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Dynamical behavior of a generalized ecoepidemiological system with prey refuge
Advances in Difference Equations volume 2018, Article number: 244 (2018)
Abstract
A generalized ecoepidemiological system with prey refuge is proposed in this paper. The saturation incidence kinetics and a generalized functional response are used to describe the contact process and the predation process, respectively. Based on mathematical issue, the local and global stability properties, Hopf bifurcation, and permanence of the dynamical system are investigated. Based on the ecological aspects, the impact of prey refuge on the dynamical consequences of the ecoepidemiological system and the mechanism of prey refuge are discussed. The results reveal that the stabilizing and destabilizing effects occur under some certain conditions. Based on epidemiological issue, the controlling strategies of the infectious disease are proposed. The results show that the prey refuge can control the spread of disease by the relative level of prey refuge. This study has resolved some basic and interesting issues for an ecoepidemiological system with a generalized response function and the effect of prey refuge.
Introduction
Ecoepidemiological systems, which are applied to describe predator and prey interactions with diseases in one population or both populations, have become important tools in analyzing the spread and control of infectious diseases, and hence have received much attention since the Kermac–Mckendric SIR model was proposed [1–10]. In ecoepidemiology, researchers study an ecological system with disease either in prey or in predator or in both populations [10–16]. Anderson and May [1] proposed an animal (predator) and plant (prey) model with infectious diseases and investigated the invasion, persistence, and spread of diseases. Chattopadhyay et al. investigated a predator–prey system with disease in the prey [4], and then applied their research to study the pelicans at risk in the Salton sea [5]. Saifuddin et al. [13] explored an ecoepidemiological system with disease in the prey and weak Allee in predator, and considered the complex dynamics including stability properties and bifurcations. Bairagi et al. [10] noticed the fact that the functional response plays an important role in determining the dynamical consequences of the population interactions, and hence conducted a comparative research on the stability aspects of a predator–prey system with a class of functional responses. Besides the published works mentioned above, more and more researchers have focused on the population interactions with diseases in prey or predator or both populations [1–16].
However, most of published research on ecoepidemiological systems incorporated a certain response function (e.g., Holling type functional response, Beddington–DeAngelis functional response) and a certain incidence rate (e.g., bilinear incidence, standard incidence, and saturation incidence), and investigated the dynamical behaviors of the considered systems [10, 11, 14–16]. As far as we know, few works have focused on a model with a generalized functional response and obtained a generalized conclusion. Motivated by these, in this paper we will propose a class of ecoepidemiological systems. That is to say, a generalized functional response is incorporated into an ecoepidemiological model with saturation incidence. Hence, the existing ecoepidemiological models become some special cases of ours.
In fact, there exist many ecological effects which influence the dynamical consequences of the species interactions, such as the effect of Allee effect, habitat complexity, harvesting, and prey refuge [17–19]. For the effect of prey refuge, the theoretical research and the field observations give a general conclusion that prey refuge can stabilize or destabilize the considered predation systems, and can prevent the prey extinction [16–36]. Here, the stabilizing effect means that the interior equilibrium point changes from an unstable state to a stable state due to increase in the degree of prey refuge [16, 21, 26]. Otherwise, the destabilizing effect is observed [19, 35]. For examples, Gonzalez–Olivares and Ramos–Jiliberto [21], and Ruxton [16] proposed two continuoustime predator–prey systems with the assumption that a constant proportion of prey could move to refuges. Their studies found a stabilizing effect on the dynamical consequences of the considered systems. The stabilizing effect was also observed in a generalized predator–prey system under some certain conditions [18, 19]. Most interestingly and importantly, Ma et al. [18, 19] proposed two generalized predator–prey systems incorporating prey refuge and observed a destabilizing effect. The above cited references reveal that the functional response of predator to prey population plays an important role in determining dynamical consequences of the interacting populations. However, few studies incorporate the effect of prey refuge into ecoepidemiological systems. Hence, this paper incorporates prey refuge into a species interaction with disease in prey.
Motivated by above analyses, in this paper we present a generalized ecoepidemiological system with the effect of prey refuge and the saturation incidence, and focus on the dynamical consequences of the proposed system and the explanations of the realistic meanings.
Model formulation
The basic model comprises two population subclasses: (i) prey population with density \(N(t)\) and (ii) predators with density \(Y(t)\), and is based on the following generalized predation model:
where all parameters are positive and have the following ecological meanings: r is the intrinsic growth rate of prey population, K is the environmental carrying capacity of prey population, c is the predation coefficients of predators to prey population, \(d_{2}\) is the natural death rate of predators, e is the conversion efficiency, \(\gamma\ (0<\gamma<1)\) denotes that a constant proportion of prey use refuges. The function \(\varphi(N)\) denotes the functional response of predators and satisfies the following assumptions:
Furthermore, it is assumed that the prey population (\(N(t)\)) is divided into two subclasses: the susceptible prey (\(S(t)\)) and the infected prey (\(I(t)\)) due to infectious disease. Beside, this paper gives the following assumptions:

(1)
The susceptible prey is capable of reproducing only and the infected prey is removed by death at a natural rate \(d_{1}\).

(2)
The disease is spread only among the prey population and the disease is not genetically inherited. The infected prey does not become immune.

(3)
Susceptible prey becomes infected with the saturation incidence kinetics \(\frac{\beta SI}{\alpha+I}\), where β measures the force of infection and α is the inhibition effect.

(4)
Predators consume susceptible and infected prey with predation coefficients \(c_{1}\) and \(c_{2}\), respectively. The consumed prey is converted into predator with efficiency e.
Combining the generalized predation model (2.1) and the above assumptions, a generalized ecoepidemiological system with prey refuge and disease in prey is proposed by the following equations:
with the initial conditions
Using the following change of variables
and rewriting system (2.1) with \((S,I,Y)\), the following system can be obtained:
Equilibria
All equilibrium points of system (2.4) can be obtained by solving the following equations:

(1)
The trivial equilibrium point \(E_{0}(0,0,0)\).

(2)
The equilibrium point in the absence of the infected prey and predators \(E_{1}((1\gamma)K,0,0)\).

(3)
The diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\), where
$$\tilde{S}=\varphi^{1} \biggl(\frac{d_{2}}{ec_{1}} \biggr),\qquad \tilde{Y}= \frac{er\tilde{S}}{d_{2}} \biggl(1\frac{\tilde{S}}{(1\gamma)K} \biggr). $$If \(\gamma<1\frac{1}{K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) has its ecological meanings.

(4)
The coexisting equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\), where
$$\begin{aligned} &S^{*}=\frac{(d_{1}I^{*}+c_{2}Y^{*}\varphi(I^{*})+I^{*})((1\gamma)\alpha+I^{*})}{\beta I^{*}}, \\ &I^{*}=\varphi^{1}\biggl(\frac{d_{2}ec_{1}\varphi(S^{*})}{ec_{2}}\biggr), \\ &Y^{*}=\frac{rS^{*}(1\frac{S^{*}+I^{*}}{(1\gamma)K})S^{*}I^{*}d_{1}I^{*}}{c_{1}\varphi(S^{*})+c_{2}\varphi(I^{*})}. \end{aligned}$$If \(0<\gamma<1\frac{rS^{*}(S^{*}+I^{*})}{K(rS^{*}d_{1}I^{*})}\), then the equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\) is a positive equilibrium point. Otherwise, it has no ecological meanings anymore.
Stability property
Stability of the diseasefree equilibrium
Local stability of the diseasefree equilibrium
In this section, we study the local stability properties of the equilibrium points of system (2.4). Especially, the local stability analysis for the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is investigated in this section.
Firstly, it is easy to show that the trivial equilibrium point \(E_{0}(0,0,0)\) is a saddle point and the equilibrium point \(E_{1}( (1\gamma) K,0,0)\) is stable if \(\beta<\frac{\alpha d_{1}}{K}\) and \(\gamma>1\frac{1}{K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\).
Again, the roots of the characteristic equation of the community matrix corresponding to \(E_{2}(\tilde{S},0,\tilde{Y})\) are \(\frac{\beta \tilde{S}}{\alpha}d_{1}c_{2}(1\gamma)\varphi'(0)\tilde{Y}\) and the roots of the following equation:
where
and
Hence, the roots of Eq. (4.1) will have negative real parts if \(A<0\), which implies that
Therefore, the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) is locally asymptotically stable iff
and
Inequality (4.2) is equivalent to the following term:
Furthermore, inequality (4.2) is equivalent to the following cases:

(1)
If \(\frac{d_{2}}{c_{1}\varphi'(\varphi^{1}(\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})} < e<\frac{2d_{2}}{c_{1}\varphi'(\varphi^{1}(\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}\), then inequality (4.3) always holds.

(2)
If \(e<\frac{d_{2}}{c_{1}\varphi'(\varphi^{1}(\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}\), then inequality (4.3) holds when \(\gamma>1\frac{\varphi^{1}(\frac{d_{2}}{ec_{1}})}{K}[\frac{2d_{2}ec_{1}\varphi'(\varphi^{1} (\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}{d_{2}ec_{1}\varphi'(\varphi^{1} (\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}]\).

(3)
If \(e>\frac{2d_{2}}{c_{1}\varphi'(\varphi^{1}(\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}\), then inequality (4.3) holds when \(\gamma<1\frac{\varphi^{1}(\frac{d_{2}}{ec_{1}})}{K}[\frac{2d_{2}ec_{1}\varphi'(\varphi^{1} (\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}{d_{2}ec_{1}\varphi'(\varphi^{1} (\frac{d_{2}}{ec_{1}}))\varphi^{1}(\frac{d_{2}}{ec_{1}})}]\).
Next, define
According to the above analyses, the following conclusions are obtained.
Theorem 4.1
Supposing that \(\underline{B} <\beta< \overline{B}\) and \(e<\underline{E} \), we have

(1)
If \(0<\gamma<1 \dfrac{K_{1}}{K} \varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable;

(2)
If \(1\dfrac{K_{1}}{K} \varphi^{1}(\frac{d_{2}}{ec_{1}})<\gamma< 1 \frac{K_{3}}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is locally asymptotically stable;

(3)
If \(1 \frac{K_{3}}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})<\gamma< 1\frac{1}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable.
Theorem 4.2
Assuming that \(\underline{B} <\beta< \overline{B}\) and \(e> \overline{E}\), we obtain

(1)
If \(0<\gamma<1\frac{K_{3}}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is locally asymptotically stable;

(2)
If \(1\frac{K_{3}}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}}) <\gamma<1\frac{1}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable.
Theorem 4.3
Assuming that \(\beta< \underline{B}\) and \(e<\underline{E} \), we obtain

(1)
If \(0<\gamma<1\dfrac{K_{1}}{K} \varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable;

(2)
If \(1\dfrac{K_{1}}{K} \varphi^{1}(\frac{d_{2}}{ec_{1}})<\gamma<1 \frac{1}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is locally asymptotically stable.
Theorem 4.4
Supposing that \(e>\overline{E}\) and \(\beta>\overline{B}\) or \(\underline{E}< e< \overline{E}\) and \(\beta>\underline{B}\), we have

(1)
If \(0<\gamma<1\frac{K_{3}}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is locally asymptotically stable;

(2)
If \(1\frac{K_{3}}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})<\gamma<1 \frac{1}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable.
Theorem 4.5
Supposing that \(\beta< \underline{B}\) and \(e>\overline{E}\), we have

(1)
If \(0<\gamma<1\dfrac{K_{1}}{K} \varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is locally asymptotically stable;

(2)
If \(1\dfrac{K_{1}}{K} \varphi^{1}(\frac{d_{2}}{ec_{1}})<\gamma<1 \frac{1}{\alpha K}\varphi^{1}(\frac{d_{2}}{ec_{1}})\), then the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable.
Theorem 4.6
Assuming that \(\beta< \underline{B}\) and \(\underline{E}< e< \overline{E}\), we have that the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is always locally asymptotically stable.
Next, three tables are given to list the above results. Define the following notations:
the conditions and conclusions of Theorem 4.1–Theorem 4.6 are listed in Table 1, Table 2, and Table 3 in which

(1)
S. means the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) is stable;

(2)
\(U. S\). means the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) is unstable;

(3)
\(A. S\). means the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) is always stable;

(4)
\(U. D\). means the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) is undefined.
Table 1 shows the stability properties of the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) when \(e<\underline{E}\).
Table 2 shows the stability properties of the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) when \(\underline{E}< e< \overline{E}\).
Table 3 shows the stability properties of the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) when \(e> \overline{E}\).
Global stability of the diseasefree equilibrium
In this section, we consider the global stability of the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\).
Theorem 4.7
If \(\beta>\frac{\bar{\alpha}(d_{1}+\varphi'(0)c_{2}) \tilde{Y}}{\tilde{S}}\) in which \(\bar{\alpha}=\alpha(1\gamma)\), then system (2.4) with initial conditions (2.3) is to be globally asymptotically stable around the diseasefree equilibrium point \(E_{2}(\tilde{S},0,\tilde{Y})\) in the region \(\Sigma_{1}=\{(S,I,Y)S \geq \tilde{S}, I \geq 0, Y \geq \tilde{Y}\} \).
Proof
We rewrite system (2.4) with initial conditions (2.3) as the following form:
where
Let us define
Next, we consider a Lyapunov function defined as follows:
Now, by simple computation, we obtain that
Again, we have
Furthermore, we obtain that
and
in which \(a=\lim_{x\rightarrow 0}\frac{\varphi(x)}{x}=\varphi'(0)\).
Thus, it is obtained that \(G_{2}>0\) while \(\beta>\frac{\bar{\alpha}(d_{1}+\varphi'(0)c_{2}) \tilde{Y}}{\tilde{S}}\).
Therefore, \(\frac{dV}{dt}<0\) in the region \(\Sigma=\{(S,I,Y)S \geq \tilde{S}, I \geq 0, Y \geq \tilde{Y}\} \). Hence the theorem is proved. □
Stability of the positive equilibrium
Local stability of the positive equilibrium
In this section, we consider the local stability of the positive equilibrium point \(E_{3}(S^{*},I^{*}, Y^{*})\).
Theorem 4.8
If \(A_{1}<0\) and \(A_{1}A_{2}< A_{3}<0\), then the positive equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\) is locally asymptotically stable.
Proof
The characteristic equation of the community matrix corresponding to the positive equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\) is as follows:
where
Now, define
Therefore, the characteristic equation of the positive equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\) can be rewritten as the following form:
According to Routh–Hurwitz’s criteria, the necessary and sufficient conditions for local stability of positive point are \(A_{1}<0\) and \(A_{1}A_{2}< A_{3}<0\). Hence the theorem is proved. □
Global stability of the positive equilibrium
In this section, we consider the global stability of the positive equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\).
Theorem 4.9
System (2.4) with initial conditions (2.3) is to be globally asymptotically stable around the positive equilibrium point \(E_{3}(S^{*},I^{*},Y^{*})\) in the region \(\Sigma_{2}=\{(S,I,Y)Y>Y^{*}, 0< S < S^{*}, 0<I <I^{*} or Y<Y^{*}, S> S^{*}, I >I^{*} \} \).
Proof
We first choose a Lyapunov function which is defined as follows:
Now, by simple computation, we obtain that
It is clear that \(\frac{dW}{dt}<0\) in \(\Sigma_{2}\). Hence the theorem is proved. □
Hopf bifurcation
In this section, we consider the Hopf bifurcation near the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) of system (2.4).
The characteristic equation of system (2.4) at the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) is given by the following form:
where
It is noted that the expressions \(A(\gamma)\), \(B(\gamma)\), and \(C(\gamma)\) are smooth functions of γ.
In order to determine the instability of system (2.4), let us consider γ (the effect of prey refuge) as a bifurcation parameter. For this purpose, let us firstly give the following lemma.
Lemma 5.1
(Hopf bifurcation theorem [16])
If \(A(\gamma)\), \(B(\gamma)\), and \(C(\gamma)\) are smooth functions of γ in an open interval about \(\gamma \in R\), such that the characteristic equation (5.1) has

(1)
a pair of complex eigenvalues \(\lambda=p(\gamma)\pm i q(\gamma)\) with \(p(\gamma)\) and \(q(\gamma)\) ∈R, so that they become purely imaginary at \(\gamma=\gamma_{0}\) and \(\frac{dp(\gamma)}{d\gamma} (\gamma=\gamma_{0})\neq 0 \),

(2)
the other eigenvalue is negative at \(\gamma=\gamma_{0}\),
then a Hopf bifurcation occurs around an equilibrium point of the considered system at \(\gamma=\gamma_{0}\) (i.e., a stability change of an equilibrium point of the considered system accompanied by the creation of a limit cycle at \(\gamma=\gamma_{0}\)).
Based on Lemma 5.1, the following conclusion can be obtained.
Theorem 5.2
If \(A<0\) and \(B<0\), then system (2.4) possesses a Hopf bifurcation around the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\).
Proof
Suppose the value \(\gamma_{0}\) is equal to
or
which are the roots of \(A=0\) or \(B=0\), respectively.
Firstly, for \(\gamma=\gamma_{0}\), the characteristic equation of system (2.4) at the diseasefree equilibrium \(E_{2}(\tilde{S},0,\tilde{Y})\) becomes the following form:
It is clear that the roots of the above equation are \(\lambda_{1}=A<0\), \(\lambda_{2}=i\sqrt{C}\), and \(\lambda_{1}=i\sqrt{C}\).
Hence, there exist a pair of purely imaginary eigenvalues and a strictly negative real eigenvalue.
Secondly, for γ in a neighborhood of \(\gamma_{0}\), the roots have the form \(\lambda_{1}=A<0\), \(\lambda_{2}=p_{1}(\gamma)+ip_{2}(\gamma)\) and \(\lambda_{3}=p_{1}(\gamma)ip_{2}(\gamma)\) in which \(p_{1}(\gamma)\) and \(p_{2}(\gamma)\) are real.
Next, we shall verify the transversality condition:
Substituting \(\lambda_{2}=p_{1}(\gamma)+ip_{2}(\gamma)\) into the characteristic Eq. (5.1), we get
Taking derivatives of both sides of (5.2) with respect to γ, we have
Comparing real and imaginary parts from both sides of Eq. (5.3), we obtain
where
Define \(E_{1}=(3p_{1}^{2}3p_{2}^{2}2p_{1}D_{1}+D_{2})\), \(E_{2}=(6p_{1}p_{2}2p_{2}D_{1})\), \(E_{3}=(p_{1}p_{2}+P_{1}D'_{2}P_{1}^{2}D'_{1}D'_{3})\), and \(E_{4}=(P_{2}D'_{2}2P_{1}P_{1}D'_{1})\), then Eqs. (5.4) become of the following form:
The value of \(p'_{1}\) can be obtained by solving Eqs. (5.5)
For \(p_{1}=0\) and any other possible value of \(p_{2}\) at \(\gamma=\gamma_{0}\), the \(E_{1}E_{3}+E_{2}E_{4}\) is always unequal to zero since \(\frac{D_{2}D_{3}}{3D'_{3}+2D_{1}D'_{2}}\neq \frac{A^{2}4B}{C^{2}}\).
Therefore, \(\lambda_{1}=A<0\) and
Hence, according to Lemma 5.1, the theorem is proved. □
Permanence
In this section, we prove the permanence of system (2.4) with initial conditions (2.3) under the condition \(X\varphi'(X)<\varphi(X)\ (X>0)\).
Definition 6.1
([6])
If there exist positive constants \(m_{S}\), \(M_{S}\), \(m_{I}\), \(M_{I}\), \(m_{Y}\), and \(M_{Y}\) such that each solution \((S(t),I(t),Y(t))\) of system (2.4) satisfies
then system (2.4) is permanent. Otherwise, it is nonpermanent.
In order to consider the permanence of system (2.4), we consider the following auxiliary system:
in which A, C, and D are nonnegatively constant numbers, B is positive and bounded by \(\frac{r}{c_{1}}\).
For system (6.1), we can obtain the following result.
Lemma 6.2
The positive equilibrium point of system (6.1) is globally asymptotically stable when it exists.
Proof
The positive equilibrium point of system (6.1) is \(\bar{P}(\bar{S},\bar{I})\), where
The equilibrium point \(\bar{P}(\bar{S},\bar{I})\) is positive if \(\beta>\frac{r(\alpha+C)(d+c_{2}D)}{K(rc_{1}B)}\).
The Jacobian matrix of system (6.1) at \(\bar{P}(\bar{S},\bar{I})\) is
Clearly, \(\operatorname{Tr}J=\frac{r\bar{S}}{K}<0\) and \(\operatorname{Det}J=\frac{\beta(r(\alpha+A)+\beta K)\bar{S}\bar{I}}{K(\alpha+A)(\alpha+C)}>0\).
Therefore, the positive equilibrium point \(\bar{P}(\bar{S},\bar{I})\) is locally asymptotically stable.
Next, we show its global asymptotic stability.
We consider the following function:
From the construction of V, it is easily seen that V is positive definite in the region \(\Sigma=\{(S,I)S \geq 0, Y \geq 0\} \) and \(V(\bar{S},\bar{I})=0\).
By simple computation, we obtain that
Let \(E=\frac{(\alpha+C)(r(\alpha+A)+\beta K)}{\beta K(\alpha+A)}>0\), then \(\frac{dV}{dt}\leq 0\) in the region \(\Sigma=\{(S,I)S \geq 0, Y \geq 0\}\).
Hence the theorem is proved. □
Next, we consider the permanence of system (2.4) with initial conditions (2.3).
Theorem 6.3
If \(\beta>\frac{r(\alpha+(1\gamma)K)(d_{1}+c_{2}\varphi'(0) M_{Y})}{(1\gamma)K(rc_{1}\varphi'(0) M_{Y})}>0\) and \(0< M_{Y}<\frac{r}{c_{1}\varphi'(0)}\), then system (2.4) with initial conditions (2.3) is permanent. Otherwise, it is nonpermanent.
Proof
From the first and second equations of system (2.4), we obtain that
By Lemma 6.2 and a standard comparison theorem, we have
where \(\hat{S}=\frac{\alpha d_{1}(1\gamma)}{\beta}>0, \hat{I}=\frac{((1\gamma)\alpha+K)((1\gamma)rK\hat{S})}{(1\gamma) (r\alpha+\beta K)+ K}>0\) if \(\beta>\frac{\alpha d_{1}}{rK}\).
Thus, for any given \(\varepsilon>0\), there exists \(T_{1}>0\) such that, for any \(t>T_{1}>0\), we get
From the third equation, we obtain that
It is easy to show that there is \(M_{Y}>0\) such that
Hence, for any given \(\varepsilon>0\), there exists \(T_{2}>T_{1}>0\) such that, for any \(t>T_{2}>0\), we have
Again, from the first and second equations, we get
where M is the maximum value of the function \(F(X)\), where
Notice that the function \(F(X)\) has the maximum and minimum values since it is continuous on the closed interval \([0,K]\).
By Lemma 6.2 and a standard comparison theorem, we have
where
when \(\beta>\frac{r(\alpha+(1\gamma)K)(d_{1}+c_{2}\varphi'(0) M_{Y})}{(1\gamma)K(rc_{1}\varphi'(0) M_{Y})}>0\) and \(0< M_{Y}<\frac{r}{c_{1}\varphi'(0)}\).
Thus, for any given \(\varepsilon>0\), there exists \(T_{3}>T_{2}>T_{1}>0\) such that, for any \(t>T_{3}>0\), we obtain
Again, from the third equation, we get
It is easy to show that there is \(m_{Y}>0\) such that
According to Definition 6.1, system (2.4) with initial conditions (2.3) is permanent under some strict conditions. Hence the theorem is proved. □
Examples
Example 1
If \(\varphi(X)=\frac{X}{a+X}\), then system (2.2) becomes the following system:
It is easy to obtain the diseasefree equilibrium point \(E_{1}(S_{1},0,Y_{1})\) of system (7.1), where
By simple computation, the equilibrium point \(E_{1}(S_{1},0,Y_{1})\) has its ecological meanings if \(\gamma<1\frac{ad_{2}}{K(c_{1}ed_{2})}\).
According to the theorems in Sect. 4, we can obtain the following propositions:

(1)
Assuming that \(\frac{\alpha d_{1}}{K}<\beta<\frac{\alpha d_{1}}{K}+ \frac{\alpha c_{1}e(c_{2}er+d_{1}(c_{1}ed_{2}))}{ d_{2}K(c_{1}ed_{2})}\), then

(a)
If \(0<\gamma<1\frac{a(c_{1}e+d_{2})}{K(c_{1}ed_{2})}\), then the diseasefree equilibrium point of system (7.1) is unstable;

(b)
If \(1\frac{a(c_{1}e+d_{2})}{K(c_{1}ed_{2})}<\gamma< 1\frac{ad_{2}(\alpha c_{2}er+\beta K(c_{1}ed_{2}))}{\alpha K(c_{1}ed_{2})(c_{2}er+d_{1}(c_{1}ed_{2}))}\), then the diseasefree equilibrium point of system (7.1) is locally asymptotically stable;

(c)
If \(1\frac{ad_{2}(\alpha c_{2}er+\beta K(c_{1}ed_{2}))}{\alpha K(c_{1}ed_{2})(c_{2}er+d_{1}(c_{1}ed_{2}))}<\gamma<1\frac{ad_{2}}{K(c_{1}ed_{2})}\), then the diseasefree equilibrium point of system (7.1) is unstable.

(a)

(2)
Assuming that \(\beta<\frac{\alpha d_{1}}{K}\), then the diseasefree equilibrium point of system (7.1) is always locally asymptotically stable.
Example 2
If \(\varphi(X)=\frac{X^{2}}{a+X^{2}}\), then system (2.2) becomes the following system:
It is easy to obtain the diseasefree equilibrium point \(E_{2}(S_{2},0,Y_{2})\) of system (7.2), where
Clearly, if \(\gamma<1\frac{1}{K}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the equilibrium point \(E_{2}(S_{1},0,Y_{1})\) has its ecological meanings.
According to the theorems in Sect. 4, we can obtain the following propositions:

(1)
Assuming that \(\frac{\alpha d_{1}}{K}<\beta<\frac{2\alpha d_{1}d_{2}}{K(2d_{2}c_{1}e)}\), then we have

(a)
If \(0<\gamma<1\frac{2 d_{2}}{K(2d_{2}c_{1}e)}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.2) is unstable;

(b)
If \(1\frac{2d_{2}}{K(2d_{2}c_{1}e)}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}<\gamma< 1\frac{\beta}{\alpha d_{1}}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.2) is locally asymptotically stable;

(c)
If \(1\frac{\beta}{\alpha d_{1}}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}<\gamma<1\frac{1}{K}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.2) is unstable.

(a)

(2)
Assuming that \(\beta<\frac{\alpha d_{1}}{K}\), then we have

(a)
If \(0<\gamma<1\frac{2 d_{2}}{K(2d_{2}c_{1}e)}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.2) is unstable;

(b)
If \(1\frac{2 d_{2}}{K(2d_{2}c_{1}e)}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}<\gamma<1\frac{1}{K}\sqrt{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.2) is locally asymptotically stable.

(a)
Example 3
If \(\varphi(X)=\frac{X^{p}}{a+X^{p}}\), then system (2.2) becomes the following system:
It is easy to obtain the diseasefree equilibrium point \(E_{3}(S_{3},0,Y_{3})\) of system (7.3), where
Clearly, if \(\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the equilibrium point \(E_{2}(S_{1},0,Y_{1})\) has its ecological meanings.
According to the theorems in Sect. 4, we can obtain the following propositions:

(I)
Supposing that \(\frac{\alpha d_{1}}{K}<\beta<\frac{\alpha d_{1}(2c_{1}ep(c_{1}ed_{2}))}{K(c_{1}ep(c_{1}ed_{2}))}\), then we obtain that

(1)
Assuming that \(p<\frac{c_{1}e}{c_{1}ed_{2}}\), then

(a)
If \(0<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]\), then the diseasefree equilibrium point of system (7.3) is unstable;

(b)
If \(1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]<\gamma< 1\frac{\beta}{\alpha d_{1}}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.3) is locally asymptotically stable;

(c)
If \(1\frac{\beta}{\alpha d_{1}}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.3) is unstable.

(a)

(2)
Assuming that \(p>\frac{2c_{1}e}{c_{1}ed_{2}}\), then

(a)
If \(0<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]\), then the diseasefree equilibrium point of system (7.3) is locally asymptotically stable;

(b)
If \(1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.3) is unstable.

(a)

(3)
Assuming that \(\frac{c_{1}e}{c_{1}ed_{2}}< p<\frac{2c_{1}e}{c_{1}ed_{2}}\), then

(a)
If \(0<\gamma<1\frac{\beta}{\alpha d_{1}}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.3) is locally asymptotically stable;

(b)
If \(1\frac{\beta}{\alpha d_{1}}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (7.3) is unstable.

(a)

(1)

(II)
Supposing that \(\beta<\frac{\alpha d_{1}}{K}\), then we obtain

(1)
Assuming that \(p<\frac{c_{1}e}{c_{1}ed_{2}}\), then

(a)
If \(0<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]\), then the diseasefree equilibrium point of system (20) is unstable;

(b)
If \(1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (20) is locally asymptotically stable.

(a)

(2)
Assuming that \(p>\frac{2c_{1}e}{c_{1}ed_{2}}\), then

(a)
If \(0<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]\), then the diseasefree equilibrium point of system (20) is locally asymptotically stable;

(b)
If \(1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}[\frac{2c_{1}ep(c_{1}ed_{2})}{ c_{1}ep(c_{1}ed_{2})}]<\gamma<1\frac{1}{K}\sqrt[p]{\frac{ad_{2}}{c_{1}ed_{2}}}\), then the diseasefree equilibrium point of system (20) is unstable.

(a)

(3)
Assuming that \(\frac{c_{1}e}{c_{1}ed_{2}}< p<\frac{2c_{1}e}{c_{1}ed_{2}}\), then the diseasefree equilibrium point of system (20) is always locally asymptotically stable.

(1)
Discussion
In this paper, a generalized system describing predator–prey interaction with prey refuges and disease in prey is proposed. Based on mathematical issues, the dynamical properties (stability, Hopf bifurcation, and permanence) are investigated, and the sufficient conditions which guarantee these properties are obtained (see Sects. 4, 5, 6). However, based on ecological and epidemiological issues, our analyses reveal that the effect of prey refuge, the force of infection, and the converting efficiency of predators play an important role in the dynamical properties of the proposed system. The ecological and epidemiological meanings will be discussed according to the following aspects:

(1)
If the infectious rate of prey is smaller than the threshold \(\underline{B}\), then the effect of prey refuge has a stabilizing impact when the converting ratio is relatively small. The contrary effect happens under the larger converting ratio for predators. However, the effect of prey refuge has no influence under the middle converting ratio and the disease will vanish. These results show that the infectious disease can be prevented by controlling the degree of the effect of prey refuge under the certain converting ratio.

(2)
If the infectious rate of prey is larger than the threshold B̅, the dynamical consequences of the considered system will be relatively simple. In this case, in order to control the infectious disease which will break out, the effect of prey refuge must be relatively small.

(3)
If the infectious rate is smaller than \(\underline{B}\) and larger than B̅, the stabilizing effect and destabilizing effect will occur under the small converting ratio, and the destabilizing effect happens while the converting ratio is larger than the threshold B̅. Therefore, for the small converting ratio, the middle level of prey refuge can be applied to control the infectious disease. But, for the larger converting ratio, the smaller level of prey refuge is required to control the disease.
According to the above discussions, we find that the effect of prey refuge has a destabilizing impact on the considered ecoepidemiological system in most cases. The reasons for this phenomenon may be the disease in prey population, the relatively large converting efficiency of predators, and the complex functional response to prey population. However, according to the published research works, the stabilizing effect is often observed in a simple predator–prey system with no disease in prey and/or predators. The stabilizing effect of prey refuge is observed when the infectious rate of prey and the converting ratio of predators are relatively small. Furthermore, the most complex dynamics is observed when the converting ratio of predators is relatively small and the infectious rate of prey is moderate. At this moment, three kinds of equilibria can be reached: the unstable point with stable orbits, the stable coexistence of prey and predators, and then the unstable point again. That is to say, the increase of the infectious rate can lead to the lost of stability. Therefore, the stabilizing effect and/or destabilizing effects mainly are determined by the essential properties of predator, such as the converting coefficient, the type of functional response of predators to prey population. Hence, it is rational to guess that the predation strategy and the evolutionary strategy of predator population are the main factors which induce the partially hiding behavior (prey refuge) of prey population.
On the other hand, our results show that the effect of prey refuge can be applied to control the spread of the infectious disease. The level of prey refuge needed to control the disease spread is mainly determined by the infectious rate and the converting ratio of predators.
References
Anderson, R.M., May, R.M.: The invasion, persistence and spread of infectious diseases within animal and plant communities. Philos. Trans. R. Soc. Lond. B 314, 533–570 (1986)
Hadeler, K.P., Freedman, H.I.: Predatorprey populations with parasite infection. J. Math. Biol. 27, 609–631 (1989)
Xiao, Y., Tang, S.: Dynamics of infection with nonlinear incidence in a simple vaccination model. Nonlinear Anal., Real World Appl. 11, 4154–4163 (2010)
Chattopadhyay, J., Arino, O.: A predatorprey model with disease in the prey. Nonlinear Anal., Theory Methods Appl. 36, 747–766 (1999)
Chattopadhyay, J., Bairagi, N.: Pelicans at risk in Salton Seaan ecoepidemiological study. Ecol. Model. 136, 103–112 (2001)
Venturino, E.: Epidemics in predatorprey models: disease in the predator. IMA J. Math. Appl. Med. Biol. 19, 185–205 (2002)
Hethcote, H.W., Wang, W., Han, L., Ma, Z.: A predatorprey model with infected prey. Theor. Popul. Biol. 66, 259–268 (2004)
Hall, S.R., Dufy, M.A., Caceres, C.E.: Selective predation and productivity jointly drive complex behavior in hostparasite systems. Am. Nat. 165, 70–81 (2005)
Fenton, A., Rands, S.A.: The impact of parasite manipulation and predator foraging behavior on predatorprey communities. Ecology 87, 2832–2841 (2006)
Bairagi, N., Roy, P.K., Chattopadhyay, J.: Role of infection on the stability of a predatorprey system with several functional responses—A comparative study. J. Theor. Biol. 248, 10–25 (2007)
Bhattacharyya, R., Mukhopadhyay, B.: On an ecoepidemiological model with prey harvesting and predator switching: local and global perspectives. Nonlinear Anal., Real World Appl. 11(455), 3824–3833 (2010)
Bob, W., George, A.K., Krishna, D.: Stabilization and complex dynamics in a predatorprey model with predator suffering from an infectious disease. Ecol. Complex. 8, 113–122 (2011)
Saifuddin, M., Biswas, S., Samanta, S., Sarkar, S., Chattopadhyay, J.: Complex dynamics of an ecoepidemiological model with different competition coefficients and weak Allee in the predator. Chaos Solitons Fractals 91, 270–285 (2016)
Biswas, S., Samanta, S., Chattopadhyaya, J.: A model based theoretical study on cannibalistic preypredator system with disease in both populations. Differ. Equ. Dyn. Syst. 23, 327–370 (2015)
Biswas, S., Sasmal, K.S., Samanta, S., Saifuddin, M., Khan, Q.J.A., Alquranc, M.: A delayed ecoepidemiological system with infected prey and predator subject to the weak Allee effect. Math. Biosci. 263, 198–208 (2015)
Liu, X.: Bifurcation of an ecoepidemiological model with a nonlinear incidence rate. Appl. Math. Comput. 218, 2300–2309 (2011)
Holling, C.S.: Some characteristics of simple types of predation and parasitism. Can. Entomol. 91, 385–398 (1959)
Ma, Z., Li, W., Zhao, Y., Wang, W., Zhang, H., Li, Z.: Effects of prey refuges on a predatorprey model with a class of functional responses: the role of refuges. Math. Biosci. 218(485), 73–79 (2009)
Ma, Z., Wang, S., Wang, T., Tang, H.: Stability analysis of preypredator system with Holling type functional response and prey refuge. Adv. Differ. Equ. 2017, 243 (2017)
Ruxton, G.D.: Short term refuge use and stability of predatorprey models. Theor. Popul. Biol. 47, 1–17 (1995)
GonzalezOlivars, E., RamosJiliberto, R.: Dynamics consequences of prey refuges in a simple model system: more prey, few predators and enhanced stability. Ecol. Model. 166, 135–146 (2003)
Hassel, M.P., May, R.M.: Stability in insect hostparasite models. J. Anim. Ecol. 42, 693–725 (1973)
Smith, J.M.: Models in Ecology. Cambridge Univ. Press, Cambridge (1974)
Murdoch, W.W., StewartOaten, A.: Predation and population stability. Adv. Ecol. Res. 9, 1–13 (1975)
Hassell, M.P.: The Dynamics of Arthropod PredatorPrey Systems. Princeton Univ. Press, Princeton (1978)
Sih, A.: Prey refuges and predatorprey stability. Theor. Popul. Biol. 31, 1–12 (1987)
Ives, A.R., Dobson, A.P.: Antipredator behavior and the population dynamics of simple predatorprey systems. Am. Nat. 130, 431–447 (1987)
Hochberg, M.E., Holt, R.D.: Refuge evolution and the population dynamics of coupled hostparasitoid associations. Evol. Ecol. 9, 633–661 (1995)
Michalski, J., Poggiale, J.C., Arditi, R., Auger, P.M.: Macroscopic dynamic effects of migration in patchy predatorprey systems. J. Theor. Biol. 185, 459–474 (1997)
Krivan, V.: Effects of optimal antipredator behavior of prey on predatorprey dynamics: the role of refuges. Theor. Popul. Biol. 53, 131–142 (1998)
Ghosh, J., Sahoo, B., Poria, S.: Preypredator dynamics with prey refuge providing additional food to predator. Chaos Solitons Fractals 96, 110–119 (2017)
Kar, T.K.: Stability analysis of a preypredator model incorporating a prey refuge. Commun. Nonlinear Sci. Numer. Simul. 10, 681–691 (2005)
Hoy, M.A.: Almonds (California). In: Helle, W., Sabelis, M.W. (eds.) Spider Mites: Their Biology, Natural Enemies and Control, World Crop Pests. 1B. Elsevier, Amsterdam (1985)
Huang, Y., Chen, F., Zhong, L.: Stability analysis of a preypredator model with Holling type III response function incorporating a prey refuge. Appl. Math. Comput. 182, 672–683 (2006)
May, R.M.: Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton (1974)
McNair, J.M.: The effects of refuges on predatorprey interactions: a reconsideration. Theor. Popul. Biol. 29, 38–63 (1986)
Acknowledgements
All authors would like to thank the editor and the anonymous referees for their valuable comments and suggestions.
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This work was supported by the Fundamental Research Funds for the Central Universities of Northwest Minzu University (No. 31920180116), Gansu Provincial firstclass discipline program of Northwest Minzu University, the Fundamental Research Funds for the Central Universities (No.lzujbky2017166) and the National Natural Science Foundation of China (No. 31560127).
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Wang, S., Ma, Z. & Wang, W. Dynamical behavior of a generalized ecoepidemiological system with prey refuge. Adv Differ Equ 2018, 244 (2018). https://doi.org/10.1186/s136620181704x
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DOI: https://doi.org/10.1186/s136620181704x
Keywords
 Ecoepidemiological system
 Generalized function response
 Prey refuge
 Dynamical behavior
 Stabilizing/Destabilizing effect
 Method of disease control