158.  Google Scholar

[20]

A. Lahrouz and A. Settati, Asymptotic properties of switching diffusion epidemic model with varying population size, Appl. Math. Comput., 219 (2013), 11134-11148.  doi: 10.1016/j.amc.2013.05.019.  Google Scholar

[21]

Y. G. Lin and D. Q. Jiang, Long-time behavior of a perturbed SIR model by white noise, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 1873-1887.  doi: 10.3934/dcdsb.2013.18.1873.  Google Scholar

[22]

Q. Y. Lu, Stability of SIRS system with random perturbations, Physica A, 388 (2009), 3677-3686.  doi: 10.1016/j.physa.2009.05.036.  Google Scholar

[23]

A. SettatiA. LahrouzM. El Jarroudi and M. El Jarroudi, Dynamics of hybrid switching diffusions SIRS model, J. Appl. Math. Comput., 52 (2016), 101-123.  doi: 10.1007/s12190-015-0932-4.  Google Scholar

[24]

E. TornatoreS. M. Buccellato and P. Vetro, Stability of a stochastic SIR system, Physica A, 35 (2005), 4111-4126.   Google Scholar

[25]

P. Van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180 (2002), 29-48.  doi: 10.1016/S0025-5564(02)00108-6.  Google Scholar

[26]

Y. N. Zhao and D. Q. Jiang, The threshold of a stochastic SIS epidemic model with vaccination, Appl. Math. Comput., 243 (2014), 718-727.  doi: 10.1016/j.amc.2014.05.124.  Google Scholar

Figure 1.  simulation results of $ S(t),I(t),R(t) $ respectively for the SDE model (1.1) with different initial conditions $ (S_{0},I_{0},R_{0}) $ and the parameters: $ \mu = 0.1 $, $ \beta = 0.62 $, $ \lambda = 0.5 $, $ \gamma = 0.5 $, $ \sigma = 0.2 $. Here $ \mathcal{R_S} = 1 $ and $ \sigma^{2}<\beta $. For the different values of initial conditions the infection-free equilibrium $ E_0(1,0,0) $ is asymptotically stable in probability
Figure 2.  simulation results of $ S(t),I(t),R(t) $ respectively for the SDE model (1.1) with different initial conditions $ (S_{0},I_{0},R_{0}) $ and the parameters: $ \mu = 0.1 $, $ \beta = 0.7802 $, $ \lambda = 0.2 $, $ \gamma = 0.2 $, $ \sigma = 0.98 $. Here $ \mathcal{R_S} = 1 $ and $ \beta<\sigma^{2} $. For the different values of initial conditions, respectively, $ S(t) $, $ I(t) $ and $ R(t) $ rises to or above the level $ \xi_s = 0.6247 $, $ \xi_i = 0.2252 $ and $ \xi_r = 0.1501 $ infinitely often with probability one
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