Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2019, Article ID 1352698, 17 pages
https://doi.org/10.1155/2019/1352698
Research Article

Stability and Hopf Bifurcation of a Vector-Borne Disease Model with Saturated Infection Rate and Reinfection

School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

Correspondence should be addressed to Zhixing Hu; nc.ude.btsu@gnixihzuh

Received 28 January 2019; Revised 4 May 2019; Accepted 13 May 2019; Published 9 June 2019

Academic Editor: Luminita Moraru

Copyright © 2019 Zhixing Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper established a delayed vector-borne disease model with saturated infection rate and cure rate. First of all, according to the basic reproductive number , we determined the disease-free equilibrium and the endemic equilibrium . Through the analysis of the characteristic equation, we consider the stability of two equilibriums. Furthermore, the effect on the stability of the endemic equilibrium by delay was studied, the existence of Hopf bifurcations of this system in was analyzed, and the length of delay to preserve stability was estimated. The direction and stability of the Hopf bifurcation were also been determined. Finally, we performed some numerical simulation to illustrate our main results.

1. Introduction

Malaria is a vector-borne infectious disease [1], caused by parasites. It is popular in 102 countries and regions, especially in some countries in Africa, southeast Asia, and South America. In the 30s of this century, malaria spread throughout the country. Clinical symptoms and signs of this disease, such as typical periodic onset of malaria, secondary anemia, and spleen, can cause serious consequences, including dangerous malaria, malarial kidney disease, and black urine fever.

The main way of transmission of malaria is the bite of an infected female anopheline mosquito. The mosquitoes would also be infected when uninfected mosquitoes bite infected people, and this transmission process has an incubation period [2]. The important feature of malaria is that the recovered immune system may establish immune memory for such antigens. It is this characteristic that greatly reduces the spread of malaria [3, 4]. Immune process is slow and, however, takes years or even decades [5]. As time goes by, the immune system gradually weakens, and at this time, reinfection likely occurs; therefore, considering the function of delay and immune system is necessary in the study of malaria.

For the vector-borne diseases such as malaria, a large number of mathematical models have been created [2, 6, 7, 8, 9], most of which consider the local immunity and delay of the spread of malaria in the crowd. Different time delay has been used to describe the latent period in the course of disease transmission [7, 8, 9]. Local stability conditions for the equilibrium of a model with two time delays have been considered by Wan and Cui [8]. The global stability of the equilibrium has been studied for a vector-borne disease model with distributed delay by Cai et al. [10].

Based on the above model, this paper considers a delayed vector-borne model with saturated infection rate and partial immunity to reinfection. We prove that the stability of this system can be changed by time delay and produce Hopf bifurcation, calculating the length of delay to preserve stability. Using the center manifold theorem [11] and norm theory, we determine the stability and bifurcation direction.

2. Model Formulation

represented as the host population at time t is divided into three subclasses: the susceptible , the infected , and the recovered . represented as the vector population at time t is divided into two subclasses: the susceptible and the infected . The Hopf bifurcation was determined in a model with direct infection and delay by Wei et al. [9]. The mathematical formulation still needs improvements. We consider an improved model as follows:where and represent the recruitment rate of the host population and vector population, respectively. b represents the average number of bites per mosquito per day. The incidence rate is the number of infections of the susceptible host caused by the infected vector, and α is the inhibitory effect rate caused by the infected vector. and represent the death rates of the host population and vector population, respectively. is the infection rate from vector to human. represents the degree of partial protection for recovered people given by a primary infection, where represents complete protection and represents no protection. γ is the per capita recovery rate of the infected host population. represents the infection rate from human to vector. τ is the time delay, representing the incubation period in the vector population; that is to say, a susceptible vector that bites an infective host at time will become infective at time t.

The model (1) meets the initial conditions:where is the Banach space of continuous functions mapping the interval into with the topology of uniform convergence. The norm is defined as follows:

Based on the fundamental theory of functional differential equations [12], it is easy to show that the solution of the model (1) with the initial condition (2) is unique and is nonnegative for all .

By (1), we know thatand can solve it by using the integrating factor:

That is,

Form the limiting theory of differential equation [13], we can draw that model (1) is the equivalent of the following equation:

Next, the model (7) can be studied in the invariant set:

Now, let us consider the existence of equilibrium.

First, it is easy to show that system (7) always has a disease-free equilibrium . The endemic equilibrium satisfies the following equation:

Form (9), we have and , where satisfies the following equation:wherewhere and are the two roots of (10) since , and we have

Obviously, and if , and and if . From the relationship between roots and coefficients, we know that and are both negative if and is positive if . According to the above discussion, we can obtain the following theorems.

Theorem 2.1. System (7) has the disease-free equilibrium if . System (7) has the disease-free equilibrium and an endemic equilibrium if .

3. Stability of Equilibrium and Hopf Bifurcation

In this section, we study the stability of equilibrium and the existence of Hopf bifurcation of system (7).

The characteristic equation of the linear approximate equation of the system (7) at equilibrium iswhere

3.1. The Local and Global Stability of the Disease-Free Equilibrium

At the disease-free equilibrium , equation (13) can be expressed as follows:

Obviously, equation (14) has a negative real root . To discuss the rest of the characteristic roots of (14), we consider the following equation:

When , equation (15) is equivalent to

By the using Routh–Hurwitz criterion, (16) has two eigenvalues with negative real parts if .

When , then the roots of (15) can enter the right-half plane in the complex plane by crossing the imaginary axis as the delay τ increases.

Let be a purely imaginary root of equation (15), then separating the real and imaginary parts yields

Squaring and taking the sum of (17) yields

Equation (18) has no roots if . Therefore, we conclude that all eigenvalues of equation (14) have negative real parts.

If , letwhich implies that

By the continuity of and zero point theorem, has at least one positive root. So, the disease-free equilibrium is unstable. Based on the results, we can draw the conclusion.

Theorem 3.1. For any τ, the virus-free equilibrium of the system (7) is locally asymptotically stable if , and it is unstable if .

In fact, using a similar approach to the literature [14], we can know that is globally asymptotically stable if . A detailed proof is given below.

For a continuous and bounded function , we define

For system (7), any solution with the initial conditions is , and we have

By the fluctuation lemma [15], we know that there is a sequence ; when , we have and . Substituting into the first equation of (7) yields

Let us take the limits on both sides:

Similarly,

Combining (24) and (25), we know that

Since is the supremum of the function , If , by using (26), which contradicts . That is to say, , which implies that . In the same way by using (25), we have . According to the limit theorem [13], we have . Combined with the local asymptotic stability of , we can get the following theorem:

Theorem 3.2. For any τ, the virus-free equilibrium of system (7) is globally asymptotically stable if .

3.2. The Local Stability of the Endemic Equilibrium

From (13), the characteristic equation of linear approximate equation of the system (7) at the endemic equilibrium iswhere

When , equation (27) is equivalent towhere

Notice that

It follows that

By using the Routh–Hurwitz criterion, equation (29) only has eigenvalues with negative real parts if . We can obtain the following theorem:

Theorem 3.3. For , the endemic equilibrium of system (7) is locally asymptotically stable if .

3.3. Hopf Bifurcation

In this subsection, we devote to investigating the stability of the endemic equilibrium and the existence of Hopf bifurcation.

Let be the root of equation (27), substituting it into equation (27) and separating the real and imaginary parts; we can obtain the following equation:

Squaring and taking the sum of (34) yieldswhere

Let , then equation (35) is equivalent tothen . The two roots of equation are

According to [16], the condition that equation (37) has positive roots is as follows:

Lemma 3.4. For equation (37),(i)If , then equation (37) has at least one positive root(ii)If and , then equation (37) has no positive root(iii)If and , then equtaion (37) has positive roots if and only if and

Based on Lemma 3.4, we concluded that if (ii) is set up, then the stability of will not change when τ increases. If equation (37) has a positive root, then the stability of may change with the change in τ.

Suppose that equation (37) has three positive roots, written as , and . Then, equation (35) has positive roots . By using (34),

Definewhere . Obviously, is a pair of pure virtual root of equation (27).

Let

It follows that is the root of equation (35) satisfying and .

Next, we verify the transversal condition. Differentiating the two sides of equation (35) with respect to τ, we havethen

Thus,

If , the transversal condition is satisfied. Therefore, according to the above discussion and the Hopf bifurcation theorem of the differential equations [12], we can get the following result:

Theorem 3.5. (i)If and , then the endemic equilibrium is locally asymptotically stable for all .(ii)If or , and , then the endemic equilibrium is locally asymptotically stable for , and if , the system (7) undergoes a Hopf bifurcation at when , where

4. Estimation of the Length of Delay to Preserve Stability

In this section, we use a Nyquist criterion [17] to calculate the length of delay to preserve stability.

Consider the system (7) and the space of the real continuous functions that is defined in and satisfied the initial conditions (2) in the interval . Define

Linearization system (7) at the endemic equilibrium is expressed as follows:

By taking the Laplace transformation for (47), we can obtainwhere

Let , thenwhere .

Similarly,where .

Thus, (48) can be written aswhere

The inverse Laplace transformation of , , and will have terms which exponentially increase with time if , , and have poles with positive real parts. Thus, is locally asymptotically stable if and only if all the poles of , , and have negative real parts.

By the method of [17] and the Nyquist criterion, the local asymptotic stability of needs to satisfy the following two conditions:wherewhere is the smallest positive root of (54). Thus (54) and (55) can be written as

In order to estimate the length of delay to preserve stability, under the premise of ensuring stability, the following conditions need to be satisfied:

If (58) and (59) are satisfied simultaneously, they are sufficient conditions to guarantee stability. Our aim is to find an upper bound to independent of τ and then to estimate τ so that (59) holds true for all values of and in particular at .

Since and , from equation (58), we have

Letobviously meets (60) and .

From equation (59), we obtain

Since is locally asymptotically stable for , the inequality (62) will continue to hold for sufficiently small τ and .

On the basis of (58) and (62), we have

Note the left-hand side of (63) is and the right-hand side is ρ. By using inequality and , we can obtain

Note the right-hand side of (64) is . Clearly, when . Thus, if , we have . Let be the positive root of , that is,where

Summarizing the above discussions, we have the following theorem.

Theorem 4.1. If , then the Nyquist criterion holds true and estimates the maximum length of the delay preserving the stability, where satisfies (65).

5. Direction and Stability of the Hopf Bifurcation

We have obtained the conditions under which the Hopf bifurcation occurs at of the system (7). This section will use the normal form theory and the center manifold theory to give the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions of system (7). We suppose that system (7) undergoes Hopf bifurcation at for . Let be a pair of conjugate pure virtual roots at when .

Define

Thus, system (7) is equivalent to the following functional differential equation in .where . And and satisfywhere

Applying the Riesz representation theorem, there exists a matrix-valued function , such that . We choosewhere δ is the Dirac delta function, meeting and .

We define for ,

Thus, (68) becomeswhere .

In order to construct coordinates to describe the integral manifold near the origin, we need to define inner product and the adjoint operator of A as follows:where and . Form the discussion in Section 3, we know that are eigenvalues of . Thus they are also eigenvalues of . Define and to be the eigenvectors of and corresponding to the eigenvalues and , then

We can calculate that

According to (75), we know thatthus