March  2016, 21(2): 721-736. doi: 10.3934/dcdsb.2016.21.721

Stochastic epidemic models driven by stochastic algorithms with constant step

1. 

Mathematics and Science College, Shanghai Normal University, Shanghai 200234, China

Received  December 2014 Revised  July 2015 Published  December 2015

In this paper we propose the stochastic epidemic model that relates directly to the deterministic counterpart and reveal close connections between these two models. Under the classic assumptions, the sample path of process eventually converges to the disease-free equilibrium, even though the corresponding deterministic flow converges to an endemic equilibrium. From the fact that disease can occur sporadically, we adjust the stochastic model slightly by introducing a stochastic incidence and establish precise connections between the long-run behavior of the discrete stochastic process and its deterministic flow approximation for large populations.
Citation: Lifeng Chen, Jifa Jiang. Stochastic epidemic models driven by stochastic algorithms with constant step. Discrete & Continuous Dynamical Systems - B, 2016, 21 (2) : 721-736. doi: 10.3934/dcdsb.2016.21.721
References:
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M. Benaïm, Recursive algorithms, urn processes and chaining number of chain recurrent sets,, Ergodic Theory Dynam. Systems, 18 (1998), 53. doi: 10.1017/S0143385798097557. Google Scholar

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M. Benaïm, Dynamics of stochastic approximation algorithms,, Séminaire de Probabilités, (1709), 1. doi: 10.1007/BFb0096509. Google Scholar

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M. Benaïm and M. W. Hirsch, Stochastic approximation algorithms with constant step size whose average is cooperative,, Ann. Appl. Probab., 9 (1999), 216. doi: 10.1214/aoap/1029962603. Google Scholar

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M. Benaïm and M. W. Hirsch, Differential and stochastic epidemic models,, Fields Inst. Commun., 21 (1999), 31. Google Scholar

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M. Y. Li and C. Xu, Limit threshold theorems for stochastic epidemic models,, preprint, (2014). Google Scholar

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show all references

References:
[1]

M. Benaïm, Recursive algorithms, urn processes and chaining number of chain recurrent sets,, Ergodic Theory Dynam. Systems, 18 (1998), 53. doi: 10.1017/S0143385798097557. Google Scholar

[2]

M. Benaïm, Dynamics of stochastic approximation algorithms,, Séminaire de Probabilités, (1709), 1. doi: 10.1007/BFb0096509. Google Scholar

[3]

M. Benaïm and M. W. Hirsch, Stochastic approximation algorithms with constant step size whose average is cooperative,, Ann. Appl. Probab., 9 (1999), 216. doi: 10.1214/aoap/1029962603. Google Scholar

[4]

M. Benaïm and M. W. Hirsch, Differential and stochastic epidemic models,, Fields Inst. Commun., 21 (1999), 31. Google Scholar

[5]

R. Bowen, $\omega$-limit sets of Axiom A diffeomorphisms,, J. Differential Equations, 18 (1975), 333. doi: 10.1016/0022-0396(75)90065-0. Google Scholar

[6]

D. L. Burkholder, B. J. Davis and R. F. Gundy, Integral inequalities for convex functions of operators on martingales,, Proc. 6th Berkeley Symp. Math. Statis. and Probab., 2 (1972), 223. Google Scholar

[7]

C. C. Conley, Isolated Invariant Sets and the Morse Index, CBMS Regional Conference Series in Mathematics., American Mathematical Society, (1978). Google Scholar

[8]

J. C. Fort and G. Pagès, Asymptotic behavior of a Markovian stochastic algorithm with constant step,, SIAM J. Control Optim., 37 (1999), 1456. doi: 10.1137/S0363012997328610. Google Scholar

[9]

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

[10]

O. Hernández-Lerma and J. B. Lasserre, Markov Chains and Invariant Probabilities,, Birkhäuser Verlag, (2003). doi: 10.1007/978-3-0348-8024-4. Google Scholar

[11]

M. Y. Li and C. Xu, Limit threshold theorems for stochastic epidemic models,, preprint, (2014). Google Scholar

[12]

R. Mañé, Ergodic Theory and Differentiable Dynamics,, Springer-Verlag, (1987). doi: 10.1007/978-3-642-70335-5. Google Scholar

[13]

A. Lajmanovich and J. Yorke, A deterministic model for gonorrhea in a nonhomogeneous population,, Math. Biosci., 28 (1976), 221. doi: 10.1016/0025-5564(76)90125-5. Google Scholar

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