doi: 10.3934/dcdsb.2020317

A stochastic differential equation SIS epidemic model with regime switching

1. 

Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK

2. 

School of Mathematical Sciences, University of Nottingham Ningbo China, Ningbo, 315100, China

* Corresponding author: yongmei.cai@nottingham.edu.cn

Received  February 2020 Revised  September 2020 Published  November 2020

In this paper, we combined the previous model in [2] with Gray et al.'s work in 2012 [8] to add telegraph noise by using Markovian switching to generate a stochastic SIS epidemic model with regime switching. Similarly, threshold value for extinction and persistence are then given and proved, followed by explanation on the stationary distribution, where the $ M $-matrix theory elaborated in [20] is fully applied. Computer simulations are clearly illustrated with different sets of parameters, which support our theoretical results. Compared to our previous work in 2019 [2, 3], our threshold value are given based on the overall behaviour of the solution but not separately specified in every state of the Markov chain.

Citation: Siyang Cai, Yongmei Cai, Xuerong Mao. A stochastic differential equation SIS epidemic model with regime switching. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2020317
References:
[1]

W. J. Anderson, Continuous-time Markov Chains: An Applications-Oriented Approach, Springer-Verlag, New York, 1991. doi: 10.1007/978-1-4612-3038-0.  Google Scholar

[2]

S. Cai, Y. Cai and X. Mao, A stochastic differential equation SIS epidemic model with two independent Brownian motions, Journal of Mathematical Analysis and Applications, 474 (2019), 1536–1550, http://www.sciencedirect.com/science/article/pii/S0022247X19301635. doi: 10.1016/j.jmaa.2019.02.039.  Google Scholar

[3]

S. Cai, Y. Cai and X. Mao, A stochastic differential equation SIS epidemic model with two correlated Brownian motions, J. Math. Anal. Appl., 474 (2019), 1536–1550, https://doi.org/10.1007/s11071-019-05114-2. doi: 10.1016/j.jmaa.2019.02.039.  Google Scholar

[4]

Y. Cai and X. Mao, Stochastic prey-predator system with foraging arena scheme, Applied Mathematical Modelling, 64 (2018), 357–371, http://www.sciencedirect.com/science/article/pii/S0307904X18303500. doi: 10.1016/j.apm.2018.07.034.  Google Scholar

[5]

Y. Cai, S. Cai and X. Mao, Stochastic delay foraging arena predator–prey system with Markov switching, Stochastic Analysis and Applications, 38 (2020), 191–212, https://doi.org/10.1080/07362994.2019.1679645. doi: 10.1080/07362994.2019.1679645.  Google Scholar

[6]

Y. Cai, S. Cai and X. Mao, Analysis of a stochastic predator-prey system with foraging arena scheme, Stochastics, 92 (2020), 193–222. https://doi.org/10.1080/17442508.2019.1612897. doi: 10.1080/17442508.2019.1612897.  Google Scholar

[7]

T. H. Fleming and J. N. Holland, The evolution of obligate pollination mutualisms: Senita cactus and senita moth, Oecologia, 114 (1998), 368-375.  doi: 10.1007/s004420050459.  Google Scholar

[8]

A. GrayD. GreenhalghX. Mao and J. Pan, The SIS epidemic model with Markovian switching, Journal of Mathematical Analysis and Applications, 394 (2012), 496-516.  doi: 10.1016/j.jmaa.2012.05.029.  Google Scholar

[9]

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

[10]

D. Greenhalgh and Y. Liang, Modelling the effect of telegraph noise in the SIRS epidemic model using Markovian switching, Physica A: Statistical Mechanics and its Applications, 462 (2016), 684-704.  doi: 10.1016/j.physa.2016.06.125.  Google Scholar

[11]

J. D. Hamilton, Regime switching models, The New Palgrave Dictionary of Economics, 2016, 1–7. Google Scholar

[12]

A. Hening and D. H. Nguyen, Stochastic Lotka–Volterra food chains, Journal of Mathematical Biology, 77 (2018), 135-163.  doi: 10.1007/s00285-017-1192-8.  Google Scholar

[13]

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

[14]

J. N. Holland and T. H. Fleming, Geographic and population variation in pollinating seed-consuming interactions between senita cacti (Lophocereus schottii) and senita moths (Upiga virescens), Oecologia, 121 (1999), 405-410.  doi: 10.1007/s004420050945.  Google Scholar

[15]

J. N. HollandD. L. DeAngelis and J. L. Bronstein, Population dynamics and mutualism: Functional responses of benefits and costs, The University of Chicago Press, 159 (2002), 231-244.   Google Scholar

[16]

R. Khasminskii, Stochastic Stability of Differential Equations, 66. Springer, Heidelberg, 2012. doi: 10.1007/978-3-642-23280-0.  Google Scholar

[17]

X. LiD. Jiang and X. Mao, Population dynamical behavior of Lotka–Volterra system under regime switching, Journal of Computational and Applied Mathematics, 232 (2009), 427-448.  doi: 10.1016/j.cam.2009.06.021.  Google Scholar

[18]

H. LiuX. Li and Q. Yang, The ergodic property and positive recurrence of a multi-group Lotka–Volterra mutualistic system with regime switching, Systems & Control Letters, 62 (2013), 805-810.  doi: 10.1016/j.sysconle.2013.06.002.  Google Scholar

[19]

Q. Luo and X. Mao, Stochastic population dynamics under regime switching, Journal of Mathematical Analysis and applications, 334 (2007), 69-84.  doi: 10.1016/j.jmaa.2006.12.032.  Google Scholar

[20]

X. Mao and C. Yuan, Stochastic Differential Equations with Markovian Switching, Imperial College Press, 2006. doi: 10.1142/p473.  Google Scholar

[21]

J. R. Norris, Markov Chains, Cambridge University Press, 1998. doi: 10.1017/CBO9780511810633.  Google Scholar

[22]

S. PangF. Deng and X. Mao, Asymptotic properties of stochastic population dynamics, Dynamics of Continuous Discrete and Impulsive Systems Series A: Mathematical Analysis, 15 (2008), 603-620.   Google Scholar

[23]

M. Slatkin, The dynamics of a population in a Markovian environment, Ecology, 59 (1978), 249-256.  doi: 10.2307/1936370.  Google Scholar

[24]

L. S. Tsimring, Noise in biology, IOP Publishing, 77 (2014), 026601. doi: 10.1088/0034-4885/77/2/026601.  Google Scholar

[25]

Y. TakeuchiN. H. DuN. T. Hieu and K. Sato, Evolution of predator–prey systems described by a Lotka–Volterra equation under random environment, Journal of Mathematical Analysis and Applications, 323 (2006), 938-957.  doi: 10.1016/j.jmaa.2005.11.009.  Google Scholar

[26]

D. A. Vasseur and P. Yodzis, The color of environmental noise, Wiley Online Library, 85 (2004), 1146-1152.   Google Scholar

[27]

G. G. Yin and Q. Zhang, Continuous-Time Markov Chains and Applications: A Singular Perturbation Approach, Springer, 37, 2012. doi: 10.1007/978-1-4614-4346-9.  Google Scholar

[28]

C. Zhu and G. Yin, Asymptotic properties of hybrid diffusion systems, SIAM Journal on Control and Optimization, 46 (2007), 1155-1179.  doi: 10.1137/060649343.  Google Scholar

show all references

References:
[1]

W. J. Anderson, Continuous-time Markov Chains: An Applications-Oriented Approach, Springer-Verlag, New York, 1991. doi: 10.1007/978-1-4612-3038-0.  Google Scholar

[2]

S. Cai, Y. Cai and X. Mao, A stochastic differential equation SIS epidemic model with two independent Brownian motions, Journal of Mathematical Analysis and Applications, 474 (2019), 1536–1550, http://www.sciencedirect.com/science/article/pii/S0022247X19301635. doi: 10.1016/j.jmaa.2019.02.039.  Google Scholar

[3]

S. Cai, Y. Cai and X. Mao, A stochastic differential equation SIS epidemic model with two correlated Brownian motions, J. Math. Anal. Appl., 474 (2019), 1536–1550, https://doi.org/10.1007/s11071-019-05114-2. doi: 10.1016/j.jmaa.2019.02.039.  Google Scholar

[4]

Y. Cai and X. Mao, Stochastic prey-predator system with foraging arena scheme, Applied Mathematical Modelling, 64 (2018), 357–371, http://www.sciencedirect.com/science/article/pii/S0307904X18303500. doi: 10.1016/j.apm.2018.07.034.  Google Scholar

[5]

Y. Cai, S. Cai and X. Mao, Stochastic delay foraging arena predator–prey system with Markov switching, Stochastic Analysis and Applications, 38 (2020), 191–212, https://doi.org/10.1080/07362994.2019.1679645. doi: 10.1080/07362994.2019.1679645.  Google Scholar

[6]

Y. Cai, S. Cai and X. Mao, Analysis of a stochastic predator-prey system with foraging arena scheme, Stochastics, 92 (2020), 193–222. https://doi.org/10.1080/17442508.2019.1612897. doi: 10.1080/17442508.2019.1612897.  Google Scholar

[7]

T. H. Fleming and J. N. Holland, The evolution of obligate pollination mutualisms: Senita cactus and senita moth, Oecologia, 114 (1998), 368-375.  doi: 10.1007/s004420050459.  Google Scholar

[8]

A. GrayD. GreenhalghX. Mao and J. Pan, The SIS epidemic model with Markovian switching, Journal of Mathematical Analysis and Applications, 394 (2012), 496-516.  doi: 10.1016/j.jmaa.2012.05.029.  Google Scholar

[9]

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

[10]

D. Greenhalgh and Y. Liang, Modelling the effect of telegraph noise in the SIRS epidemic model using Markovian switching, Physica A: Statistical Mechanics and its Applications, 462 (2016), 684-704.  doi: 10.1016/j.physa.2016.06.125.  Google Scholar

[11]

J. D. Hamilton, Regime switching models, The New Palgrave Dictionary of Economics, 2016, 1–7. Google Scholar

[12]

A. Hening and D. H. Nguyen, Stochastic Lotka–Volterra food chains, Journal of Mathematical Biology, 77 (2018), 135-163.  doi: 10.1007/s00285-017-1192-8.  Google Scholar

[13]

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

[14]

J. N. Holland and T. H. Fleming, Geographic and population variation in pollinating seed-consuming interactions between senita cacti (Lophocereus schottii) and senita moths (Upiga virescens), Oecologia, 121 (1999), 405-410.  doi: 10.1007/s004420050945.  Google Scholar

[15]

J. N. HollandD. L. DeAngelis and J. L. Bronstein, Population dynamics and mutualism: Functional responses of benefits and costs, The University of Chicago Press, 159 (2002), 231-244.   Google Scholar

[16]

R. Khasminskii, Stochastic Stability of Differential Equations, 66. Springer, Heidelberg, 2012. doi: 10.1007/978-3-642-23280-0.  Google Scholar

[17]

X. LiD. Jiang and X. Mao, Population dynamical behavior of Lotka–Volterra system under regime switching, Journal of Computational and Applied Mathematics, 232 (2009), 427-448.  doi: 10.1016/j.cam.2009.06.021.  Google Scholar

[18]

H. LiuX. Li and Q. Yang, The ergodic property and positive recurrence of a multi-group Lotka–Volterra mutualistic system with regime switching, Systems & Control Letters, 62 (2013), 805-810.  doi: 10.1016/j.sysconle.2013.06.002.  Google Scholar

[19]

Q. Luo and X. Mao, Stochastic population dynamics under regime switching, Journal of Mathematical Analysis and applications, 334 (2007), 69-84.  doi: 10.1016/j.jmaa.2006.12.032.  Google Scholar

[20]

X. Mao and C. Yuan, Stochastic Differential Equations with Markovian Switching, Imperial College Press, 2006. doi: 10.1142/p473.  Google Scholar

[21]

J. R. Norris, Markov Chains, Cambridge University Press, 1998. doi: 10.1017/CBO9780511810633.  Google Scholar

[22]

S. PangF. Deng and X. Mao, Asymptotic properties of stochastic population dynamics, Dynamics of Continuous Discrete and Impulsive Systems Series A: Mathematical Analysis, 15 (2008), 603-620.   Google Scholar

[23]

M. Slatkin, The dynamics of a population in a Markovian environment, Ecology, 59 (1978), 249-256.  doi: 10.2307/1936370.  Google Scholar

[24]

L. S. Tsimring, Noise in biology, IOP Publishing, 77 (2014), 026601. doi: 10.1088/0034-4885/77/2/026601.  Google Scholar

[25]

Y. TakeuchiN. H. DuN. T. Hieu and K. Sato, Evolution of predator–prey systems described by a Lotka–Volterra equation under random environment, Journal of Mathematical Analysis and Applications, 323 (2006), 938-957.  doi: 10.1016/j.jmaa.2005.11.009.  Google Scholar

[26]

D. A. Vasseur and P. Yodzis, The color of environmental noise, Wiley Online Library, 85 (2004), 1146-1152.   Google Scholar

[27]

G. G. Yin and Q. Zhang, Continuous-Time Markov Chains and Applications: A Singular Perturbation Approach, Springer, 37, 2012. doi: 10.1007/978-1-4614-4346-9.  Google Scholar

[28]

C. Zhu and G. Yin, Asymptotic properties of hybrid diffusion systems, SIAM Journal on Control and Optimization, 46 (2007), 1155-1179.  doi: 10.1137/060649343.  Google Scholar

Figure 1.  Extinction with $ I(0) = 90 $
Figure 2.  Extinction with $ I(0) = 10 $
Figure 3.  Persistence Case 1 with $ I(0) = 90 $
Figure 4.  Persistence Case 1 with $ I(0) = 10 $
Figure 5.  Persistence Case 2 with $ I(0) = 90 $
Figure 6.  Persistence Case 2 with $ I(0) = 10 $
Figure 7.  Stationary Distribution Case 1 with $ I(0) = 90 $
Figure 8.  Stationary Distribution Case 1 with $ I(0) = 10 $
Figure 9.  Stationary Distribution Case 2 with $ I(0) = 90 $
Figure 10.  Stationary Distribution Case 2 with $ I(0) = 10 $
[1]

Nalin Fonseka, Jerome Goddard II, Ratnasingham Shivaji, Byungjae Son. A diffusive weak Allee effect model with U-shaped emigration and matrix hostility. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020356

[2]

Yu Zhou, Xinfeng Dong, Yongzhuang Wei, Fengrong Zhang. A note on the Signal-to-noise ratio of $ (n, m) $-functions. Advances in Mathematics of Communications, 2020  doi: 10.3934/amc.2020117

[3]

Hui Lv, Xing'an Wang. Dissipative control for uncertain singular markovian jump systems via hybrid impulsive control. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 127-142. doi: 10.3934/naco.2020020

[4]

Peizhao Yu, Guoshan Zhang, Yi Zhang. Decoupling of cubic polynomial matrix systems. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 13-26. doi: 10.3934/naco.2020012

[5]

Shengxin Zhu, Tongxiang Gu, Xingping Liu. AIMS: Average information matrix splitting. Mathematical Foundations of Computing, 2020, 3 (4) : 301-308. doi: 10.3934/mfc.2020012

[6]

Chandra Shekhar, Amit Kumar, Shreekant Varshney, Sherif Ibrahim Ammar. $ \bf{M/G/1} $ fault-tolerant machining system with imperfection. Journal of Industrial & Management Optimization, 2021, 17 (1) : 1-28. doi: 10.3934/jimo.2019096

[7]

Parikshit Upadhyaya, Elias Jarlebring, Emanuel H. Rubensson. A density matrix approach to the convergence of the self-consistent field iteration. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 99-115. doi: 10.3934/naco.2020018

[8]

S. Sadeghi, H. Jafari, S. Nemati. Solving fractional Advection-diffusion equation using Genocchi operational matrix based on Atangana-Baleanu derivative. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020435

[9]

Yuri Fedorov, Božidar Jovanović. Continuous and discrete Neumann systems on Stiefel varieties as matrix generalizations of the Jacobi–Mumford systems. Discrete & Continuous Dynamical Systems - A, 2020  doi: 10.3934/dcds.2020375

[10]

Sihem Guerarra. Maximum and minimum ranks and inertias of the Hermitian parts of the least rank solution of the matrix equation AXB = C. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 75-86. doi: 10.3934/naco.2020016

[11]

Dan Zhu, Rosemary A. Renaut, Hongwei Li, Tianyou Liu. Fast non-convex low-rank matrix decomposition for separation of potential field data using minimal memory. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2020076

[12]

Laurence Cherfils, Stefania Gatti, Alain Miranville, Rémy Guillevin. Analysis of a model for tumor growth and lactate exchanges in a glioma. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020457

[13]

Laurent Di Menza, Virginie Joanne-Fabre. An age group model for the study of a population of trees. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020464

[14]

Eduard Feireisl, Elisabetta Rocca, Giulio Schimperna, Arghir Zarnescu. Weak sequential stability for a nonlinear model of nematic electrolytes. Discrete & Continuous Dynamical Systems - S, 2021, 14 (1) : 219-241. doi: 10.3934/dcdss.2020366

[15]

Weiwei Liu, Jinliang Wang, Yuming Chen. Threshold dynamics of a delayed nonlocal reaction-diffusion cholera model. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020316

[16]

Yining Cao, Chuck Jia, Roger Temam, Joseph Tribbia. Mathematical analysis of a cloud resolving model including the ice microphysics. Discrete & Continuous Dynamical Systems - A, 2021, 41 (1) : 131-167. doi: 10.3934/dcds.2020219

[17]

Zhouchao Wei, Wei Zhang, Irene Moroz, Nikolay V. Kuznetsov. Codimension one and two bifurcations in Cattaneo-Christov heat flux model. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020344

[18]

Shuang Chen, Jinqiao Duan, Ji Li. Effective reduction of a three-dimensional circadian oscillator model. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020349

[19]

Barbora Benešová, Miroslav Frost, Lukáš Kadeřávek, Tomáš Roubíček, Petr Sedlák. An experimentally-fitted thermodynamical constitutive model for polycrystalline shape memory alloys. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020459

[20]

Cuicui Li, Lin Zhou, Zhidong Teng, Buyu Wen. The threshold dynamics of a discrete-time echinococcosis transmission model. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020339

2019 Impact Factor: 1.27

Metrics

  • PDF downloads (17)
  • HTML views (30)
  • Cited by (0)

Other articles
by authors

[Back to Top]