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2015, 20(9): 2933-2947. doi: 10.3934/dcdsb.2015.20.2933

Persistence-time estimation for some stochastic SIS epidemic models

1. 

Department of Applied Mathematics and Statistics and Operations Research, University of the Basque Country UPV/EHU, Spain, Spain

2. 

Department of Di erential Equations and Numerical Analysis, University of Sevilla, Spain

Received  July 2014 Revised  July 2015 Published  September 2015

In this paper, we study two stochastic SIS epidemic models: the first one with a constant population size, and the second one with a death factor. We analyze persistence and extinction behaviors for these models. The persistence time depends on the initial population size and satisfies a stationary backward Kolmogorov differential equation, which is a linear second-order partial differential equation with variable degenerate coefficients. We solve this equation numerically using a classical finite element method. We give computational evidence that the importance of understanding the dynamics of both the deterministic and the stochastic epidemic models is due to the numerical approximations to the mean persistence time. This can give more information about the model and may perhaps explain strange behaviors, such as the differences between the deterministic model and the stochastic one for long times.
Citation: Francisco de la Hoz, Anna Doubova, Fernando Vadillo. Persistence-time estimation for some stochastic SIS epidemic models. Discrete & Continuous Dynamical Systems - B, 2015, 20 (9) : 2933-2947. doi: 10.3934/dcdsb.2015.20.2933
References:
[1]

E. Allen, Modeling with Itô Stochastic Differential Equations,, Springer, (2007).

[2]

E. Allen, L. Allen and H. Schurz, A comparison of persistence-time estimation for discrete and continuous stochastic population models that include demographic and environmental variability,, Mathematical Biosciences, 196 (2005), 14. doi: 10.1016/j.mbs.2005.03.010.

[3]

L. Allen, An Introduction to Stochastic Processes with Applications to Biology,, Person Prentice Hall, (2003).

[4]

L. Allen and E. Allen, A comparison of three different stochastic population models with regard to persistence time,, Theoretical Population Biology, 64 (2003), 439. doi: 10.1016/S0040-5809(03)00104-7.

[5]

L. Allen and A. Burgin, Comparison of deterministic and stochastic SIS and SIR models in discrete time,, Mathematical Biosciences, 163 (2000), 1. doi: 10.1016/S0025-5564(99)00047-4.

[6]

X. Antoine, A. Arnold, C. Besse, M. Ehrhardt and A. Schädle, A review of transparent and artificial boundary conditions techniques for linear and nonlinear Schrödinger equations,, Commun. Comput. Phys., 4 (2008), 729.

[7]

E. Becker, G. Caray and J. Oden, Finite Elements. Vol. 1, 2, 3 and 4,, Prentice-Hall, (): 1981.

[8]

P. Ciarlet and J. Lions, Handbook of Numerical Analysis Vol. II, Finite Element Methods,, North-Holland, (1991).

[9]

F. de la Hoz and F. Vadillo, A mean extinction-time estimate for a stochastic Lotka-Volterra predator-prey model,, Applied Mathematics and Computation, 219 (2012), 170. doi: 10.1016/j.amc.2012.05.060.

[10]

C. Doering, K. Sargsyan and L. Sander, Extinction times for birth-death processes: Exact results, continuum asymptotics, and the failure of the Fokker-Planck approximation,, Multiscale Model. Simul., 3 (2005), 283. doi: 10.1137/030602800.

[11]

M. Gockenbach, Understanding and Implementing the Finite Element Method,, SIAM, (2006). doi: 10.1137/1.9780898717846.

[12]

F. Hecht, New development in FreeFem++,, J. Numer. Math., 20 (2012), 251.

[13]

D. Higham, X. Mao and A. Stuarts, Strong convergence of euler-type methods for nonlinear stochastic differential equations,, SIAM J. Numer. Anal., 40 (2002), 1041. doi: 10.1137/S0036142901389530.

[14]

C. Johnson, Numerical Solution of Partial Differential Equations by the Finite Element Method,, Cambrigde University Press, (1987).

[15]

P. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations,, Applications of Mathematics, (1992). doi: 10.1007/978-3-662-12616-5.

[16]

R. Kryscio and C. Lefèvre, On the extinction of the S-I-S stochastic logistic epidemic,, J. Appl. Prob., 26 (1989), 685. doi: 10.2307/3214374.

[17]

M. Liu and K. Wanga, Persistence and extinction in stochastic non-autonomous logistic systems,, J. Math. Anal. Appl., 375 (2011), 443. doi: 10.1016/j.jmaa.2010.09.058.

[18]

J. Mena-Lorca and H. Hethcote, Dynamic models of infectious diseases as regulators of population sizes,, J. Math. Biol., 30 (1992), 693. doi: 10.1007/BF00173264.

[19]

I. Nåsell, Stochastic models of some endemic infections,, Mathematical Biosciences, 179 (2002), 1. doi: 10.1016/S0025-5564(02)00098-6.

[20]

R. Norden, On the distribution of the time to extinction in the stochastic logistic population model,, Adv. Appl. Prob., 14 (1982), 687. doi: 10.2307/1427019.

[21]

L. Trefethen, Spectral Methods in MATLAB,, SIAM, (2000). doi: 10.1137/1.9780898719598.

show all references

References:
[1]

E. Allen, Modeling with Itô Stochastic Differential Equations,, Springer, (2007).

[2]

E. Allen, L. Allen and H. Schurz, A comparison of persistence-time estimation for discrete and continuous stochastic population models that include demographic and environmental variability,, Mathematical Biosciences, 196 (2005), 14. doi: 10.1016/j.mbs.2005.03.010.

[3]

L. Allen, An Introduction to Stochastic Processes with Applications to Biology,, Person Prentice Hall, (2003).

[4]

L. Allen and E. Allen, A comparison of three different stochastic population models with regard to persistence time,, Theoretical Population Biology, 64 (2003), 439. doi: 10.1016/S0040-5809(03)00104-7.

[5]

L. Allen and A. Burgin, Comparison of deterministic and stochastic SIS and SIR models in discrete time,, Mathematical Biosciences, 163 (2000), 1. doi: 10.1016/S0025-5564(99)00047-4.

[6]

X. Antoine, A. Arnold, C. Besse, M. Ehrhardt and A. Schädle, A review of transparent and artificial boundary conditions techniques for linear and nonlinear Schrödinger equations,, Commun. Comput. Phys., 4 (2008), 729.

[7]

E. Becker, G. Caray and J. Oden, Finite Elements. Vol. 1, 2, 3 and 4,, Prentice-Hall, (): 1981.

[8]

P. Ciarlet and J. Lions, Handbook of Numerical Analysis Vol. II, Finite Element Methods,, North-Holland, (1991).

[9]

F. de la Hoz and F. Vadillo, A mean extinction-time estimate for a stochastic Lotka-Volterra predator-prey model,, Applied Mathematics and Computation, 219 (2012), 170. doi: 10.1016/j.amc.2012.05.060.

[10]

C. Doering, K. Sargsyan and L. Sander, Extinction times for birth-death processes: Exact results, continuum asymptotics, and the failure of the Fokker-Planck approximation,, Multiscale Model. Simul., 3 (2005), 283. doi: 10.1137/030602800.

[11]

M. Gockenbach, Understanding and Implementing the Finite Element Method,, SIAM, (2006). doi: 10.1137/1.9780898717846.

[12]

F. Hecht, New development in FreeFem++,, J. Numer. Math., 20 (2012), 251.

[13]

D. Higham, X. Mao and A. Stuarts, Strong convergence of euler-type methods for nonlinear stochastic differential equations,, SIAM J. Numer. Anal., 40 (2002), 1041. doi: 10.1137/S0036142901389530.

[14]

C. Johnson, Numerical Solution of Partial Differential Equations by the Finite Element Method,, Cambrigde University Press, (1987).

[15]

P. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations,, Applications of Mathematics, (1992). doi: 10.1007/978-3-662-12616-5.

[16]

R. Kryscio and C. Lefèvre, On the extinction of the S-I-S stochastic logistic epidemic,, J. Appl. Prob., 26 (1989), 685. doi: 10.2307/3214374.

[17]

M. Liu and K. Wanga, Persistence and extinction in stochastic non-autonomous logistic systems,, J. Math. Anal. Appl., 375 (2011), 443. doi: 10.1016/j.jmaa.2010.09.058.

[18]

J. Mena-Lorca and H. Hethcote, Dynamic models of infectious diseases as regulators of population sizes,, J. Math. Biol., 30 (1992), 693. doi: 10.1007/BF00173264.

[19]

I. Nåsell, Stochastic models of some endemic infections,, Mathematical Biosciences, 179 (2002), 1. doi: 10.1016/S0025-5564(02)00098-6.

[20]

R. Norden, On the distribution of the time to extinction in the stochastic logistic population model,, Adv. Appl. Prob., 14 (1982), 687. doi: 10.2307/1427019.

[21]

L. Trefethen, Spectral Methods in MATLAB,, SIAM, (2000). doi: 10.1137/1.9780898719598.

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