Global dynamic analysis of a vector-borne plant disease model
© Shi et al.; licensee Springer. 2014
Received: 20 December 2013
Accepted: 21 January 2014
Published: 7 February 2014
An epidemic model which describes vector-borne plant diseases is proposed with the aim to investigate the effect of insect vectors on the spread of plant diseases. Firstly, the analytical formula for the basic reproduction number is obtained by using the next generation matrix method, and then the existence of disease-free equilibrium and endemic equilibrium is discussed. Secondly, by constructing a suitable Lyapunov function and employing the theory of additive compound matrices, the threshold for the dynamics is obtained. If , then the disease-free equilibrium is globally asymptotically stable, which means that the plant disease will disappear eventually; if , then the endemic equilibrium is globally asymptotically stable, which indicates that the plant disease will persist for all time. Finally some numerical investigations are provided to verify our theoretical results, and the biological implications of the main results are briefly discussed in the last section.
In the natural world, plants are very important, since they are the survival foundation for all kinds of creatures, including human being, animals, and even microbes. However, there are a lot of plant diseases which affect the health of plants, such as Cucumber mosaic virus, Broad bean wilt virus, Beet curly top virus, and Maize streak virus . A serious potato disease destroyed almost all the potatoes of the Irish and caused a great famine in 1845-1846. In fact, in human history, plant diseases were not recognized until very late. In the late 18th century, there were many scientists who began to research the essence of plant diseases. For example, Marthieu Tillet experimentally proved that Wheat bunt is caused by a kind of black powder; Adolf Mayer found that Tobacco mosaic disease can be spread by the juice of infected leafs; and many other plant diseases have been found and researched. It was confirmed that insect vectors (such as aphids, leafhoppers, plant hopper, mealworm etc.) have close relations with many kinds of plant diseases [2, 3].
Recently vector-borne plant diseases have attracted the interest of many mathematical modeling researchers. For instance, F van den Bosch and MJ Jeger have researched plant virus’ propagation characteristics and population dynamics in [4, 5], and they evaluated the influence of insects’ various transmission types and migration on the spread of virus plant diseases in ; MP Grill discussed the influence of the timings of insects mediators feeding on plant virus’ infection rate in ; MJ Jeger et al., presented some control strategies in , and they pointed out that biological control method has become an important part of the integrated pest management. In , NJ Cunniffe and CA Gilligan considered the effect of biological control on soil-borne plant pathogens. An antagonist is included in their model to control plant diseases. They obtained invasion criteria for all three species: host, pathogen and antagonist.
The research of plant diseases is also attractive to epidemiologists. They need to establish a simple plausible mechanism to protect susceptible hosts, allowing coexistence of pathogens and hosts, which is consistent with empirical studies of diseases in plant populations. The dynamics of these host-pathogen systems are routinely modeled by compartmental susceptible-infected-removed (SIR) epidemic models. Some criteria were derived for the invasion and persistence of both the pathogen and the host [10, 11]. In [12, 13], the authors considered plant disease models with impulsive effects.
Although early investigations of the epidemics caused by plant pathogens seldom included the demographics of the host population, replenishment of susceptible hosts is common in those models [4, 5, 14, 15]. Motivated by references [10, 11, 14, 15], in this paper, we will develop and analyze the dynamics of a vector-borne plant disease model. The organization of this paper is as follows: In Section 2, the model is constructed and the basic reproduction number is obtained by using the next generation matrix method. In Section 3, we consider the local stability of the equilibria by the Jacobian matrix method; and by the method of constructing a Lyapunov function and employing the theory of competitive systems, the global stability of the disease-free equilibrium and endemic equilibrium are investigated. In Section 4, we give some numerical simulations to prove our theoretical results, and a brief discussion is also provided in this section.
2 Model formulation and the basic reproduction number
To construct the model, we make the following assumptions.
(A1) For an insect vector population, the total population is divided into two categories, X and Y, which denote the densities of the susceptible vector and infective vector at time t, respectively. For the plant host population, the total population is divided into three categories S, I, and R, which denote the numbers of the susceptible, infective, and recovered host plant population at time t, respectively. The total number of plants is a positive constant. Here, the assumption that the number of plants in one area is fixed is reasonable. In fact, one can always keep the total number fixed by adding a new plant when a plant has died. Further, we assume that those new plants are susceptible, i.e., we chose the birth rate of susceptible plant host as .
(A2) The susceptible plants can be infected not only by the infected insect vectors but also by the infected plants.
(A3) A susceptible vector can be infected only by an infected plant host, and after it is infected, it will hold the virus for the rest of its life. Further, there is no vertical infection being considered.
(A4) The replenishment rate of insect vectors is a positive constant, and all of the new born vectors are susceptible.
(A5) A nonlinear incidence rate of the disease is included in the model.
Dimensionless variables and parameters (with illustrative parameter values) in system ( 1 )
number of the susceptible plant hosts
number of the infected plant hosts
number of the recovered plant hosts
sum of the total plant hosts
density of the susceptible insect vectors
density of the infected insect vectors
sum of the total insect vectors density
infection ratio between infected hosts and susceptible vectors
biting rate of an infected vector on the susceptible host plants
infection incidence between infected and susceptible hosts
determines the level at which the force of infection saturates
determines the level at which the force of infection saturates
determines the level at which the force of infection saturates
the conversion rate of infected hosts to recovered hosts
natural death rate of plant hosts
birth or immigration of insect vectors
natural death rate of insect vectors
disease-induced mortality of infected hosts
where . From Eq. (2), we easily get as .
is the positively invariant set for system (3).
3 The equilibria and their stability
3.1 The existence of equilibria
In this subsection, we investigate the existence of equilibria of system (3). It is easy to see that system (3) always has a disease-free equilibrium , and . Next, we consider the existence of endemic equilibrium.
3.2 Local stability of the equilibria
If , we know that both of the roots of Eq. (10) have a negative real part; if , there exists at least one root with positive real part. Therefore, we get the following result.
Theorem 3.1 The disease-free equilibrium is locally asymptotically stable if , and unstable if . In addition, when , the unique endemic equilibrium emerges in Ω.
To demonstrate the local stability of the positive equilibrium , we need the following lemma.
Let M be a real matrix. If , and are all negative, then all of the eigenvalues of M have negative real part.
Theorem 3.3 The endemic equilibrium of system (3) is locally asymptotically stable if .
Therefore, it follows from Lemma 3.2 that the proof is complete. □
3.3 Global stability of the equilibria
In this subsection, we will investigate the global stability of the equilibria of system (3).
Theorem 3.4 If , then the disease-free equilibrium is globally asymptotically stable in Ω.
and it is easy to see that as . Therefore, by the LaSalle invariance principle  we conclude that all trajectories starting in Ω approach for .
Together with the result of Theorem 3.1, we complete the proof of this theorem. □
Now we will investigate the global stability of the endemic equilibrium in the positively invariant set Ω. To do so, we will use the results for the three dimensional competitive systems that live in convex sets [20–22] and a powerful theory of additive compound matrix to prove asymptotic orbital stability of periodic solutions [16, 23]. The approach has been adopted by many authors (see [24, 25] and the references cited therein) to show the global stability of the endemic equilibrium in the three dimensional competitive systems. Before proving our main result, we give the following useful lemma.
Lemma 3.5 
where is a three-dimensional vector, D is an open subset on , and F is twice continuously differentiable in D. Assume D is convex and bounded, and system (15) is competitive and permanent and has the property of stability of periodic orbits. If is the only equilibrium point in intD and if it is locally asymptotically stable, then it is globally asymptotically stable in intD.
Theorem 3.6 If , then system (3) is uniformly persistent, i.e., there exists (independent of the initial conditions), such that , , .
The inequality (18) implies that as . However, is bounded on the set Ω. According to Theorem 1 in reference , we get the result of this theorem. □
Theorem 3.7 If , then system (3) has the property of stability of periodic orbits.
which implies that as . By Eq. (22), it shows that as , which implies that the linear system (20) is asymptotically stable.
This completes the proof. □
Theorem 3.8 If , then the unique endemic equilibrium is globally asymptotically stable for system (3).
Proof Combining the results of Theorems 3.3, 3.6, and 3.7 with Lemma 3.5, we can complete the proof. □
4 Numerical analysis and discussion
4.1 Numerical simulations
In this subsection, we will illustrate the influence of insect vector on the spread of plant disease by numerical simulations.
In this paper, we propose a differential system to model a vector-borne plant disease. Our main object is to investigate the effect of the insect vector on the dynamics of the plant disease. We get the basic reproduction number by the next generation matrix method in Section 2. In detail, the existence, local stability, and global stability of the disease-free equilibrium and endemic equilibrium are investigated in Section 3. By employing a suitable Lyapunov function, and the second additive compound matrix method, the main results as shown in Theorems 3.4 and 3.8 have been derived. Our main results indicate that if , then the disease-free equilibrium is globally asymptotically stable in Ω, and the unique endemic equilibrium is globally asymptotically stable provided that . It follows from these results that the basic reproduction number plays an important role in determining the persistence or dying out of the disease.
Note that the basic reproduction number, , is a strictly increasing function with respect to the parameters , , , whereas it decreases the function with respect to parameters m, ω. The parameters , , have no relations to the value of . That is, the total number of the host plant, birth rate of the vector, and incidence rate of the disease can positively affect the value of ; while the death rate of the host plant, the death rate of the vector, and the disease-induced death rate can negatively affect the value of ; but the saturation rate of the incidence has no relation to the value of . Those results are useful and could help us to design optimal control strategies for disease control. For example, if under natural conditions the value of is greater than 1, then it follows from Theorem 3.8 that the endemic equilibrium is globally stable. This means that the disease will be an endemic. However, we can take measures to reduce the values of incidence rate , , and (or) , such that the value of can be reduced until it is less than 1.
The first author is supported by Postdoctoral Science Foundation of China (no. 2011M501428) and Young Science Funds of Shanxi (no. 2013021002-2). The third author is supported by National Natural Science Foundation of China (11171199). The authors would like to thank the anonymous reviewers for their helpful comments, which improved the quality of this paper greatly.
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