Dynamical behaviors of an SIRI epidemic model with nonlinear incidence and latent period
© Guo et al.; licensee Springer. 2014
Received: 27 January 2014
Accepted: 14 May 2014
Published: 4 June 2014
In this paper, we study an SIRI epidemic model with nonlinear incidence rate and latent period, namely , 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.
KeywordsSIRI model stability nonlinear incidence rate Hopf bifurcation
An SIRI epidemiological model in a population was formulated by Tudor , 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  studied an SIRI model with general saturated incidence rate 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.  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 was not investigated.
In 1978, Capasso and Serio  introduced a saturated incidence rate by research of the cholera epidemic spread in Bari. Ruan and Wang  studied an epidemic model with a particular nonlinear incident rate and provided a detailed qualitative analysis on the Hopf bifurcation and Bogdanov-Takens bifurcation for the model. Xiao and Ruan  studied nonlinear incidence rates of . Yang and Xiao  investigated the general nonlinear incidence rate . Recently, a more general incidence rate was investigated by Liu et al. , Derrick and van den Driessche , 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 . In Section 3, we obtain the threshold dynamics on stability for the model without the latent period (i.e., ). Furthermore, we study the stability and Hopf bifurcation for the model with 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
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 , and represents the continuous function space on with the norm , . 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 is an invariant set and also an absorbing set of Eq. (3) in the first quadrant.
In terms of , we obtain the following result on the existence of the equilibrium for Eq. (3).
If , then Eq. (3) has two equilibria in the first quadrant, which are and , where .
When , Eq. (7) has no positive solution since . So, the conclusion in case (i) holds.
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, and 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
We first give the local stability and topological structure of the equilibria.
If , then DFE of Eq. (8) is a saddle-node and is locally asymptotically stable in the first quadrant.
If , then the DFE of Eq. (8) is an unstable saddle and the EE is a locally asymptotically stable hyperbolic node.
Then the DFE of Eq. (8) is locally asymptotically stable if .
and this has a unique point . It follows from the Lasalle invariant principle that the DFE is asymptotically stable when .
This implies that the endemic equilibrium 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 .
By the Bendixson-Dulac criterion , 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 in if .
Lemma 3.3 The two stable manifolds of saddle are not in if .
, is a closed orbit, or is a singular closed trajectory,
where is negative limit set of . Case (i) and Lemma 3.1 lead to a contradiction, and case (ii) and Lemma 3.2 lead to a contradiction.
If the ran out of the region , it is divided into two parts to (if the two stable manifolds are all in , and all passed through this region , they are divided into three parts of ) since or is not an orbit of Eq. (8). Then the equilibrium is not in one part. In this part, we arbitrarily select a point 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 is a closed orbit or a singular closed trajectory, where is a positive limit set of . 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.
If , then Eq. (8) has a unique DFE , which is a saddle-node and is globally asymptotically stable in the first quadrant.
If , then Eq. (8) has one unique DFE and a unique EE , and is a unstable saddle and is a globally asymptotically stable hyperbolic node in the first quadrant.
Proof If , has a unique disease-free equilibrium. Obviously, is a bounded absorbing region which contains exactly one critical point . For any point P in , according to , its Omega-limit set satisfies or is a closed orbit or is a singular closed trajectory. Lemma 3.2 essentially means that there is no closed orbit or singular closed trajectory in . So we have . This proves that is a global attractor in .
From Lemma 3.1 and Lemma 3.3, is an unstable saddle in and its two stable manifolds are not in if . By a similar proof, we have , where P is an arbitrary point in . This proves that is a global attractor in .
It follows from Lemma 2.1 that is a absorbing set in the first quadrant. Thus, in case (i) and case (ii) and 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 plays a threshold role in determining the stability of Eq. (8).
4 Dynamical behaviors for the case
In this section, we shall study the stability and Hopf bifurcation of the equilibria for Eq. (3) with the latent period .
The following result shows that is a threshold value for the DFE.
Theorem 4.1 If , the DFE of Eq. (3) is globally asymptotically stable. If , the DFE is unstable.
By a similar analysis, we derive that Eq. (9) has a unique zero root and all other roots with negative real parts for any if . Thus, is locally asymptotically stable if for any .
If , Eq. (9) has a root with the positive real part for any . Thus, the DFE is unstable.
Theorem 4.2 If and , Eq. (3) has a locally asymptotically stable EE for any .
Now, we consider the case that if and . Firstly, we give the following lemma.
then there is a positive such that Eq. (12) has a pair of purely imaginary eigenvalues as , and all eigenvalues with negative real parts as .
Then is a pair of purely imaginary roots of Eq. (12) if .
The proof is completed. □
Based on Lemma 4.1, we have the following.
If , Eq. (3) has an EE which is locally asymptotically stable;
Equation (3) can undergo a Hopf bifurcation if , and a periodic orbit exists in the small neighborhood of the EE .
Then we have the conclusion. □
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 ; ; ; ; ; ; .
Example 5.2 Take ; ; ; ; ; ; .
Example 5.3 Take ; ; ; ; ; ; .
Example 5.4 Take ; ; ; ; ; ; .
The basic reproductive number determines the existence of the equilibrium. When , model (1) has one unique disease-free equilibrium. When , model (1) has a disease-free equilibrium and one unique endemic equilibrium.
For model (1) without latent period, its threshold dynamics is determined by . That is, the disease-free equilibrium is globally asymptotically stable if , while the unique endemic equilibrium is globally asymptotically stable if .
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 , τ, h. If , 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 . When and , the SIRI model may undergo a Hopf bifurcation and produce a periodic orbit for a large latent period. When and , the endemic equilibrium is locally asymptotically stable.
Is the endemic equilibrium globally asymptotically stable if and ? We conjecture it is positive by some numerical simulations.
Does the model undergo a Hopf bifurcation and produce a periodic orbit if , , but is not satisfied?
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.
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