# Global stability of the endemic equilibrium of a discrete SIR epidemic model

- Xia Ma
^{1}Email author, - Yicang Zhou
^{1}and - Hui Cao
^{2}

**2013**:42

https://doi.org/10.1186/1687-1847-2013-42

© Ma et al.; licensee Springer 2013

**Received: **10 October 2012

**Accepted: **29 January 2013

**Published: **25 February 2013

## Abstract

The basic reproductive number ${R}_{0}$ of a discrete SIR epidemic model is defined and the dynamical behavior of the model is studied. It is proved that the disease free equilibrium is globally asymptotically stable if ${R}_{0}<1$, and the persistence of the model is obtained when ${R}_{0}>1$. The main attention is paid to the global stability of the endemic equilibrium. Sufficient conditions for the global stability of the endemic equilibrium are established by using the comparison principle. Numerical simulations are done to show our theoretical results and to demonstrate the complicated dynamics of the model.

## Keywords

## 1 Introduction

Mathematical models have played a significant role in describing the dynamical evolution of infectious diseases. SIR model is one of the classical epidemic models with compartment structure, suitable for the transmission of infectious diseases that confer long-lasting immunity such as chicken pox and SARS. The host population is divided into three epidemiological groups: the susceptibles, the infectives, and the removed/recovered. The transmission dynamics of an infectious disease is described by modeling the population movements among those epidemiological compartments.

There is an increasing interest in the study and application of discrete epidemic models. Allen *et al.* have studied some discrete-time SI, SIR, and SIS epidemic models, compared the dynamics of deterministic and stochastic discrete-time epidemic models, and also applied the discrete epidemic model to the spread of rabies [1–3]. Allen and Driessche have given the basic reproductive number for some discrete-time epidemic models [4]. Castillo-Chavez and Yakubu have investigated a discrete SIS model with complex dynamics [5]. Zhou *et al.* have formulated and discussed the dynamical behavior of age-structured SIS models [6, 7]. 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]. One reason for the upsurge of discrete epidemic models is that discrete models have advantages in describing an infectious disease since epidemic data are usually collected in discrete time units, which would make it more convenient to use discrete-time models [11].

The studies on discrete epidemic models are relatively few compared with those on continuous epidemic models. Most research works on discrete epidemic models concern the definition of the basic reproductive number, the global stability of the disease free equilibrium, the persistence of diseases, the existence and local stability of endemic equilibria, the existence of flip bifurcation and Hopf bifurcation. The results on the global stability of the endemic equilibrium are quite few for discrete epidemic models due to the following two factors: (1) There is not enough effective theory and methods for global stability of discrete dynamical systems; (2) The discrete model exhibits much more complicated dynamical behaviors than its corresponding continuous model.

We will study the global stability of the endemic equilibrium of a discrete SIR model and get sufficient conditions. The discrete SIR model, the biological requirement of the model, the basic reproductive number, and the invariant domain of the model are given in the next section. The global stability of disease free equilibrium and the persistence are discussed in Section 3. The sufficient conditions for the global stability of endemic equilibrium are obtained in Section 4. Numerical simulations and application are presented to demonstrate our theoretical results, to show the complex dynamics and application example of the model in Section 5. Conclusions and discussions are included in the last section.

## 2 The discrete SIR model

There are different approaches to model infectious disease evolution in discrete time. The recurrent difference equations from the discretization of continuous differential equation models is one of the direct and frequently-used modeling approaches. This kind of model can be well understood in application under reasonable assumptions though there are some limitations on the range of parameters. We will adopt this approach to formulate model and assume that the infected people will obtain permanent immunity after they get recovered from infection.

*t*. According to the disease transmission mechanism, the host population is grouped into three epidemiological compartments. Let $S(t)$, $I(t)$, and $R(t)$ be the number of individuals in the susceptible, infective, and removed/recovered compartments at time

*t*, respectively. In addition to the death and recruitment, there are population movements among those three epidemiological compartments from time unit

*t*to time $t+1$. We assume that the recruited individuals (by birth and immigration) are constant and enter the susceptible compartment. After one unit time, the susceptible individuals may stay in the susceptible compartment, or get infected and move to the infectious compartment, or die. The individuals in the infective compartment can keep being the infective, or get recover and transfer to the recovered compartment, or die. The individuals in the recovered compartment never leave the compartment unless they die. The discrete SIR model is

where Λ is the constant recruitment rate of the population, *β* is the transmission rate, *μ* is the natural death rate, *δ* is the death rate caused by disease, and *γ* is the recovery rate. The biological background requires that all parameters be non-negative.

There is abundant amount of research into SIR models since they capture the basic evolution mechanism of the infectious diseases when the recovered individuals will acquire life-long immunity. A lot of results on the existence and global stability of the endemic equilibrium have been obtained for continuous SIR models with various transmission rate. However, the result on the global stability of the endemic equilibrium for discrete SIR model is rare, even for the model with mass action incidence. We will try to give sufficient conditions for the global stability of the endemic equilibrium. Those results on global stability of the endemic equilibrium can promote the study on this challengeable problem.

Those two conditions in (4) are natural requirements for model (1). The first inequality, $\beta +\mu <1$, says that the percentage of the susceptible individuals who die or get infected is less than one within a unit time. The second inequality, $\mu +\delta +\gamma <1$, states that the percentage of the infected people who die or get recovered is less than one within a unit time. It is easy to verify that those two inequalities ensure $S(t)\ge 0$, $I(t)\ge 0$, and $R(t)\ge 0$ for all $t\ge 0$ if the initial values satisfy (3).

is a compact, positively invariant set for model (1). ${\mathrm{\Omega}}_{1}$ is also a global compact attractor of system (1) since it attracts all positive orbits with an initial value $({S}_{0},{I}_{0},{R}_{0})\in {\mathbb{R}}_{+}^{3}$.

The basic reproductive number, ${R}_{0}$, is fundamental and widely used in the study of epidemiological models. ${R}_{0}$ is the average number of secondary infections produced by one infected individual during the entire course of infection in a completely susceptible population. ${R}_{0}$ often serves as a threshold parameter that predicts whether an infection dies out or keeps persistence in a population. Following the idea and framework given in [4, 12, 13], we obtain the formula of the basic reproduction number of model (1), ${R}_{0}=\frac{\beta}{\mu +\delta +\gamma}$. The magnitude of ${R}_{0}$ plays a crucial role in determining the dynamical behavior of model (1).

## 3 Extinction and persistence for the disease

This section focuses on the disease extinction or persistence, which is determined by the stability of the disease free equilibrium and the existence of endemic equilibrium of model (1). It is obvious that ${E}^{0}(\frac{\mathrm{\Lambda}}{\mu},0,0)$ is an equilibrium of model (1). ${E}^{0}$ is called the disease free equilibrium since *I* and *R* components are zero. The stability of the disease free equilibrium ${E}^{0}$ is given in the following theorem.

**Theorem 1** *The disease free equilibrium* ${E}^{0}$ *of model* (1) *is globally asymptotically stable if* ${R}_{0}<1$, *and* ${E}^{0}$ *is unstable if* ${R}_{0}>1$.

*Proof*The linearized matrix of (1) at the disease free equilibrium ${E}^{0}$ is

Obviously, the eigenvalues of matrix *A* are ${\lambda}_{1}={\lambda}_{2}=1-\mu $ and ${\lambda}_{3}=1+\beta -\mu -\delta -\gamma $, respectively. The conditions $\beta +\mu <1$, $\mu +\delta +\gamma <1$, and ${R}_{0}<1$ imply that $|{\lambda}_{i}|<1$, $i=1,2,3$. Therefore, the disease free equilibrium ${E}^{0}$ is asymptotically stable. When ${R}_{0}>1$, we have ${\lambda}_{3}=1+\beta -\mu -\delta -\gamma >1$, then the disease free equilibrium ${E}^{0}$ is unstable.

The inequality (6) implies that ${lim}_{t\to \mathrm{\infty}}I(t)=0$.

The equation $\tilde{R}(t+1)=\tilde{R}(t)+\gamma \epsilon -\mu \tilde{R}(t)$ has a unique equilibrium ${\tilde{R}}^{\ast}=\frac{\gamma \epsilon}{\mu}$, which is globally asymptotically stable. The comparison principle implies that there exists an integer ${T}_{2}>{T}_{1}$ such that $R(t)\le \tilde{R}(t)<\frac{2\gamma \epsilon}{\mu}$ if $t>{T}_{2}$. The arbitrary *ε* implies that ${lim}_{t\to \mathrm{\infty}}R(t)=0$.

*ε*, ${\epsilon}_{1}$, and ${\epsilon}_{2}$ imply that ${lim}_{t\to \mathrm{\infty}}N(t)=\frac{\mathrm{\Lambda}}{\mu}$,

*i.e.*, ${lim}_{t\to \mathrm{\infty}}S(t)={lim}_{t\to \mathrm{\infty}}(N(t)-I(t)-R(t))=\frac{\mathrm{\Lambda}}{\mu}$. Those limits

imply that the disease free equilibrium of (1) is globally asymptotically stable when ${R}_{0}<1$. □

${R}_{0}>1$ means that the average number of a new infection by an infected individual is more than one. The epidemiological interpretation indicates that the disease may keep persistent in the population. The following theorem confirms the persistence of the disease in the case ${R}_{0}>1$.

**Theorem 2** *If* ${R}_{0}>1$, *then the disease will keep persistence in the population*, *i*.*e*., *there exists a positive* *ε* *such that the solution of model* (1) *with the initial value* $I(0)>0$ *satisfies* ${lim\hspace{0.17em}inf}_{t\to \mathrm{\infty}}I(t)>\epsilon $.

*Proof*We define the sets $X={\mathrm{\Omega}}_{1}=\{(S,I,R)\in {\mathbb{R}}_{+}^{3}\mid S+I+R\le \frac{\mathrm{\Lambda}}{\mu}\}$, ${X}_{0}=\{(S,I,R)\in X\mid I>0,R>0\}$, and $\partial {X}_{0}=X\mathrm{\setminus}{X}_{0}$. Let $\mathrm{\Phi}:X\to X$, ${\mathrm{\Phi}}_{t}({x}^{0})=\varphi (t,{x}^{0})$, be the solution map of model (1) with $\varphi (0,{x}^{0})={x}^{0}$ and ${x}^{0}=(S(0),I(0),R(0))$. Let $\mathcal{M}=\{{\mathrm{\Phi}}_{0}\}=(\frac{\mathrm{\Lambda}}{\mu},0,0)$ and

has a positive equilibrium ${S}_{\ast}=\frac{\mathrm{\Lambda}}{\mu}$, which is globally attractive. By using Lemma 5.9 in [14], we know that no subset of ℳ forms a cycle in $\partial {X}_{0}$. The fact ${\mathrm{\Phi}}_{t}((S(0),0,R(0)))=(S(t),0,R(t))$ implies that ${\mathrm{\Phi}}_{t}({M}_{\partial})\subset {M}_{\partial}$. If ${x}^{0}=(S(0),0,R(0))\in {M}_{\partial}$, then we have ${lim}_{t\to \mathrm{\infty}}S(t)=\frac{\mathrm{\Lambda}}{\mu}$, ${lim}_{t\to \mathrm{\infty}}R(t)={lim}_{t\to \mathrm{\infty}}{(1-\mu )}^{t}R(0)=0$, and $\mathrm{\Omega}({M}_{\partial})={\mathrm{\Phi}}_{0}$.

Since $0\le I(t)\le N(t)$ and $N(t+1)=\mathrm{\Lambda}+N(t)-\mu N(t)-\delta I(t)$, we have $N(t+1)\ge N(t)+\mathrm{\Lambda}-(\mu +\delta )N(t)$ and $N(t+1)\le N(t)+\mathrm{\Lambda}-\mu N(t)$. The difference equation ${N}_{1}(t+1)=\mathrm{\Lambda}+(1-\mu -\delta ){N}_{1}(t)$ has a unique equilibrium ${N}_{1}^{\ast}=\frac{\mathrm{\Lambda}}{\mu +\delta}$, and $N(t+1)=N(t)+\mathrm{\Lambda}-\mu N(t)$ has a unique equilibrium ${N}_{2}^{\ast}=\frac{\mathrm{\Lambda}}{\mu}$, which is globally asymptotically stable. Therefore, for any given $\epsilon >0$, there exists a ${T}_{1}>0$ such that $\frac{\mathrm{\Lambda}}{(\mu +\delta )}-\epsilon \le N(t)\le \frac{\mathrm{\Lambda}}{\mu}+\epsilon $ for all $t\ge {T}_{1}$.

*σ*such that

*σ*, there exist a point $({S}_{\sigma}^{0},{I}_{\sigma}^{0},{R}_{\sigma}^{0})\in {X}_{0}$ and a large ${T}_{2}>{T}_{1}$ such that

*σ*small enough, then the condition ${R}_{0}>1$ implies that

From the inequalities in (13) and (14), we have that ${lim}_{t\to \mathrm{\infty}}I(t)=\mathrm{\infty}$. The limit ${lim}_{t\to \mathrm{\infty}}I(t)=\mathrm{\infty}$ contradicts the inequality $I(t)<\sigma $ in (11). The contradiction comes from the assumption given in (10). Therefore, the conclusion in (9) holds true. Subsequently, ${W}^{s}({\mathrm{\Phi}}_{0})\cap {X}_{0}=\mathrm{\varnothing}$ and ${\mathrm{\Phi}}_{0}$ is isolated in *X*. From Theorem 1.3.1 and Remark 1.3.1 in [15], it follows that Φ is uniformly persistent with respect to $({X}_{0},\partial {X}_{0})$. Furthermore, Theorem 1.3.4 in [15] implies that the solutions of model (1) are uniformly persistent with respect to $({X}_{0},\partial {X}_{0})$ when ${R}_{0}>1$. That is, there exists an $\epsilon >0$ such that ${lim\hspace{0.17em}inf}_{t\to \mathrm{\infty}}I(t)>\epsilon >0$. □

The conditions in Theorem 1 and Theorem 2 are very simple though the proof is long and complicated. It is very easy to determine whether the disease goes to extinction or keeps persistence in the population by the magnitude of the basic reproductive number, ${R}_{0}$. The underlining mechanism of the nice threshold result given in Theorem 1 is that the number of the infected population will gradually become lower and lower if the new infection created by an individual during his/her infection period is less than one. The epidemiological interpretation of Theorem 2 says that there is at least a certain number of infected population if the new infection created by an individual during his/her infection period is more than one. The necessary condition is to eliminate an infectious disease, to reduce ${R}_{0}$, and to have ${R}_{0}<1$. The basis reproductive number, ${R}_{0}$, is a very useful quantity in mathematical analysis and epidemiological application.

## 4 The stability of the endemic equilibrium

The local stability of the endemic equilibrium ${E}^{\ast}$ is given in the following theorem.

**Theorem 3** *If* ${R}_{0}>1$, *then the endemic equilibrium* ${E}^{\ast}=({S}^{\ast},{I}^{\ast},{R}^{\ast})$ *of model* (1) *is locally asymptotically stable*.

*Proof*In order to discuss the stability of the endemic equilibrium of model (1), we consider the following equivalent system:

The Jury criterion [16] implies that the two roots, ${\lambda}_{2}$ and ${\lambda}_{3}$, of the equation ${\varphi}_{1}(\lambda )=0$ satisfy $|{\lambda}_{2}|<1$ and $|{\lambda}_{3}|<1$. The linearization theory implies that the positive equilibrium $({N}^{\ast},{I}^{\ast},{R}^{\ast})$ of system (15) is locally asymptotically stable if ${R}_{0}>1$, *i.e.*, the endemic equilibrium ${E}^{\ast}$ of system (1) is locally asymptotically stable. □

The global stability of the endemic equilibrium ${E}^{\ast}$ of model (1) is quite difficult. Sufficient conditions are presented in two theorems for the special case $\delta =0$ and for the general case.

**Theorem 4** *Assume that* $\delta =0$. *The endemic equilibrium* ${E}^{\ast}({S}^{\ast},{I}^{\ast},{R}^{\ast})$ *of model* (1) *is globally asymptotically stable if* ${R}_{0}>\frac{\mu +\gamma}{\mu}$ *and* $\gamma <\mu $.

*Proof*When $\delta =0$, the host population $N(t)=S(t)+I(t)+R(t)$ satisfies

From the solution of the host population, we have that ${lim}_{t\to \mathrm{\infty}}N(t)={N}^{\ast}=\frac{\mathrm{\Lambda}}{\mu}$, and the limit of $N(t)$ is independent of the initial value ${N}_{0}$.

From those two inequalities in (20) and the comparison theorem [17], we know that for any small $\epsilon >0$, there exists a positive integer ${T}_{1l}$ such that ${L}_{1}^{l}\le L(t)\le {L}_{1}^{m}$ for $t>{T}_{1l}$, where ${L}_{1}^{l}=\frac{\mathrm{\Lambda}}{\mu +\gamma}-\epsilon $ and ${L}_{1}^{m}=\frac{\mathrm{\Lambda}}{\mu}+\epsilon $.

The variables ${x}_{1}^{m}(t)$ and ${x}_{1}^{l}(t)$ satisfy the quadratic difference equation. The well-known result of the famous population model $x(t+1)=rx(t)(1-x(t))$ says that ${x}_{0}^{\ast}=0$ is the unique and globally asymptotically stable equilibrium if $0<r<1$, whereas ${x}_{1}^{\ast}=1-\frac{1}{r}$ is the unique positive equilibrium if $1<r<3$ and ${x}_{1}^{\ast}$ is globally asymptotically stable. The result on the quadratic population model $x(t+1)=rx(t)(1-x(t))$ implies that the first equation in (22) has a positive equilibrium ${I}_{1\ast}^{m}={L}_{1}^{m}-\frac{\mathrm{\Lambda}}{\mu {R}_{0}}$, which is globally asymptotically stable. A similar argument implies that the second equation in (22) has a positive equilibrium ${I}_{1\ast}^{l}={L}_{1}^{l}-\frac{\mathrm{\Lambda}}{\mu {R}_{0}}$, which is globally asymptotically stable if ${R}_{0}>\frac{\mu +\gamma}{\mu}$.

From (24) and a similar argument, we can obtain that there exists a positive integer ${T}_{2l}$ such that ${L}_{2}^{l}\le L(t)\le {L}_{2}^{m}$ for all $t\ge {T}_{2l}$, where ${L}_{2}^{l}=\frac{\mathrm{\Lambda}-\gamma {I}_{1}^{m}}{\mu}-\epsilon $, ${L}_{2}^{m}=\frac{\mathrm{\Lambda}-\gamma {I}_{1}^{l}}{\mu}+\epsilon $, and ${L}_{1}^{l}<{L}_{2}^{l}<{L}_{2}^{m}<{L}_{1}^{m}$.

Let ${\lambda}_{1}$ and ${\lambda}_{2}$ be two roots of the linearized matrix of system (27) at the equilibrium ${P}_{\ast}^{i}$. It is easy to see that $|{\lambda}_{1}|=|{\lambda}_{2}|=\frac{\gamma}{\mu}<1$ if $\gamma <\mu $. The stability theory of difference equations implies that the equilibrium ${P}_{\ast}^{i}$ of (27) is globally asymptotically stable, *i.e.*, ${lim}_{k\to \mathrm{\infty}}{I}_{k}^{l}={I}_{\ast}^{l}(\epsilon )$ and ${lim}_{k\to \mathrm{\infty}}{I}_{k}^{m}={I}_{\ast}^{m}(\epsilon )$. From the expressions of ${I}_{\ast}^{l}(\epsilon )$ and ${I}_{\ast}^{m}(\epsilon )$, we have that ${lim}_{\epsilon \to 0}{I}_{\ast}^{l}(\epsilon )={lim}_{\epsilon \to 0}{I}_{\ast}^{m}(\epsilon )=\frac{\mathrm{\Lambda}({R}_{0}-1)}{\beta}$. From the inequality ${I}_{k}^{l}<I(t)<{I}_{k}^{m}$ and those limits, we obtain that ${lim}_{t\to \mathrm{\infty}}I(t)=\frac{\mathrm{\Lambda}({R}_{0}-1)}{\beta}$.

*k*tends to infinity and

*ε*tends to zero,

*i.e.*,

Therefore, the endemic equilibrium of system (18) is globally asymptotic stable when ${R}_{0}>\frac{\mu +\gamma}{\mu}$ and $\gamma <\mu $. Equivalently, the endemic equilibrium of system (1) is globally asymptotic stable. □

The comparison principle is the main idea to prove Theorem 4. The limitation of the method and the construction of the comparison equations may lead to the imposed conditions ${R}_{0}>\frac{\mu +\gamma}{\mu}$ and $\gamma \le \mu $. We do hope that the global stability of the endemic equilibrium can be proved under the condition ${R}_{0}>1$. When $\delta >0$, the global stability condition of endemic equilibrium is more complicated. We use the same idea to get the sufficient stability conditions.

**Theorem 5**

*If*$\mu >\delta +\gamma $, ${\mu}^{2}>\frac{(\mu +\delta ){(\mu +\delta +\gamma )}^{2}}{2+\mu +\delta +\gamma}$

*and*

*then the endemic equilibrium* ${E}^{\ast}({S}^{\ast},{I}^{\ast},{R}^{\ast})$ *of model* (1) *is globally asymptotically stable*.

From (31) and the comparison theorem [17], we know that there exists a positive integer ${T}_{1l}$ such that ${L}_{1}^{l}\le L(t)\le {L}_{1}^{m}$ for all $t>{T}_{1l}$, where ${L}_{1}^{l}=\frac{\mathrm{\Lambda}}{\mu +\gamma +\delta}-\epsilon $ and ${L}_{1}^{m}=\frac{\mathrm{\Lambda}}{\mu}+\epsilon $.

are the quadratic difference equations of the form $x(t+1)=rx(t)(1-x(t))$. When ${\mu}^{2}>\frac{(\mu +\delta ){(\mu +\delta +\gamma )}^{2}}{2+\mu +\delta +\gamma}$ and $\frac{\mu +\delta +\gamma}{\mu}<{R}_{0}<(1+\frac{2}{\mu +\delta +\gamma})\frac{\mu}{\mu +\delta}$, the well-know result on the population model $x(t+1)=rx(t)(1-x(t))$ and the comparison theorem imply that there exists an integer ${T}_{1i}>{T}_{1c}$ such that ${I}_{1}^{l}\le I(t)\le {I}_{1}^{m}$ for all $t>{T}_{1i}$, where ${I}_{1}^{l}={L}_{1}^{l}-\frac{{N}_{1}^{m}}{{R}_{0}}-\epsilon $ and ${I}_{1}^{m}={L}_{1}^{m}-\frac{{N}_{1}^{l}}{{R}_{0}}+\epsilon $.

When ${\mu}^{2}>\frac{(\mu +\delta ){(\mu +\delta +\gamma )}^{2}}{2+\mu +\delta +\gamma}$ and $\frac{\mu +\delta +\gamma}{\mu}<{R}_{0}<(1+\frac{2}{\mu +\delta +\gamma})\frac{\mu}{\mu +\delta}$, a similar procedure as aforementioned can imply that there exists an integer ${T}_{2i}>{T}_{2c}$ such that ${I}_{2}^{l}\le I(t)\le {I}_{2}^{m}$ for all $t>{T}_{2i}$, where ${I}_{2}^{l}={L}_{2}^{l}-\frac{{N}_{2}^{m}}{{R}_{0}}-\epsilon $ and ${I}_{2}^{m}={L}_{2}^{m}-\frac{{N}_{2}^{l}}{{R}_{0}}+\epsilon $.

*B*satisfy $|{\lambda}_{j}|<1$ ($j=1,2$), and the matrix series ${\sum}_{k=0}^{\mathrm{\infty}}{B}^{k}$ converges to ${(I-B)}^{-1}$. Under the conditions of Theorem 5, we can have ${lim}_{k\to \mathrm{\infty}}z(k)={(I-B)}^{-1}b$,

*i.e.*, ${z}^{\ast}=({I}_{\ast}^{m},{I}_{\ast}^{l})={({(I-B)}^{-1}b)}^{\tau}$ is the globally stable equilibrium of (37). Further calculation shows that

Therefore, the endemic equilibrium of system (1) is globally asymptotic stable when the conditions of Theorem 5 hold. □

There are some parameter values which can satisfy the conditions of Theorem 5. For example, if $\delta =\gamma =0$, those conditions become $\mu >0$, $1+\mu >0$, $1<{R}_{0}<1+\frac{2}{\mu}$. Then, for $\mu >0$, we know that the conditions of Theorem 5 will hold if *δ* and *γ* are small enough.

## 5 Numerical simulations

In this section, we carry out numerical simulations to demonstrate our theoretical results and the complex dynamics of model (1) for several sets of parameters and initial values. We use the SIR model to simulate the mumps infection in China, too.

### 5.1 Simulations of model (1)

The simulations in this subsection demonstrate our theoretical results or the complex dynamics of the model. The parameter values for those simulations are selected mathematically to let the model exhibit required dynamics.

*t*tends to infinity. For the same values of

*δ*,

*μ*,

*γ*, and Λ, the straightforward calculation yields ${R}_{0}\in (1,1.6)$ if $\beta \in (0.016,0.0256)$, which does not satisfy the condition ${R}_{0}>\frac{\mu +\gamma}{\mu}$ of Theorem 4. On the other hand, the simulation shows that the endemic equilibrium of model (1) may be globally asymptotically stable even if the condition ${R}_{0}>\frac{\mu +\gamma}{\mu}$ of Theorem 4 does not hold. The solution curves of the infected people for $\beta =0.02$ and two different initial values are given in Figure 1(b). Those simulations remind us that the condition ${R}_{0}>\frac{\mu +\gamma}{\mu}$ of Theorem 4 may not be necessary for the global stability. It is a pity that we cannot prove the global stability without the condition ${R}_{0}>\frac{\mu +\gamma}{\mu}$.

For the case $\delta >0$, we choose $\delta =0.001$, $\mu =0.01$, $\gamma =0.006$, and $\mathrm{\Lambda}=20$. Then we have $max\{\frac{\delta}{\mu -\delta -\gamma},\frac{\mu +\delta +\gamma}{\mu}\}=1.7$, $(1+\frac{2}{\mu +\delta +\gamma})\frac{\mu}{\mu +\delta}=107.86$, ${\mu}^{2}>\frac{(\mu +\delta ){(\mu +\delta +\gamma )}^{2}}{2+\mu +\delta +\gamma}$, and $\mu >\delta +\gamma $. Theorem 5 implies that the endemic equilibrium ${E}^{\ast}$ is globally asymptotically stable when $1.7<{R}_{0}<107.86$. Figure 1(c) shows the global stability of the endemic equilibrium of model (1) for $\beta =0.03$ and ${R}_{0}=1.765>1$. The straightforward calculation shows that ${R}_{0}\in (1,1.7)$ for those parameter values and $\beta \in (0.017,0.0289)$. Although ${R}_{0}\in (1,1.7)$ does not satisfy the conditions of Theorem 5, the numerical simulation shows that the endemic equilibrium of model (1) may still be globally asymptotically stable for ${R}_{0}\in (1,1.7)$. Figure 1(d) gives the number of the infected people for $\beta =0.02$ and ${R}_{0}=1.176$, where the condition of Theorem 5 does not hold, but the number of infected people approaches its equilibrium.

*β*), the endemic equilibrium is unique and globally asymptotically stable (see Figure 2(a)). When ${R}_{0}$ passes through 186.8, the endemic equilibrium losses its stability, and a stable periodic solution of period 2 appears (see Figure 2(b)). When ${R}_{0}$ passes through 226.8, the periodic solution of period 2 losses its stability, and a stable periodic solution of period four appears (see Figure 2(c)). As ${R}_{0}$ increases further, the periodic solution of period 4 becomes unstable, and a periodic solution of period 8 appears. The numerical simulation shows that the period-doubling bifurcation may continue and go to chaos (see Figure 2(d)).

### 5.2 Application of model (1)

The simulation in this subsection is an application of the SIR model to the mumps infection in China. The model parameters are estimated according to the demographic and epidemiological data in China from 2005 to 2010. We have compared the model simulation result of the number of annual mumps cases to the reported number of notifiable diseases.

Mumps, caused by the mumps virus, is an acute respirator infectious disease and it is spread from person to person by coughing or sneezing. Mumps usually starts with a fever and headache for a day or two. It then presents with swelling and soreness of the parotid salivary gland. The main symptom of mumps is the swollen, painful salivary glands on one or both sides of the face. The entire course of mumps infection is 10 to 14 days. The infected people will obtain permanent immunity after they get recovered from infection. The SIR model is suitable to describe the infection of mumps due to its permanent immunity. The host population is chosen to be individuals who are younger than 20 since mumps is common in children and adolescents, but rare among adults. The values of model parameters are estimated from the demographic and epidemiological data in China from 2005 to 2010.

In the application of the SIR model, the simulation time unit is taken as one day. All the parameter values will be estimated on the basis of that time unit. From the statistical yearbook of China, we know that the number of the host population (less than twenty years old) in 2005 is 359 million [18]. The annual birth rate in China from 2005 to 2010 is approximately 12/1,000, and the recruitment rate is taken as $\mathrm{\Lambda}=44\text{,}054$ [18–21]. The annual death rate of the population is 7.06/1,000 [22, 23], hence, the daily death rate is 19.34/1,000,000. Since the age of the host population is less than twenty, an individual should be removed when his/her age is over twenty. The daily removed rate is taken to be 1/7,300, and the value of parameter *μ* is estimated to be $\mu =0.00001934+0.000137=0.00015634$. From the fact that the entire course of mumps infection is 10 to 14 days, we take $\gamma =1/12=0.0833$ [24]. From the report on notifiable infectious diseases in China, we take $\delta =0.0000003$.

## 6 Conclusion

SIR models are suitable to describe the transmission of infectious diseases with life long immunity. A lot of continuous SIR models with various transmission rates have been formulated and studied. The global stability of the endemic equilibrium of the continuous SIR model has been investigated extensively and many sufficient conditions have been obtained. Nevertheless, the discrete SIR model and its dynamics are quite few. In this paper, we have formulated a discrete SIR epidemic model and studied its asymptotical behaviors. Especially, we have got some sufficient conditions for the global stability of the endemic equilibrium. We have also applied the SIR model to the mumps infection in China. The model simulation results match the reported data of the notifiable diseases well.

Although the SIR model considered in this paper is quite simple, it exhibits very complicated dynamical behavior. The complicated dynamics of the simple SIR model reminds us that we cannot expect to have the global stability of the endemic equilibrium when ${R}_{0}>1$. Other conditions should be given for the global stability. In this paper we have obtained sufficient conditions for the global stability of the endemic equilibrium. Although those conditions given in our paper are not satisfactory, our result is a good exploration of the challengeable problem. We expect to prove the global stability of the endemic equilibrium when ${R}_{0}$ is not large. The comparison principle used in this paper and the Lyapunov function may help us to have better results.

## Declarations

### Acknowledgements

This research is supported by NSFC grant 11171267, by IDRC of Canada grant 104519-010.

## Authors’ Affiliations

## References

- Allen L: Some discrete-time SI, SIR, and SIS epidemic models.
*Math. Biosci.*1994, 124: 83-105. 10.1016/0025-5564(94)90025-6View ArticleGoogle Scholar - Allen L, Burgin A: Comparison of deterministic and stochastic SIS and SIR models in discrete time.
*Math. Biosci.*2000, 163: 1-33. 10.1016/S0025-5564(99)00047-4MathSciNetView ArticleGoogle Scholar - Allen L, Flores D, Ratnayake R, Herbold J: Discrete-time deterministic and stochastic models for the spread of rabies.
*Appl. Math. Comput.*2002, 132: 271-292. 10.1016/S0096-3003(01)00192-8MathSciNetView ArticleGoogle Scholar - Allen L, van den Driessche P: The basic reproduction number in some discrete-time epidemic models.
*J. Differ. Equ. Appl.*2008, 14: 1127-1147. 10.1080/10236190802332308MathSciNetView ArticleGoogle Scholar - Castillo-Chavez C, Yakubu A: Discrete-time SIS models with complex dynamics.
*Nonlinear Anal.*2001, 47: 4753-4762. 10.1016/S0362-546X(01)00587-9MathSciNetView ArticleGoogle Scholar - Zhou Y, Paolo F: Dynamics of a discrete age-structured SIS models.
*Discrete Contin. Dyn. Syst., Ser. B*2004, 4: 843-852.Google Scholar - Zhou Y, Ma Z: Global stability of a class of discrete age-structured SIS models with immigration.
*Math. Biosci. Eng.*2009, 6: 409-425.MathSciNetView ArticleGoogle Scholar - Li X, Wang W: A discrete epidemic model with stage structure.
*Chaos Solitons Fractals*2005, 26: 947-958. 10.1016/j.chaos.2005.01.063MathSciNetView ArticleGoogle Scholar - Franke J, Yakubu A: Discrete-time SIS epidemic model in a seasonal environment.
*SIAM J. Appl. Math.*2006, 66: 1563-1587. 10.1137/050638345MathSciNetView ArticleGoogle Scholar - Cao H, Zhou Y: The discrete age-structured SEIT model with application to tuberculosis transmission in China.
*Math. Comput. Model.*2012, 55(3-4):385-395. 10.1016/j.mcm.2011.08.017MathSciNetView ArticleGoogle Scholar - Longili I: The generalized discrete-time epidemic model with immunity: a synthesis.
*Math. Biosci.*1986, 82: 19-41. 10.1016/0025-5564(86)90003-9MathSciNetView ArticleGoogle Scholar - Diekmann O, Heesterbeek J, Metz J:On the definition and the computation of the basic reproduction ratio ${R}_{0}$ in models for infectious diseases.
*J. Math. Biol.*1990, 35: 503-522.MathSciNetGoogle Scholar - van den 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 ArticleGoogle Scholar - Salceanu P, Smith H: Persistence in a discrete-time, stage-structured epidemic model.
*J. Differ. Equ. Appl.*2010, 16: 73-103. 10.1080/10236190802400733MathSciNetView ArticleGoogle Scholar - Zhao X:
*Dynamical Systems in Population Biology*. Springer, New York; 2003.View ArticleGoogle Scholar - Elaydi SN:
*An Introduction to Difference Equations*. Springer, New York; 2005.Google Scholar - Smith H, Walman P:
*The Theory of the Chemostat*. Cambridge University Press, Cambridge; 1995:261-268.View ArticleGoogle Scholar - National Bureau of Statistics of China:
*China Statistical Yearbook 2006*. China Statistics Press, Beijing; 2006.Google Scholar - National Bureau of Statistics of China:
*China Statistical Yearbook 2009*. China Statistics Press, Beijing; 2009.Google Scholar - National Bureau of Statistics of China:
*China Statistical Yearbook 2010*. China Statistics Press, Beijing; 2010.Google Scholar - National Bureau of Statistics of China:
*China Statistical Yearbook 2012*. China Statistics Press, Beijing; 2012.Google Scholar - National Bureau of Statistics of China. http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexch.htm. Accessed 22 Dec 2012Google Scholar
- National Bureau of Statistics of China:
*China Population Census Data in 2010*. China Statistics Press, Beijing; 2010.Google Scholar - Moneim I: Seasonally varying epidemics with and without latent period: a comparative simulation study.
*Math. Med. Biol.*2007, 24: 1-15.View ArticleGoogle Scholar - Chinese Center for Disease Control and Prevention: Yearly report on infectious disease in China. http://www.chinacdc.cn/tjsj/fdcrbbg/index.html. Accessed 22 Dec 2012Google Scholar
- Zhou Y, Khan K, Feng Z, Wu J: Projection of tuberculosis incidence with increasing immigration trends.
*J. Theor. Biol.*2008, 254: 215-228. 10.1016/j.jtbi.2008.05.026MathSciNetView ArticleGoogle Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.