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Dynamical behaviors of an SIRI epidemic model with nonlinear incidence and latent period
Advances in Difference Equations volume 2014, Article number: 164 (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.
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
In this paper, we consider the general incidence rate in an epidemiological model
where kI represents the infection force of the disease and 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:
Then we propose the following SIRI model with general incidence rate and latent period:
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.
For convenience, we first transform the three-dimensional Eq. (1) into its two-dimensional limit form. Summing up the three equations in Eq. (1) and denoting , we have
Then we obtain the following limit equations of Eq. (1):
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]).
As regards the biological meaning, we always assume that the initial value of Eq. (3) is nonnegative and has the form of
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.
Proof From (3), we can see that on the line , , and on the line , . Hence, no orbit of Eq. (3) can exit from the first quadrant, with the boundary and . From (2), we have
the orbit of Eq. (3) getting at the boundary must go into the interior of . Thus, the region is an absorbing set of Eq. (3) in the first quadrant. This completes the proof. □
As is well known, the basic reproductive number is a key parameter to discuss the dynamical behaviors of epidemic model. According to the literature [20, 21], we get the basic reproductive number of model (3) as follows:
In terms of , we obtain the following result on the existence of the equilibrium for Eq. (3).
Lemma 2.2 (i) If , then Eq. (3) has a unique equilibrium in the first quadrant.
If , then Eq. (3) has two equilibria in the first quadrant, which are and , where .
Proof Clearly, Eq. (3) always has an equilibrium , and it has no other boundary equilibrium in the first quadrant from the second equation of (3). Furthermore, Eq. (3) has a positive equilibrium if and only if it satisfies
It is equivalent to being a positive solution of
When , Eq. (7) has no positive solution since . So, the conclusion in case (i) holds.
To prove case (ii), set
When , we have and φ is strictly monotone decreasing for . 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, 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
In this section, we shall study the stability and topological structure of the DFE and EE of Eq. (3) without the latent period, that is,
We first give the local stability and topological structure of the equilibria.
Lemma 3.1 (i) If , then the DFE of Eq. (8) is a locally asymptotically stable hyperbolic node.
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.
Proof The Jacobian matrix of Eq. (8) at is
When , we compute the determinant and trace of
Then the DFE of Eq. (8) is locally asymptotically stable if .
When , and , which implies that the DFE is locally stable since one eigenvalue of is equal to 0 and the other is less than 0. Furthermore, we construct the Lyapunov function . From , we have
and this has a unique point . It follows from the Lasalle invariant principle that the DFE is asymptotically stable when .
When , we have , which implies of Eq. (8) is an unstable saddle. Furthermore, Eq. (8) has an unique EE . We compute the Jacobian matrix at as follows:
Let . Since satisfies Eq. (6), we have
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 .
Thus, we have and . Let
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 .
Proof Suppose there is a stable manifold in , then the stay in the or traverse through . Assuming the former case, we know that the satisfy one of the following cases (see ):
, 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.
Theorem 3.1 (i) If , then Eq. (8) has a unique DFE , which is a globally asymptotically stable hyperbolic node in the first quadrant.
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.
Proof We firstly give the local stability of DFE . Linearize Eq. (3) at and obtain the characteristic equation
where E is the unit matrix and
Then we have
From Theorem 3.1, the real parts of all roots of Eq. (9) are negative if and . Thus, all roots of Eq. (9) have negative real parts if . Assume Eq. (9) has a pair conjugate imaginary roots if , where ω is a positive real number. Then we have
Thus, we have
By a simple calculation, we have
If , then , . Accordingly, Eq. (10) does not have any real root and all roots of Eq. (3) have negative real parts for any if .
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.
Next, we show that the DFE is globally asymptotically stable if . Construct the Lyapunov functional
Clearly, and in the interior of . Since , we have
Furthermore, the set
has a unique point . It follows from the Lyapunov-Lasalle invariant principle in [18, 23] that the DFE is globally asymptotically stable. This completes the proof. □
When , the system has a unique endemic equilibrium . In the following, we shall analyze the stability of , which is dependent on the parameters h and τ. To do this, we calculate the linearization of Eq. (3) at and obtain the characteristic equation
where E is the unit matrix and
Note that satisfies Eq. (6). Then the characteristic equation becomes
Suppose that there is a positive such that Eq. (12) has a pair of purely imaginary roots , . Then ω satisfies
Separating the real and imaginary parts, we have
By a simple computation, we have
From Theorem 3.1, the real parts of all roots for Eq. (12) with are negative, which implies that
Furthermore, we have
When and , we have , , and 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 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.
Lemma 4.1 If , and where
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 .
Proof From Eq. (7), we have
Thus, we have
Let of Eq. (13), we have
From , there exists a unique positive root of Eq. (18). Then Eq. (18) has a unique positive root . Denote
Then is a pair of purely imaginary roots of Eq. (12) if .
Now, we select such that
Then Eq. (12) has a pair of purely imaginary roots if . Note that any root of Eq. (12) has a negative real part for and Eq. (12) has no zero root for any . Then all roots of Eq. (12) has a negative real part if from the continuity of the roots on parameter 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 , , and , then there is a positive real number such that the following conclusions hold.
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 .
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  for the complex eigenvalues of the EE at . Let be a root of Eq. (12) as , where ξ, ω are the real part and imaginary parts of λ, respectively. Then, from Eq. (12), we have
Since and , we have
Noting and , we have
Thus, we have
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 ; ; ; ; ; ; .
Since , it follows from Theorem 3.1 and Theorem 4.1 that the disease-free equilibrium is globally asymptotically stable for any latent period. Figure 1 shows the global asymptotical stability of the equilibrium for the model (1) with , , respectively.
Example 5.2 Take ; ; ; ; ; ; .
Since , it follows from Theorem 3.1 and Theorem 4.1 that the disease-free equilibrium is globally asymptotically stable for any latent period. Figure 2 shows the globally asymptotical stability of the equilibrium for the model (1) with , , respectively.
Example 5.3 Take ; ; ; ; ; ; .
Since and , it follows from Theorem 3.1 (Theorem 4.2) that the endemic equilibrium is globally (locally) asymptotically stable for (). Figure 3 shows the asymptotical stability of the equilibrium for the model (1) with , , respectively.
Example 5.4 Take ; ; ; ; ; ; .
We compute , , . From Theorem 4.3, the endemic equilibrium is locally asymptotically stable for a small latent period, while the SIRI model can undergo a Hopf bifurcation and produce a periodic orbit for a large latent period. Figure 4 shows the asymptotical stability of the equilibrium as , and Figure 5 shows the periodic orbit as .
From the above results and numerical examples, we can draw the following conclusions.
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.
In addition, we leave two unsolved problems in this paper for future work.
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?
Tudor D: A deterministic model for herpes infections in human and animal populations. SIAM Rev. 1990, 32: 136–139. 10.1137/1032003
Chin J: Control of Communicable Diseases Manual. American Public Health Association, Washington; 1999.
Martin SW: Livestock Disease Eradication: Evaluation of the Cooperative State Federal Bovine Tuberculosis Eradication Program. National Academy Press, Washington; 1994.
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.1988.03720070051034
Moreira HN, Wang Y:Global stability in an model. SIAM Rev. 1997, 39: 496–502. 10.1137/S0036144595295879
van den Driessche P, Wang L, Zou X: Modeling diseases with latency and relapse. Math. Biosci. Eng. 2007, 4: 205–219.
Capasso V, Serio G: A generalization of the Kermack-McKendrick deterministic epidemic model. Math. Biosci. 1978, 42: 43–61. 10.1016/0025-5564(78)90006-8
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-X
Xiao D, Ruan S: Global analysis of an epidemic model with nonmonotone incidence rate. Math. Biosci. 2007, 208: 419–429. 10.1016/j.mbs.2006.09.025
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.
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/BF00276956
Derrick WR, van der Driessche P: A disease transmission model in a nonconstant population. J. Math. Biol. 1993, 31: 495–512.
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/BF00276080
Beretta E, Takeuchi Y: Global stability of an SIR epidemic model with time delays. J. Math. Biol. 1995, 33: 250–260.
Lizana M, Rivero J: Multiparametric bifurcations for a model in epidemiology. J. Math. Biol. 1996, 35: 21–36. 10.1007/s002850050040
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.2006.04.022
Li G, Wang W: Bifurcation analysis of an epidemic model with nonlinear incidence. Appl. Math. Comput. 2009, 214: 411–423. 10.1016/j.amc.2009.04.012
Hale JK Applied Mathematical Sciences 3. In Theory of Functional Differential Equations. 2nd edition. Springer, New York; 1977.
Kuang Y: Delay Differential Equations with Applications in Population Dynamics. Academic Press, San Diego; 1993.
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)00108-6
Brauer F, van der Driessche P, Wu J Lecture Notes in Mathematics 1945. In Mathematical Epidemiology. Springer, New York; 2008.
Zhang ZF, Ding TR, Huang WZ, Dong ZX Translations of Mathematical Monographs 101. In Qualitative Theory of Differential Equations. Am. Math. Soc., Providence; 1992.
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.
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.
The authors declare that they have no competing interests.
The authors declare that the study was realized in collaboration with the same responsibility. All authors read and approved the final manuscript.
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Guo, P., Yang, X. & Yang, Z. Dynamical behaviors of an SIRI epidemic model with nonlinear incidence and latent period. Adv Differ Equ 2014, 164 (2014). https://doi.org/10.1186/1687-1847-2014-164
- SIRI model
- nonlinear incidence rate
- Hopf bifurcation