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# Dynamical behaviors of an SIRI epidemic model with nonlinear incidence and latent period

- Peng Guo
^{1, 2}, - Xinsong Yang
^{1}and - Zhichun Yang
^{1}Email author

**2014**:164

https://doi.org/10.1186/1687-1847-2014-164

© Guo et al.; licensee Springer. 2014

**Received:**27 January 2014**Accepted:**14 May 2014**Published:**4 June 2014

## Abstract

In this paper, we study an SIRI epidemic model with nonlinear incidence rate and latent period, namely $\frac{kI(t-\tau )S}{1+\alpha {I}^{h}(t-\tau )}$, which describes the psychological effect of certain serious diseases on the community when the size of the set of infective individuals is getting larger. We first obtain the threshold dynamics on the global stability of the equilibria for the model without latent period, and then we analyze the stability and Hopf bifurcation for the model with the latent period. The results show the influence of nonlinear incidence rate and latent period on the dynamical behaviors of the SIRI model. The examples and its simulations are given to illustrate the obtained results.

## Keywords

- SIRI model
- stability
- nonlinear incidence rate
- Hopf bifurcation

## 1 Introduction

An SIRI epidemiological model in a population was formulated by Tudor [1], which consists of a system with three compartments: susceptible, infective, and removed individuals, labeled by *S*, *I*, *R*. In this model, susceptibles become infectious, then they are removed with temporary immunity, and finally they become infectious again. The SIRI model is appropriate for some diseases such as human and bovine tuberculosis, and herpes [2–4], in which recovered individuals may revert back to the infective class due to reactivation of the latent infection or incomplete treatment. Some authors have studied this type of SIRI epidemic model to understand the principle of disease transmissions. Moreira and Wang [5] studied an SIRI model with general saturated incidence rate $\phi (S)I$ and derived sufficient conditions for the global asymptotic stability of the disease-free and endemic equilibria of the model by using an elementary analysis of Lienard’s equation and Lyapunov’s direct method. In 2007, van den Driessche *et al.* [6] formulated an SEIRI model with standard incidence rate and distributed delay for a disease with an exposed (latent) period and relapse, and they investigated its threshold property by the obtained basic reproduction number. However, the qualitative analysis of an SIRI epidemic model with nonlinear incidence rates in the form of $\frac{k{I}^{p}S}{1+\alpha {I}^{q}}$ was not investigated.

In 1978, Capasso and Serio [7] introduced a saturated incidence rate $\frac{kI}{1+\alpha I}$ by research of the cholera epidemic spread in Bari. Ruan and Wang [8] studied an epidemic model with a particular nonlinear incident rate $\frac{k{I}^{2}S}{1+\alpha {I}^{2}}$ and provided a detailed qualitative analysis on the Hopf bifurcation and Bogdanov-Takens bifurcation for the model. Xiao and Ruan [9] studied nonlinear incidence rates of $\frac{kIS}{1+\alpha {I}^{2}}$. Yang and Xiao [10] investigated the general nonlinear incidence rate $\frac{kIS}{1+\alpha {I}^{h}}$. Recently, a more general incidence rate $\frac{k{I}^{p}S}{1+\alpha {I}^{q}}$ was investigated by Liu *et al.* [11], Derrick and van den Driessche [12], *etc.* In addition, for many infectious diseases, an individual once infected is unable to immediately infect another, and he undergoes a certain latent period before it can infect others. Mathematically, this corresponds to the delay effect of the infection (see also [10, 13, 14]). Therefore, it is interesting to investigate the nonlinear incidence rates and the latent period.

Our aim is to investigate an SIRI epidemic model with temporary immunity, nonlinear incidence rate, and latent period. The organization of this paper is as follows. In Section 2, we give the description of the SIRI model, and we discuss the existence of equilibria of the model by the obtained basic reproduction number ${\mathcal{R}}_{0}$. In Section 3, we obtain the threshold dynamics on stability for the model without the latent period (*i.e.*, $\tau =0$). Furthermore, we study the stability and Hopf bifurcation for the model with $\tau >0$ in Section 4. From these results, it can be seen that the nonlinear incidence rate and latent period influence the dynamical behaviors of the SIRI model. In Section 5, we give some conclusions and a discussion of the influence, and some illustrative examples and simulations also are provided.

## 2 Model description and preliminaries

*kI*represents the infection force of the disease and $\frac{1}{1+\alpha {I}^{h}}$ represents the inhibition effect from the behavioral change of the susceptible individuals when their number increases or from the crowding effect of the infective individuals. Furthermore, assume that the latent period is a constant

*τ*, that is, a susceptible becomes infective if he contacted the infected

*τ*times ago. Therefore, delay can be incorporated into the nonlinear incidence function as follows:

where *b* is the birth rate of the population, *d* is the natural death rate of the population, *k* is the proportionality constant, *μ* is the rate at which infected individuals become temporarily immune, *γ* is the rate at which the recovered class revert to the infective class, *α* is the saturated parameter, and *b*, *d*, *k*, *α*, *γ*, *μ*, *h* are positive parameters.

Following the idea in [8, 11, 15], we shall mainly study the existence, uniqueness, and stability of equilibria and Hopf bifurcation of the limit Eq. (3) to obtain the dynamics of Eq. (1) (see also [9, 16, 17]).

where $({\phi}_{1},{\phi}_{2})\in C$, and $C=C([-\tau ,0],{\mathbb{R}}_{+}^{2})$ represents the continuous function space on $[-\tau ,0]$ with the norm $\parallel \phi \parallel ={sup}_{-\tau \le \theta \le 0}|\phi (\theta )|$, $\phi \in C$. From the literature on retarded differential equations or functional differential equations in [18, 19], there is a unique local solution of Eq. (3) for all allowable positive parameters.

Firstly, we have the following conclusions.

**Lemma 2.1** *The region* ${S}_{\mathrm{\u25b3}}=\{(I,R)|I\ge 0,R\ge 0,I+R\le \frac{b}{d}\}$ *is an invariant set and also an absorbing set of Eq*. (3) *in the first quadrant*.

*Proof*From (3), we can see that on the line $I=0$, $\frac{dI}{dt}>0$, and on the line $R=0$, $\frac{dR}{dt}>0$. Hence, no orbit of Eq. (3) can exit from the first quadrant, with the boundary $R=0$ and $I=0$. From (2), we have

the orbit of Eq. (3) getting at the boundary $I+R=\frac{b}{d}$ must go into the interior of ${S}_{\mathrm{\u25b3}}$. Thus, the region ${S}_{\mathrm{\u25b3}}$ is an absorbing set of Eq. (3) in the first quadrant. This completes the proof. □

In terms of ${\mathcal{R}}_{0}$, we obtain the following result on the existence of the equilibrium for Eq. (3).

**Lemma 2.2**(i)

*If*${\mathcal{R}}_{0}\le 1$,

*then Eq*. (3)

*has a unique equilibrium*${E}_{0}=(0,0)$

*in the first quadrant*.

- (ii)
*If*${\mathcal{R}}_{0}>1$,*then Eq*. (3)*has two equilibria in the first quadrant*,*which are*${E}_{0}$*and*${E}_{\ast}=({I}_{\ast},{R}_{\ast})$,*where*${I}_{\ast},{R}_{\ast}>0$.

*Proof*Clearly, Eq. (3) always has an equilibrium ${E}_{0}=(0,0)$, and it has no other boundary equilibrium in the first quadrant from the second equation of (3). Furthermore, Eq. (3) has a positive equilibrium ${E}_{\ast}=({I}_{\ast},{R}_{\ast})$ if and only if it satisfies

When ${\mathcal{R}}_{0}\le 1$, Eq. (7) has no positive solution since $\frac{\gamma \mu}{\gamma +d}-(d+\mu )<0$. So, the conclusion in case (i) holds.

*φ*is strictly monotone decreasing for $I\ge 0$. Also,

This implies that Eq. (7) has one unique positive solution and the conclusion in case (ii) holds. Therefore, we have completed the proof. □

As usual, ${E}_{0}$ and ${E}_{\ast}$ are called the disease-free equilibrium (DFE) and the endemic equilibrium (EE) of Eq. (3), respectively. In what follows, we shall mainly discuss dynamical behaviors on the stability of two equilibria and Hopf bifurcation for Eq. (3) with or without the latent period.

## 3 Threshold dynamics for the case $\tau =0$

We first give the local stability and topological structure of the equilibria.

**Lemma 3.1**(i)

*If*${\mathcal{R}}_{0}<1$,

*then the DFE*${E}_{0}$

*of Eq*. (8)

*is a locally asymptotically stable hyperbolic node*.

- (ii)
*If*${\mathcal{R}}_{0}=1$,*then DFE*${E}_{0}$*of Eq*. (8)*is a saddle*-*node and is locally asymptotically stable in the first quadrant*. - (iii)
*If*${\mathcal{R}}_{0}>1$,*then the DFE*${E}_{0}$*of Eq*. (8)*is an unstable saddle and the EE*${E}_{\ast}$*is a locally asymptotically stable hyperbolic node*.

*Proof*The Jacobian matrix of Eq. (8) at ${E}_{0}=(0,0)$ is

Then the DFE ${E}_{0}$ of Eq. (8) is locally asymptotically stable if ${\mathcal{R}}_{0}<1$.

and this has a unique point ${E}_{0}=(0,0)$. It follows from the Lasalle invariant principle that the DFE ${E}_{0}$ is asymptotically stable when ${\mathcal{R}}_{0}=1$.

This implies that the endemic equilibrium ${E}_{\ast}$ is a locally asymptotically stable hyperbolic node. The proof is completed. □

Furthermore, we discuss the topological structure of the trajectories of Eq. (8).

**Lemma 3.2** *Equation* (8) *has neither a nontrivial periodic orbit nor a singular closed trajectory including a finite number of equilibria in* ${S}_{\mathrm{\u25b3}}$.

*Proof*Let

By the Bendixson-Dulac criterion [22], we know that Eq. (8) does not have nontrivial periodic orbits or a singular closed trajectory which contains a finite number of equilibria. The proof is completed. □

Then we show that there is no positive half-trajectory close to ${E}_{0}$ in ${S}_{\mathrm{\u25b3}}$ if ${\mathcal{R}}_{0}>1$.

**Lemma 3.3** *The two stable manifolds of saddle* ${E}_{0}$ *are not in* ${S}_{\mathrm{\u25b3}}$ *if* ${\mathcal{R}}_{0}>1$.

*Proof*Suppose there is a stable manifold ${L}_{P}^{+}$ in ${S}_{\mathrm{\u25b3}}$, then the ${L}_{P}^{-}$ stay in the ${S}_{\mathrm{\u25b3}}$ or traverse through ${S}_{\mathrm{\u25b3}}$. Assuming the former case, we know that the ${L}_{P}^{-}$ satisfy one of the following cases (see [22]):

- (i)
${A}_{P}=\{{E}_{\ast}\}$,

- (ii)
${A}_{P}=\{{E}_{0}\}$, ${A}_{P}$ is a closed orbit, or ${A}_{P}$ is a singular closed trajectory,

where ${A}_{P}$ is negative limit set of ${L}_{P}$. Case (i) and Lemma 3.1 lead to a contradiction, and case (ii) and Lemma 3.2 lead to a contradiction.

If the ${L}_{P}^{-}$ ran out of the region ${S}_{\mathrm{\u25b3}}$, it is divided into two parts to ${S}_{\mathrm{\u25b3}}$ (if the two stable manifolds are all in ${S}_{\mathrm{\u25b3}}$, and all passed through this region ${S}_{\mathrm{\u25b3}}$, they are divided into three parts of ${S}_{\mathrm{\u25b3}}$) since $I=0$ or $R=0$ is not an orbit of Eq. (8). Then the equilibrium ${E}_{\ast}$ is not in one part. In this part, we arbitrarily select a point $Q\ne O$ and *Q* is not in any of the stable manifolds, then the positive half orbit through the point *Q* will run out of this part or ${\mathrm{\Omega}}_{Q}$ is a closed orbit or a singular closed trajectory, where ${\mathrm{\Omega}}_{Q}$ is a positive limit set of ${L}_{Q}$. This conflicts with the topological structure of a saddle or Lemma 2.1 or Lemma 3.2. This completes the proof. □

On the basis of Lemmas 3.1, 3.2, and 3.3, we have the following.

**Theorem 3.1**(i)

*If*${\mathcal{R}}_{0}<1$,

*then Eq*. (8)

*has a unique DFE*${E}_{0}$,

*which is a globally asymptotically stable hyperbolic node in the first quadrant*.

- (ii)
*If*${\mathcal{R}}_{0}=1$,*then Eq*. (8)*has a unique DFE*${E}_{0}$,*which is a saddle*-*node and is globally asymptotically stable in the first quadrant*. - (iii)
*If*${\mathcal{R}}_{0}>1$,*then Eq*. (8)*has one unique DFE*${E}_{0}$*and a unique EE*${E}_{\ast}$,*and*${E}_{0}$*is a unstable saddle and*${E}_{\ast}$*is a globally asymptotically stable hyperbolic node in the first quadrant*.

*Proof* If ${\mathcal{R}}_{0}\le 1$, ${S}_{\mathrm{\u25b3}}$ has a unique disease-free equilibrium. Obviously, ${S}_{\mathrm{\u25b3}}$ is a bounded absorbing region which contains exactly one critical point ${E}_{0}$. For any point *P* in ${S}_{\mathrm{\u25b3}}$, according to [22], its Omega-limit set satisfies ${\mathrm{\Omega}}_{P}=\{{E}_{0}\}$ or ${\mathrm{\Omega}}_{P}$ is a closed orbit or ${\mathrm{\Omega}}_{P}$ is a singular closed trajectory. Lemma 3.2 essentially means that there is no closed orbit or singular closed trajectory in ${S}_{\mathrm{\u25b3}}$. So we have ${\mathrm{\Omega}}_{P}=\{{E}_{0}\}$. This proves that ${E}_{0}$ is a global attractor in ${S}_{\mathrm{\u25b3}}$.

From Lemma 3.1 and Lemma 3.3, ${E}_{0}$ is an unstable saddle in ${S}_{\mathrm{\u25b3}}$ and its two stable manifolds are not in ${S}_{\mathrm{\u25b3}}$ if ${\mathcal{R}}_{0}>1$. By a similar proof, we have ${\mathrm{\Omega}}_{P}=\{{E}_{\ast}\}$, where *P* is an arbitrary point in ${S}_{\mathrm{\u25b3}}-\{{E}_{0}\}$. This proves that ${E}_{\ast}$ is a global attractor in ${S}_{\mathrm{\u25b3}}$.

It follows from Lemma 2.1 that ${S}_{\mathrm{\u25b3}}$ is a absorbing set in the first quadrant. Thus, ${E}_{0}$ in case (i) and case (ii) and ${E}_{\ast}$ in case (iii) is a global attractor in the first quadrant.

Combining with the local stability in Lemma 3.1, we obtain the conclusion. This completes the proof. □

From the above theorem, we can see that ${\mathcal{R}}_{0}$ plays a threshold role in determining the stability of Eq. (8).

## 4 Dynamical behaviors for the case $\tau >0$

In this section, we shall study the stability and Hopf bifurcation of the equilibria for Eq. (3) with the latent period $\tau >0$.

The following result shows that ${\mathcal{R}}_{0}$ is a threshold value for the DFE.

**Theorem 4.1** *If* ${\mathcal{R}}_{0}\le 1$, *the DFE* ${E}_{0}$ *of Eq*. (3) *is globally asymptotically stable*. *If* ${\mathcal{R}}_{0}>1$, *the DFE is unstable*.

*Proof*We firstly give the local stability of DFE ${E}_{0}=(0,0)$. Linearize Eq. (3) at ${E}_{0}$ and obtain the characteristic equation

*E*is the unit matrix and

*i.e.*

*ω*is a positive real number. Then we have

If ${\mathcal{R}}_{0}<1$, then ${D}_{1}>0$, ${D}_{2}>0$. Accordingly, Eq. (10) does not have any real root and all roots of Eq. (3) have negative real parts for any $\tau >0$ if ${\mathcal{R}}_{0}<1$.

By a similar analysis, we derive that Eq. (9) has a unique zero root and all other roots with negative real parts for any $\tau >0$ if ${\mathcal{R}}_{0}=1$. Thus, ${E}_{0}$ is locally asymptotically stable if ${\mathcal{R}}_{0}\le 1$ for any $\tau >0$.

If ${\mathcal{R}}_{0}>1$, Eq. (9) has a root with the positive real part for any $\tau >0$. Thus, the DFE is unstable.

has a unique point ${E}_{0}=(0,0)$. It follows from the Lyapunov-Lasalle invariant principle in [18, 23] that the DFE ${E}_{0}$ is globally asymptotically stable. This completes the proof. □

*h*and

*τ*. To do this, we calculate the linearization of Eq. (3) at ${E}_{\ast}$ and obtain the characteristic equation

*E*is the unit matrix and

*ω*satisfies

When ${\mathcal{R}}_{0}>1$ and $0<h\le 2$, we have ${\mathrm{\Lambda}}_{2}$, ${\mathrm{\Lambda}}_{3}$, ${D}_{3}^{2}-2{D}_{4}-{D}_{6}^{2}$ and ${D}_{4}^{2}-{D}_{5}^{2}{D}_{6}^{2}$ are positive. In this case, Eq. (13) has no real root. That is, the real parts of all roots of Eq. (12) are negative. So, we obtain the following.

**Theorem 4.2** *If* ${\mathcal{R}}_{0}>1$ *and* $0<h\le 2$, *Eq*. (3) *has a locally asymptotically stable EE* ${E}_{\ast}=({I}_{\ast},{R}_{\ast})$ *for any* $\tau >0$.

Now, we consider the case that $h>2$ if ${\mathcal{R}}_{0}>1$ and $\tau >0$. Firstly, we give the following lemma.

**Lemma 4.1**

*If*${\mathcal{R}}_{0}>1$, $h>2$

*and*$H<0$

*where*

*then there is a positive* ${\tau}_{0}$ *such that Eq*. (12) *has a pair of purely imaginary eigenvalues* $\pm {\omega}_{0}i$ *as* $\tau ={\tau}_{0}$, *and all eigenvalues with negative real parts as* $0<\tau <{\tau}_{0}$.

*Proof*From Eq. (7), we have

Then $\pm j{\omega}_{0}$ is a pair of purely imaginary roots of Eq. (12) if $\tau ={\tau}^{j}$.

*τ*. This implies the conclusion when we take

The proof is completed. □

Based on Lemma 4.1, we have the following.

**Theorem 4.3**

*Assume that*${\mathcal{R}}_{0}>1$, $h>2$,

*and*$H<0$,

*then there is a positive real number*${\tau}_{0}$

*such that the following conclusions hold*.

- (i)
*If*$0<\tau <{\tau}_{0}$,*Eq*. (3)*has an EE*${E}_{\ast}$*which is locally asymptotically stable*; - (ii)
*Equation*(3)*can undergo a Hopf bifurcation if*$\tau >{\tau}_{0}$,*and a periodic orbit exists in the small neighborhood of the EE*${E}_{\ast}$.

*Proof*From Lemma 4.1, we have the conclusion in case (i). To obtain the Hopf bifurcation, we need to check the transversal condition in [18] for the complex eigenvalues of the EE ${E}_{\ast}$ at $\tau ={\tau}_{0}$. Let $\lambda (\tau )=\xi (\tau )+i\omega (\tau )$ be a root of Eq. (12) as $\tau \ge {\tau}_{0}$, where

*ξ*,

*ω*are the real part and imaginary parts of

*λ*, respectively. Then, from Eq. (12), we have

*i.e.*

Then we have the conclusion. □

## 5 Examples

According to the above theorems, we have the same results on the dynamics of the SIRI model (1) with one of its limit equations (3). In this section, we shall give some examples and their simulations to illustrate the effectiveness of these results. Furthermore, we show the influence of the nonlinear incidence rate and latent period on the dynamical behaviors of the SIRI model.

Consider the SIRI model (1) for the following cases of different parameters.

**Example 5.1** Take $b=0.3$; $d=0.3$; $k=0.3$; $h=1$; $\gamma =0.5$; $\mu =0.2$; $\alpha =10$.

**Example 5.2** Take $b=0.3$; $d=0.3$; $k=0.375$; $h=1$; $\gamma =0.5$; $\mu =0.2$; $\alpha =10$.

**Example 5.3** Take $b=1.5$; $d=0.3$; $k=0.3$; $h=1$; $\gamma =0.5$; $\mu =0.2$; $\alpha =10$.

**Example 5.4** Take $b=1$; $d=0.3$; $k=0.5$; $h=3$; $\gamma =0.5$; $\mu =0.2$; $\alpha =1\text{,}000$.

- (1)
The basic reproductive number ${\mathcal{R}}_{0}$ determines the existence of the equilibrium. When ${\mathcal{R}}_{0}\le 1$, model (1) has one unique disease-free equilibrium. When ${\mathcal{R}}_{0}>1$, model (1) has a disease-free equilibrium and one unique endemic equilibrium.

- (2)
For model (1) without latent period, its threshold dynamics is determined by ${\mathcal{R}}_{0}$. That is, the disease-free equilibrium is globally asymptotically stable if ${\mathcal{R}}_{0}\le 1$, while the unique endemic equilibrium is globally asymptotically stable if ${\mathcal{R}}_{0}>1$.

- (3)
When the SIRI model has a latent period, the dynamical behaviors of the SIRI model (1) become complex, which are dependent on the values of ${\mathcal{R}}_{0}$,

*τ*,*h*. If ${\mathcal{R}}_{0}\le 1$, the latent period does not impact on the stability of the disease-free equilibrium, which is globally asymptotically stable for any latent period. However, the parameter*h*in the nonlinear incidence rate and the latent period*τ*are essential for the stability of the endemic equilibrium if ${\mathcal{R}}_{0}>1$. When $h>2$ and ${\mathcal{R}}_{0}>1$, the SIRI model may undergo a Hopf bifurcation and produce a periodic orbit for a large latent period. When $0<h<2$ and ${\mathcal{R}}_{0}>1$, the endemic equilibrium is locally asymptotically stable.

- (i)
Is the endemic equilibrium globally asymptotically stable if $0<h<2$ and ${\mathcal{R}}_{0}>1$? We conjecture it is positive by some numerical simulations.

- (ii)
Does the model undergo a Hopf bifurcation and produce a periodic orbit if ${\mathcal{R}}_{0}>1$, $h>2$, but $H<0$ is not satisfied?

## Declarations

### Acknowledgements

The work is supported partially by National Natural Science Foundation of China under Grant No. 10971240, No. 61263020, the Program of Chongqing Innovation Team Project in University under Grant No. KJTD201308, Natural Science Foundation of Chongqing under Grant CQ CSTC 2012jjA40052, Foundation of Science and Technology project of Chongqing Education Commission under Grant KJ120615, KJ130613.

## Authors’ Affiliations

## References

- Tudor D: A deterministic model for herpes infections in human and animal populations.
*SIAM Rev.*1990, 32: 136–139. 10.1137/1032003MathSciNetView ArticleMATHGoogle Scholar - Chin J:
*Control of Communicable Diseases Manual*. American Public Health Association, Washington; 1999.Google Scholar - Martin SW:
*Livestock Disease Eradication: Evaluation of the Cooperative State Federal Bovine Tuberculosis Eradication Program*. National Academy Press, Washington; 1994.Google Scholar - VanLandingham KE, Marsteller HB, Ross GW, Hayden FG: Relapse of herpes simplex encephalitis after conventional acyclovir therapy.
*JAMA*1988, 259: 1051–1053. 10.1001/jama.1988.03720070051034View ArticleGoogle Scholar - Moreira HN, Wang Y:Global stability in an $\mathrm{S}\to \mathrm{I}\to \mathrm{R}\to \mathrm{I}$ model.
*SIAM Rev.*1997, 39: 496–502. 10.1137/S0036144595295879MathSciNetView ArticleMATHGoogle Scholar - van den Driessche P, Wang L, Zou X: Modeling diseases with latency and relapse.
*Math. Biosci. Eng.*2007, 4: 205–219.MathSciNetView ArticleMATHGoogle Scholar - Capasso V, Serio G: A generalization of the Kermack-McKendrick deterministic epidemic model.
*Math. Biosci.*1978, 42: 43–61. 10.1016/0025-5564(78)90006-8MathSciNetView ArticleMATHGoogle Scholar - Ruan S, Wang W: Dynamical behavior of an epidemic model with a nonlinear incidence rate.
*J. Differ. Equ.*2003, 188: 135–163. 10.1016/S0022-0396(02)00089-XMathSciNetView ArticleMATHGoogle Scholar - Xiao D, Ruan S: Global analysis of an epidemic model with nonmonotone incidence rate.
*Math. Biosci.*2007, 208: 419–429. 10.1016/j.mbs.2006.09.025MathSciNetView ArticleMATHGoogle Scholar - Yang Y, Xiao D: Influence of latent period and nonlinear incidence rate on the dynamics of SIRS epidemiological models.
*Discrete Contin. Dyn. Syst., Ser. B*2010, 13: 195–211.MathSciNetMATHGoogle Scholar - Liu WM, Levin SA, Iwasa Y: Influence of nonlinear incidence rates upon the behavior of SIRS epidemiological models.
*J. Math. Biol.*1986, 23: 187–204. 10.1007/BF00276956MathSciNetView ArticleMATHGoogle Scholar - Derrick WR, van der Driessche P: A disease transmission model in a nonconstant population.
*J. Math. Biol.*1993, 31: 495–512.MathSciNetView ArticleMATHGoogle Scholar - Hethcote HW, Lewis MA, van der Driessche P: An epidemiological model with a delay and a nonlinear incidence rate.
*J. Math. Biol.*1989, 27: 49–64. 10.1007/BF00276080MathSciNetView ArticleMATHGoogle Scholar - Beretta E, Takeuchi Y: Global stability of an SIR epidemic model with time delays.
*J. Math. Biol.*1995, 33: 250–260.MathSciNetView ArticleMATHGoogle Scholar - Lizana M, Rivero J: Multiparametric bifurcations for a model in epidemiology.
*J. Math. Biol.*1996, 35: 21–36. 10.1007/s002850050040MathSciNetView ArticleMATHGoogle Scholar - Jin Y, Wang W, Xiao S: An SIRS model with a nonlinear incidence rate.
*Chaos Solitons Fractals*2007, 34: 1482–1497. 10.1016/j.chaos.2006.04.022MathSciNetView ArticleMATHGoogle Scholar - Li G, Wang W: Bifurcation analysis of an epidemic model with nonlinear incidence.
*Appl. Math. Comput.*2009, 214: 411–423. 10.1016/j.amc.2009.04.012MathSciNetView ArticleMATHGoogle Scholar - Hale JK Applied Mathematical Sciences 3. In
*Theory of Functional Differential Equations*. 2nd edition. Springer, New York; 1977.View ArticleGoogle Scholar - Kuang Y:
*Delay Differential Equations with Applications in Population Dynamics*. Academic Press, San Diego; 1993.MATHGoogle Scholar - van der Driessche P, Watmough J: Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission.
*Math. Biosci.*2002, 180: 29–48. 10.1016/S0025-5564(02)00108-6MathSciNetView ArticleMATHGoogle Scholar - Brauer F, van der Driessche P, Wu J Lecture Notes in Mathematics 1945. In
*Mathematical Epidemiology*. Springer, New York; 2008.View ArticleGoogle Scholar - Zhang ZF, Ding TR, Huang WZ, Dong ZX Translations of Mathematical Monographs 101. In
*Qualitative Theory of Differential Equations*. Am. Math. Soc., Providence; 1992.Google Scholar - Lasalle JP: An invariance principle in the theory of stability. In
*International Symposium on Differential Equations and Dynamical Systems*. Academic Press, New York; 1967:277–286.Google Scholar

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