Dynamics analysis and numerical simulations of a delayed stochastic epidemic model subject to a general response function

This paper proposes a new delayed stochastic epidemic model with double epidemic hypothesis and a general nonlinear response function. The main purpose is to explore the effects of environmental noise and the general nonlinear response function on stochastic dynamics. By constructing appropriate Lya...

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Vydané v:Computational & applied mathematics Ročník 38; číslo 2; s. 1 - 30
Hlavní autori: Li, Fei, Zhang, Shengqiang, Meng, Xinzhu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.06.2019
Springer Nature B.V
Predmet:
Applications of Mathematics
Applied physics
Asymptotic properties
Background noise
Computational mathematics
Computational Mathematics and Numerical Analysis
Computer simulation
Economic models
Epidemics
Liapunov functions
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematical models
Mathematics
Mathematics and Statistics
Nonlinear response
Response functions
Simulation
Stochastic models
Time delays
Numerical simulations
Global asymptotics
65C30
34E10
92B05
Nonlinear incidence rate
Stochastic delayed epidemic model
ISSN:2238-3603, 1807-0302
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Shrnutí:This paper proposes a new delayed stochastic epidemic model with double epidemic hypothesis and a general nonlinear response function. The main purpose is to explore the effects of environmental noise and the general nonlinear response function on stochastic dynamics. By constructing appropriate Lyapunov functions and using some novel differential inequality techniques, we first investigate the long-time asymptotic properties of the stochastic delayed system. Moreover, the threshold conditions for the persistence in mean are established. At last, we carry out a series of numerical simulations to illustrate the performance of the theoretical results. The developed theoretical methods and stochastic inequalities techniques can be applied to explore stochastic differential systems with the general response function.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2238-3603
1807-0302
DOI:10.1007/s40314-019-0857-x