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Many epidemic models are described by the ordinary differential equations.
As is well known, the bilinear incidence rate (beta SI) is frequently used in many epidemic models [1 5].
Researchers have proposed many epidemic models to understand the mechanism of disease transmission (see [1 6] and the references therein).
To better understand the transmission pattern of infectious disease, a great many epidemic models have been formulated (see [1 21] and references therein).
In order to predict the spreading of infectious diseases, many epidemic models have been proposed and analyzed in recent years (see [1 13]).
In recent years, many epidemic models on complex networks, such as SIS (susceptible-infected-susceptible) [1 8] and SIR (susceptible-infected-removed) [9 16] and so on, have been widely studied by researchers from different subjects.
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For many deterministic epidemic models, an infection can get started in a fully susceptible population if and only if R0>1.
Oscillations in incidence were predicted in many previous epidemic models with different underlying mechanisms, for example, in one-strain SIR models [ 4, 8– 11], two-strain SIR models [ 68], and two-strain SIRS models [ 12] where the seasonal forcing was thought to be the mechanism.
A stage-structured model, a discrete epidemic model with seasonal variation in environment, a discrete tuberculosis transmission model, and many other discrete epidemic models have been constructed, studied, and applied [8 10].
There are also many results on the epidemic models with jumps [19 22].
Many authors have studied the epidemic models, which display the dynamical behavior of the transmission of infected disease.
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