Loading [MathJax]/jax/element/mml/optable/GeneralPunctuation.js
Research article Special Issues

Evaluating the effect of virus mutation on the transmission of avian influenza H7N9 virus in China based on dynamical model

  • Received: 22 February 2019 Accepted: 03 April 2019 Published: 19 April 2019
  • In 2017, the low pathogenic avian influenza H7N9 virus in China had mutated into high pathogenicity to domestic poultry, and led to a large number of poultry death and human cases. To evaluate the effect of virus mutation on the transmission of avian influenza H7N9 virus, this paper takes Guangdong province for the research area, takes domestic poultry, virus in the domestic poultry survival environment and human beings for the research objects, and establishes a non-autonomous dynamical model. By fitting model with the newly confirmed human cases in Guangdong province, the model we established is confirmed and applied to explain the dynamics of historical human cases. By carrying on parameter estimation, it is deduced that at least 5279376 human beings in Guangdong province had been infected with avian influenza H7N9 virus from March 2013 to September 2017, but most of them were not confirmed, since they had no obvious symptoms or had been cured as common influenza. And comparing with the low pathogenic avian influenza H7N9 virus (H7N9 LPAIV), the transmission rate of the highly pathogenic avian influenza H7N9 virus (H7N9 HPAIV) to human is almost unchanged, but to domestic poultry is about 3.87 times higher. Also, we calculate the basic reproduction number R0 = 1.3042, which indicates that the virus will persist in Guangdong province with time. Besides, we also perform some sensitivity analysis of the newly confirmed human cases and R0 in terms of model parameters and conclude that reducing the birth population of domestic poultry, speeding up the circulation of domestic poultry in the market and raising the rate of disease-related death of domestic poultry are benefit to control the transmission of the avian influenza H7N9 virus.

    Citation: Ning Bai, Juan Zhang , Li Li, Zhen Jin. Evaluating the effect of virus mutation on the transmission of avianinfluenza H7N9 virus in China based on dynamical model[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 3393-3410. doi: 10.3934/mbe.2019170

    Related Papers:

    [1] Ceyu Lei, Xiaoling Han, Weiming Wang . Bifurcation analysis and chaos control of a discrete-time prey-predator model with fear factor. Mathematical Biosciences and Engineering, 2022, 19(7): 6659-6679. doi: 10.3934/mbe.2022313
    [2] Yajie Sun, Ming Zhao, Yunfei Du . Multiple bifurcations of a discrete modified Leslie-Gower predator-prey model. Mathematical Biosciences and Engineering, 2023, 20(12): 20437-20467. doi: 10.3934/mbe.2023904
    [3] Xiaoling Han, Xiongxiong Du . Dynamics study of nonlinear discrete predator-prey system with Michaelis-Menten type harvesting. Mathematical Biosciences and Engineering, 2023, 20(9): 16939-16961. doi: 10.3934/mbe.2023755
    [4] Shuo Yao, Jingen Yang, Sanling Yuan . Bifurcation analysis in a modified Leslie-Gower predator-prey model with fear effect and multiple delays. Mathematical Biosciences and Engineering, 2024, 21(4): 5658-5685. doi: 10.3934/mbe.2024249
    [5] Mengyun Xing, Mengxin He, Zhong Li . Dynamics of a modified Leslie-Gower predator-prey model with double Allee effects. Mathematical Biosciences and Engineering, 2024, 21(1): 792-831. doi: 10.3934/mbe.2024034
    [6] Xiaoyuan Chang, Junjie Wei . Stability and Hopf bifurcation in a diffusivepredator-prey system incorporating a prey refuge. Mathematical Biosciences and Engineering, 2013, 10(4): 979-996. doi: 10.3934/mbe.2013.10.979
    [7] Saheb Pal, Nikhil Pal, Sudip Samanta, Joydev Chattopadhyay . Fear effect in prey and hunting cooperation among predators in a Leslie-Gower model. Mathematical Biosciences and Engineering, 2019, 16(5): 5146-5179. doi: 10.3934/mbe.2019258
    [8] Hongqiuxue Wu, Zhong Li, Mengxin He . Dynamic analysis of a Leslie-Gower predator-prey model with the fear effect and nonlinear harvesting. Mathematical Biosciences and Engineering, 2023, 20(10): 18592-18629. doi: 10.3934/mbe.2023825
    [9] Christian Cortés García . Bifurcations in a discontinuous Leslie-Gower model with harvesting and alternative food for predators and constant prey refuge at low density. Mathematical Biosciences and Engineering, 2022, 19(12): 14029-14055. doi: 10.3934/mbe.2022653
    [10] A. Q. Khan, I. Ahmad, H. S. Alayachi, M. S. M. Noorani, A. Khaliq . Discrete-time predator-prey model with flip bifurcation and chaos control. Mathematical Biosciences and Engineering, 2020, 17(5): 5944-5960. doi: 10.3934/mbe.2020317
  • In 2017, the low pathogenic avian influenza H7N9 virus in China had mutated into high pathogenicity to domestic poultry, and led to a large number of poultry death and human cases. To evaluate the effect of virus mutation on the transmission of avian influenza H7N9 virus, this paper takes Guangdong province for the research area, takes domestic poultry, virus in the domestic poultry survival environment and human beings for the research objects, and establishes a non-autonomous dynamical model. By fitting model with the newly confirmed human cases in Guangdong province, the model we established is confirmed and applied to explain the dynamics of historical human cases. By carrying on parameter estimation, it is deduced that at least 5279376 human beings in Guangdong province had been infected with avian influenza H7N9 virus from March 2013 to September 2017, but most of them were not confirmed, since they had no obvious symptoms or had been cured as common influenza. And comparing with the low pathogenic avian influenza H7N9 virus (H7N9 LPAIV), the transmission rate of the highly pathogenic avian influenza H7N9 virus (H7N9 HPAIV) to human is almost unchanged, but to domestic poultry is about 3.87 times higher. Also, we calculate the basic reproduction number R0 = 1.3042, which indicates that the virus will persist in Guangdong province with time. Besides, we also perform some sensitivity analysis of the newly confirmed human cases and R0 in terms of model parameters and conclude that reducing the birth population of domestic poultry, speeding up the circulation of domestic poultry in the market and raising the rate of disease-related death of domestic poultry are benefit to control the transmission of the avian influenza H7N9 virus.


    In biological systems, the continuous predator-prey model has been successfully investigated and many interesting results have been obtained (cf. [1,2,3,4,5,6,7,8,9] and the references therein). Moreover, based on the continuous predator-prey model, many human factors, such as time delay [10,11,12], impulsive effect [13,14,15,16,17,18,19,20], Markov Switching [21], are considered. The existing researches mainly focus on stability, periodic solution, persistence, extinction and boundedness [22,23,24,25,26,27,28].

    In 2011, the authors [28] considered the system incorporating a modified version of Leslie-Gower functional response as well as that of the Holling-type Ⅲ:

    {˙x(t)=x(a1bxc1y2x2+k1),˙y(t)=y(a2c2yx+k2). (1)

    With the diffusion of the species being also taken into account, the authors [28] studied a reaction-diffusion predator-prey model, and gave the stability of this model.

    In model (1) x represents a prey population, y represents a predator with population, a1 and a2 represent the growth rate of prey x and predator y respectively, constant b represents the strength of competition among individuals of prey x, c1 measures the maximum value of the per capita reduction rate of prey x due to predator y, k1 and k2 represent the extent to which environment provides protection to x and to y respectively, c2 admits a same meaning as c1. All the constants a1,a2,b,c1,c2,k1,k2 are positive parameters.

    However, provided with experimental and numerical researches, it has been obtained that bifurcation is a widespread phenomenon in biological systems, from simple enzyme reactions to complex ecosystems. In general, the bifurcation may put a population at a risk of extinction and thus hinder reproduction, so the bifurcation has always been regarded as a unfavorable phenomenon in biology [29]. This bifurcation phenomenon has attracted the attention of many mathematicians, so the research on bifurcation problem is more and more abundant [30,31,32,33,34,35,36,37,38,39,40].

    Although the continuous predator-prey model has been successfully applied in many ways, its disadvantages are also obvious. It requires that the species studied should have continuous and overlapping generations. In fact, we have noticed that many species do not have these characteristics, such as salmon, which have an annual spawning season and are born at the same time each year. For the population with non-overlapping generation characteristics, the discrete time model is more practical than the continuous model [38], and discrete models can generate richer and more complex dynamic properties than continuous time models [39]. In addition, since many continuous models cannot be solved by symbolic calculation, people usually use difference equations for approximation and then use numerical methods to solve the continuous model.

    In view of the above discussion, the study of discrete system is paid more and more attention by mathematicians. Many latest research works have focused on flip bifurcation for different models, such as, discrete predator-prey model [41,42]; discrete reduced Lorenz system [43]; coupled thermoacoustic systems [44]; mathematical cardiac system [45]; chemostat model [46], etc.

    For the above reasons, we will study from different perspectives in this paper, focusing on the discrete scheme of Eq (1).

    In order to get a discrete form of Eq (1), we first let

    u=ba1x,v=c1a1y,τ=a1t,

    and rewrite u,v,τ as x,y,t, then (1) changes into:

    {˙x(t)=x(1xβ1y2x2+h1),˙y(t)=αy(1β2yx+h2), (2)

    where β1=b2c1a1,h1=b2k1a21,α=a2c1,β2=c2bc1a2,h2=bk2a1.

    Next, we use Euler approximation method, i.e., let

    dxdtxn+1xnt,dydtyn+1ynt,

    where t denotes a time step, xn,yn and xn+1,yn+1 represent consecutive points. Provided with Euler approximation method with the time step t=1, (2) changes into a two-dimensional discrete dynamical system:

    {xn+1=xn+xn(1xnβ1y2nx2n+h1),yn+1=yn+αyn(1β2ynxn+h2). (3)

    For the sake of analysis, we rewrite (3) in the following map form:

    (xy)(x+x(1xβ1y2x2+h1)y+αy(1β2yx+h2)). (4)

    In this paper, we will consider the effect of the coefficients of map (4) on the dynamic behavior of the map (4). Our goal is to show how a flipped bifurcation of map (4) can appear under some certain conditions.

    The remainder of the present paper is organized as follows. In section 2, we discuss the fixed points of map (4) including existence and stability. In section 3, we investigate the flip bifurcation at equilibria E2 and E. It has been proved that map (4) can undergo the flip bifurcation provided with that some values of parameters be given certain. In section 4, we give an example to support the theoretical results of the present paper. As the conclusion, we make a brief discussion in section 5.

    Obviously, E1(1,0) and E2(0,h2β2) are fixed points of map (4). Given the biological significance of the system, we focus on the existence of an interior fixed point E(x,y), where x>0,y>0 and satisfy

    1x=β1(y)2(x)2+h1,x+h2=β2y,

    i.e., x is the positive root of the following cubic equation:

    β22x3+(β1β22)x2+(β22h1+2β1h2)x+β1h22β22h1=0. (5)

    Based on the relationship between the roots and the coefficients of Eq (5), we have

    Lemma 2.1 Assume that β1h22β22h1<0, then Eq (5) has least one positive root, and in particular

    (ⅰ) a unique positive root, if β1β22;

    (ⅱ) three positive roots, if β1<β22.

    The proof of Lemma 2.1 is easy, and so it is omitted.

    In order to study the stability of equilibria, we first give the Jacobian matrix J(E) of map (4) at any a fixed point E(x,y), which can be written as

    J(E)=(22xβ1y2(h1x2)(x2+h1)22β1xyx2+h1αβ2y2(x+h2)21+α2αβ2yx+h2).

    For equilibria E1, we have

    J(E1)=(0001+α).

    The eigenvalues of J(E1) are λ1=0,λ2=1+α with λ2>1 due to the constant α>0, so E1(1,0) is a saddle.

    For equilibria E2, note that

    J(E2)=(2β1h22β22h10αβ21α),

    then the eigenvalues of J(E2) are λ1=2β1h22β22h1,λ2=1α, and so we get

    Lemma 2.2 The fixed point E2(0,h2β2) is

    (ⅰ) a sink if 1<β1h22β22h1<3 and 0<α<2;

    (ⅱ) a source if β1h22β22h1<1 or β1h22β22h1>3 and α>2;

    (ⅲ) a a saddle if 1<β1h22β22h1<3 and α>2, or, β1h22β22h1<1 or β1h22β22h1>3 and 0<α<2;

    (ⅳ) non-hyperbolic if β1h22β22h1=1 or β1h22β22h1=3 or α=2.

    In this section, we will use the relevant results of literature [38,39,40] to study the flip bifurcation at equilibria E2 and E.

    Based on (ⅲ) in Lemma 2.2, it is known that if α=2, the eigenvalues of J(E2) are: λ1=2β1h22β22h1,λ2=1. Define

    Fl={(β1,β2,h1,h2,α):α=2,β1,β2,h1,h2>0}.

    We conclude that a flip bifurcation at E2(0,h2β2) of map (4) can appear if the parameters vary in a small neighborhood of the set Fl.

    To study the flip bifurcation, we take constant α as the bifurcation parameter, and transform E2(0,h2β2) into the origin. Let e=2β1h22β22h1,α1=α2, and

    u(n)=x(n),v(n)=y(n)h2β2,

    then map (4) can be turned into

    (uv)(euu22β1h2β2h1uv+O((|u|+|v|+|α1|)3)2β2uv2β2h2u22β2h2v2+4h2uv+α1β2uα1vα1β2h2u2α1β2h2v2+2α1h2uv+O((|u|+|v|+|α1|)3)). (6)

    Let

    T1=(1+e02β21),

    then by the following invertible transformation:

    (uv)=T1(sw),

    map (6) turns into

    (sw)(es(1+e)s22β1h2β2h1s(2sβ2+w)+O(|s|+|w|+|α1|)3w+F2(s,w,α1)), (7)

    where

    F2=2β2[(1+e)s2+2β1h2β2h1s(2sβ2+w)]2β2h2(1+e)2s22β2h2(2sβ2+w)2+4(1+e)h2s(2sβ2+w)
    +(1+e)α1β2sα1(2sβ2+w)(1+e)2α1β2h2s2α1β2h2(2sβ2+w)2
    +2(1+e)α1h2s(2sβ2+w)+O(|s|+|w|+|α1|)3.

    Provided with the center manifold theorem (Theorem 7 in [40]), it can be obtained that there will exist a center manifold Wc(0,0) for map (7), and the center manifold Wc(0,0) can be approximated as:

    Wc(0,0)={(w,s,α1)R3:s=aw2+bwα1+c(α1)2+O(|w|+|α1|)3}.

    As the center manifold satisfies:

    s=a(w+F2)2+b(w+F2)α1+c(α1)2=e(aw2+bwα1+c(α1)2)(1+e)(aw2+bwα1+c(α1)2)22β1h2β2h1(aw2+bwα1+c(α1)2)(2β2(aw2+bwα1+c(α1)2)+w)+O(|s|+|w|+|α1|)3,

    it can be obtained by comparing the coefficients of the above equality that a=0,b=0,c=0, so the center manifold of map (7) at E2(0,h2β2) is s=0. Then map (7) restricted to the center manifold turns into

    w(n+1)=w(n)α1w(n)2β2h2w2(n)α1β2h2w2(n)+O(|w(n)|+|α1|)3
    f(w,α1).

    Obviously,

    fw(0,0)=1,fww(0,0)=4β2h2,

    so

    (fww(0,0))22+fwww(0,0)30,fwα1(0,0)=10.

    Therefore, Theorem 4.3 in [38] guarantees that map (3) undergoes a flip bifurcation at E2(0,h2β2).

    Note that

    J(E)=(22xβ1(y)2(h1(x)2)((x)2+h1)22β1xy(x)2+h1αβ21α),

    then the characteristic equation of Jacobian matrix J(E) of map (3) at E(x,y) is:

    λ2(1+α0α)λ+(1α)α0ηα=0, (8)

    where

    α0=22xβ1(y)2(h1(x)2)((x)2+h1)2,η=2β1xyβ2((x)2+h1).

    Firstly, we discuss the stability of the fixed point E(x,y). The stability results can be described as the the following Lemma, which can be easily proved by the relations between roots and coefficients of the characteristic Eq (8), so the proof has been omitted.

    Lemma 3.1 The fixed point E(x,y) is

    (ⅰ) a sink if one of the following conditions holds.

    (ⅰ.1) 0<α0+η<1, and α01α0+η<α<2(1+α0)1+α0+η;

    (ⅰ.2) 1<α0+η<0, and α<min{α01α0+η,2(1+α0)1+α0+η};

    (ⅰ.3) α0+η<1, and α01α0+η>α>2(1+α0)1+α0+η;

    (ⅱ) a source if one of the following conditions holds.

    (ⅱ.1) 0<α0+η<1, and α<min{α01α0+η,2(1+α0)1+α0+η};

    (ⅱ.2) 1<α0+η<0, and α01α0+η<α<2(1+α0)1+α0+η;

    (ⅱ.3) α0+η<1, and α>max{α01α0+η,2(1+α0)1+α0+η};

    (ⅲ) a saddle if one of the following conditions holds.

    (ⅲ.1) 1<α0+η<1, and α>2(1+α0)1+α0+η;

    (ⅲ.2) α0+η<1, and α<2(1+α0)1+α0+η;

    (ⅳ) non-hyperbolic if one of the following conditions holds.

    (ⅳ.1) α0+η=1;

    (ⅳ.2) α0+η1; and α=2(1+α0)1+α0+η;

    (ⅳ.3) α0+η0,α=α01α0+η and (1+α0α)2<4((1α)α0ηα).

    Then based on (ⅳ.2) of Lemma 3.1 and α1+α0,3+α0, we get that one of the eigenvalues at E(x,y) is 1 and the other satisfies |λ|1. For α,β1,β2,h1,h2>0, let us define a set:

    Fl={(β1,β2,h1,h2,α):α=2(1+α0)1+α0+η,α0+η1,α1+α0,3+α0}.

    We assert that a flip bifurcation at E(x,y) of map (3) can appear if the parameters vary in a small neighborhood of the set Fl.

    To discuss flip bifurcation at E(x,y) of map (3), we choose constant α as the bifurcation parameter and adopt the central manifold and bifurcation theory [38,39,40].

    Let parameters (α1,β1,β2,h1,h2)Fl, and consider map (3) with (α1,β1,β2,h1,h2), then map (3) can be described as

    {xn+1=xn+xn(1xnβ1y2nx2n+h1),yn+1=yn+α1yn(1β2ynxn+h2). (9)

    Obviously, map (9) has only a unique positive fixed point E(x,y), and the eigenvalues are λ1=1,λ2=2+α0α, where |λ2|1.

    Note that (α1,β1,β2,h1,h2)Fl, then α1=2(1+α0)1+α0+η. Let |α| small enough, and consider the following perturbation of map (9) described by

    {xn+1=xn+xn(1xnβ1y2nx2n+h1),yn+1=yn+(α1+α)yn(1β2ynxn+h2), (10)

    with α be a perturbation parameter.

    To transform E(x,y) into the origin, we let u=xx,v=yy, then map (10) changes into

    (uv)(a1u+a2v+a3u2+a4uv+a5v2+a6u3+a7u2v+a8uv2+a9v3+O((|u|+|v|)4)b1u+b2v+b3u2+b4uv+b5v2+c1uα+c2vα+c3u2α+c4uvα+c5v2α+b6u3+b7u2v+b8uv2+b9v3+O((|u|+|v|+|α|)4)), (11)

    where

    a1=22xβ1(y)2f(0)β1x(y)2f(0); a2=2β1xyf(0);

    a3=1β1(y)2f(0)12β1x(y)2f a_{4} = -2\beta_{1}y^{\ast}f(0)-2\beta_{1}x^{\ast}y^{\ast}f'(0);

    a_{5} = -\beta_{1}x^{\ast}f(0); a_{6} = -\frac{1}{2}\beta_{1}(y^{\ast})^{2}f''(0)-\frac{1}{6}\beta_{1}x^{\ast}(y^{\ast})^{2}f'''(0);

    a_{7} = -\beta_{1}x^{\ast}y^{\ast}f''(0)-2\beta_{1}y^{\ast}f'(0); a_{8} = -\beta_{1}f(0)-\beta_{1}x^{\ast}f'(0), \; \; \; a_{9} = 0;

    f(0) = \frac{1}{(x^{\ast})^{2}+h_{1}}, f'(0) = \frac{-2x^{\ast}}{[(x^{\ast})^{2}+h_{1}]^{2}}, f''(0) = \frac{6(x^{\ast})^{2}-2h_{1}}{[(x^{\ast})^{2}+h_{1}]^{3}}, f'''(0) = \frac{ 24x^{\ast} (h_{1}-(x^{\ast})^{2})}{[(x^{\ast})^{2}+h_{1}]^{4}}.

    b_{1} = \frac{\alpha_{1}\beta_{2}(y^{\ast})^{2} }{(x^{\ast}+h_{2})^{2} }; b_{2} = 1+\alpha_{1}-\frac{\alpha_{1}\beta_{2}y^{\ast} }{x^{\ast}+h_{2}}; b_{3} = -\frac{\alpha_{1}\beta_{2}(y^{\ast})^{2} }{(x^{\ast}+h_{2})^{3} }; b_{4} = \frac{2\alpha_{1}\beta_{2}y^{\ast} }{ (x^{\ast}+h_{2})^{2} };

    b_{5} = -\frac{\alpha_{1}\beta_{2} }{x^{\ast}+h_{2}}; c_{1} = \frac{\beta_{2}(y^{\ast})^{2} }{(x^{\ast}+h_{2})^{2} }; c_{2} = 1-\frac{\beta_{2}y^{\ast} }{x^{\ast}+h_{2}}; c_{3} = -\frac{\beta_{2}(y^{\ast})^{2} }{(x^{\ast}+h_{2})^{3} };

    c_{4} = \frac{2\beta_{2}y^{\ast} }{(x^{\ast}+h_{2})^{2} }; c_{5} = -\frac{\beta_{2} }{x^{\ast}+h_{2} }; b_{6} = \frac{\alpha_{1}\beta_{2}(y^{\ast})^{2} }{(x^{\ast}+h_{2})^{4} }; b_{7} = -\frac{2\alpha_{1}\beta_{2}y^{\ast} }{ (x^{\ast}+h_{2})^{3} };

    b_{8} = \frac{\alpha_{1}\beta_{2} }{(x^{\ast}+h_{2})^{2} }; \; \; \; \; b_{9} = 0.\; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \;

    Now let's construct an matrix

    \begin{gather*} T_{2} = \begin{pmatrix}a_{2} & a_{2} \\-1-a_{1} & \lambda_{2}-a_{1} \end{pmatrix}. \end{gather*}

    It's obvious that the matrix T_{2} is invertible due to \lambda_{2}\neq -1, and then we use the following invertible translation

    \left( \begin{array}{cc} {u}\\ {v} \end{array} \right) = T_{2} \left( \begin{array}{cc} {s } \\ {w } \end{array} \right),

    map (11) can be described by

    \begin{align} \left( \begin{array}{cc} {s}\\ {w} \end{array} \right) \mapsto \left( \begin{array}{cc} {-s +f_{1}(s, w, \alpha^{\ast}) } \\ {\lambda_{2}w +f_{2}(s, w, \alpha^{\ast}) } \end{array} \right), \end{align} (12)

    where

    f_{1}(s, w, \alpha^{\ast}) = \frac{(\lambda_{2}-a_{1})a_{3}-a_{2} b_{3}}{ a_{2}(\lambda_{2}+1) }u^{2} + \frac{(\lambda_{2}-a_{1})a_{4}-a_{2} b_{4}}{ a_{2}(\lambda_{2}+1) }uv +\frac{(\lambda_{2}-a_{1})a_{5}-a_{2} b_{5}}{ a_{2}(\lambda_{2}+1) }v^{2} +\frac{(\lambda_{2}-a_{1})a_{6}-a_{2} b_{6}}{ a_{2}(\lambda_{2}+1) }u^{3} \\ \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; +\frac{(\lambda_{2}-a_{1})a_{7}-a_{2} b_{7}}{ a_{2}(\lambda_{2}+1) }u^{2}v +\frac{(\lambda_{2}-1)a_{8}-a_{2} b_{8}}{ a_{2}(\lambda_{2}+1) }uv^{2} +\frac{(\lambda_{2}-a_{1})a_{9}-a_{2} b_{9}}{ a_{2}(\lambda_{2}+1) }v^{3}- \frac{a_{2}c_{1}}{a_{2}(\lambda_{2}+1)}u\alpha^{\ast} \\ \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; - \frac{a_{2}c_{2}}{a_{2}(\lambda_{2}+1)}v\alpha^{\ast}- \frac{a_{2}c_{3}}{a_{2}(\lambda_{2}+1)}u^{2}\alpha^{\ast} - \frac{a_{2}c_{4}}{a_{2}(\lambda_{2}+1)}uv\alpha^{\ast} - \frac{a_{2}c_{5}}{a_{2}(\lambda_{2}+1)}v^{2}\alpha^{\ast} \\ \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; +O((|s|+ |w|+|\alpha^{\ast}| )^{4}), \\ f_{2}(s, w, \alpha^{\ast}) = \frac{(a_{1}+1)a_{3}+a_{2} b_{3}}{ a_{2}(\lambda_{2}+1) }u^{2} + \frac{(a_{1}+1)a_{4}+a_{2} b_{4}}{ a_{2}(\lambda_{2}+1) }uv +\frac{(a_{1}+1)a_{5}+a_{2} b_{5}}{ a_{2}(\lambda_{2}+1) }v^{2} +\frac{(a_{1}+1)a_{6}+a_{2} b_{6}}{ a_{2}(\lambda_{2}+1) }u^{3} \\ \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; +\frac{(a_{1}+1)a_{7}+a_{2} b_{7}}{ a_{2}(\lambda_{2}+1) }u^{2}v +\frac{(a_{1}+1)a_{8}+a_{2} b_{8}}{ a_{2}(\lambda_{2}+1) }uv^{2}+\frac{(a_{1}+1)a_{9}+a_{2} b_{9}}{ a_{2}(\lambda_{2}+1) }v^{3}+ \frac{a_{2}c_{1}}{a_{2}(\lambda_{2}+1)}u\alpha^{\ast}\\ \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; + \frac{a_{2}c_{2}}{a_{2}(\lambda_{2}+1)}v\alpha^{\ast}+ \frac{a_{2}c_{3}}{a_{2}(\lambda_{2}+1)}u^{2}\alpha^{\ast}+ \frac{a_{2}c_{4}}{a_{2}(\lambda_{2}+1)}uv\alpha^{\ast} + \frac{a_{2}c_{5}}{a_{2}(\lambda_{2}+1)}v^{2}\alpha^{\ast}\\ \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; +O((|s|+ |w|+|\alpha^{\ast}| )^{4}),

    with

    u = a_{2}(s+w), v = (\lambda_{2}-a_{1})w-(a_{1}+1)s;

    u^{2} = (a_{2}(s+w))^{2};

    uv = (a_{2}(s+w))((\lambda_{2}-a_{1})w-(a_{1}+1)s);

    v^{2} = ((\lambda_{2}-a_{1})w-(a_{1}+1)s)^{2};

    u^{3} = (a_{2}(s+w))^{3};

    u^{2}v = (a_{2}(s+w))^{2}((\lambda_{2}-a_{1})w-(a_{1}+1)s);

    uv^{2} = (a_{2}(s+w))((\lambda_{2}-a_{1})w-(a_{1}+1)s)^{2};

    v^{3} = ((\lambda_{2}-a_{1})w-(a_{1}+1)s)^{3}.

    In the following, we will study the center manifold of map (12) at fixed point (0, 0) in a small neighborhood of \alpha^{\ast} = 0. The well-known center manifold theorem guarantee that a center manifold W^{c}(0, 0) can exist, and it can be approximated as follows

    W^{c}(0, 0) = \{(s, w, \alpha^{\ast})\in R^{3}:w = d_{1}s^{2}+d_{2}s\alpha^{\ast}+d_{3}(\alpha^{\ast})^{2}+ O(( |s|+ |\alpha^{\ast}|)^{3}) \},

    which satisfies

    w = d_{1}(-s+f_{1}(s, w, \alpha^{\ast}))^{2}+d_{2}(-s+f_{1}(s, w, \alpha^{\ast}))\alpha^{\ast}+d_{3}(\alpha^{\ast})^{2}
    = \lambda_{2}( d_{1}s^{2}+d_{2}s\alpha^{\ast}+d_{3}(\alpha^{\ast})^{2} )+f_{2}(s, w, \alpha^{\ast}).\; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \;

    By comparing the coefficients of the above equation, we have

    d_{1} = \frac{a_{2}((a_{1}+1)a_{3}+a_{2}b_{3})}{1-\lambda_{2}^{2}}-\frac{(a_{1}+1)((a_{1}+1)a_{4}+a_{2}b_{4})}{1-\lambda_{2}^{2}}+ \frac{(a_{1}+1)^{2}((a_{1}+1)a_{5}+a_{2}b_{5})}{1-\lambda_{2}^{2}}, \\ d_{2} = \frac{c_{2}(a_{1}+1) -a_{2}c_{1} }{(1+\lambda_{2})^{2}}, \; \; \; \; \; \; d_{3} = 0.\; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \;

    So, restricted to the center manifold W^{c}(0, 0), map (12) turns into

    \begin{align} s \mapsto -s+e_{1}s^{2}+e_{2}s\alpha^{\ast}+e_{3}s^{2}\alpha^{\ast}+e_{4}s(\alpha^{\ast})^{2}+e_{5}s^{3}+O(( |s|+ |\alpha^{\ast}|)^{4}) \\ \triangleq F_{2}(s, \alpha^{\ast}), \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \; \end{align} (13)

    where

    e_{1} = A_{1}a_{2}^{2}-A_{2}a_{2}(a_{1}+1)+ A_{3}(a_{1}+1)^{2};

    e_{2} = -A_{8}a_{2}+ A_{9}(a_{1}+1);

    e_{3} = 2A_{1}d_{2}a_{2}^{2}+ A_{2}a_{2}d_{2}(\lambda_{2}-2a_{1}-1)-2A_{3}d_{2}(\lambda_{2}-a_{1})(a_{1}+1)-A_{8}a_{2}d_{1}

    -A_{9}(\lambda_{2}-a_{1})d_{1}-A_{10}a_{2}^{2}+A_{11}a_{2}(a_{1}+1)-A_{12}(a_{1}+1)^{2};

    e_{4} = -A_{8}a_{2}d_{2}-A_{9}(\lambda_{2}-a_{1})d_{2};

    e_{5} = 2A_{1}a_{2}^{2}d_{1}+A_{2}a_{2}d_{1}(\lambda_{2}-2a_{1}-1)-2A_{3}d_{1}(\lambda_{2}-a_{1})(a_{1}+1)+A_{4}a_{2}^{3}

    -A_{5}a_{2}^{2}(a_{1}+1)+A_{6}a_{2}(a_{1}+1)^{2}-A_{7}(a_{1}+1)^{3};

    with

    A_{1} = \frac{(\lambda_{2}-a_{1})a_{3}-a_{2} b_{3}}{ a_{2}(\lambda_{2}+1) }; \; \; A_{2} = \frac{(\lambda_{2}-a_{1})a_{4}-a_{2} b_{4}}{ a_{2}(\lambda_{2}+1) }; \; \; A_{3} = \frac{(\lambda_{2}-a_{1})a_{5}-a_{2} b_{5}}{ a_{2}(\lambda_{2}+1) }; \; \; A_{4} = \frac{(\lambda_{2}-a_{1})a_{6}-a_{2} b_{6}}{ a_{2}(\lambda_{2}+1) };

    A_{5} = \frac{(\lambda_{2}-a_{1})a_{7}-a_{2} b_{7}}{ a_{2}(\lambda_{2}+1) }; \; \; A_{6} = \frac{(\lambda_{2}-1)a_{8}-a_{2} b_{8}}{ a_{2}(\lambda_{2}+1) }; \; \; A_{7} = \frac{(\lambda_{2}-a_{1})a_{9}-a_{2} b_{9}}{ a_{2}(\lambda_{2}+1) }; \; \; A_{8} = \frac{a_{2}c_{1}}{a_{2}(\lambda_{2}+1)};

    A_{9} = \frac{a_{2}c_{2}}{a_{2}(\lambda_{2}+1)}; \; \; \; \; \; \; \; \; A_{10} = \frac{a_{2}c_{3}}{a_{2}(\lambda_{2}+1)}; \; \; \; \; \; \; \; \; A_{11} = \frac{a_{2}c_{4}}{a_{2}(\lambda_{2}+1)}; \; \; \; \; \; \; \; \; A_{12} = \frac{a_{2}c_{5}}{a_{2}(\lambda_{2}+1)}.

    To study the flip bifurcation of map (13), we define the following two discriminatory quantities

    \mu_{1} = \left( \frac{\partial^{2}F_{2}}{\partial s\partial\alpha^{\ast}}+\frac{1}{2}\frac{\partial F_{2}}{\partial\alpha^{\ast}} \frac{\partial^{2}F_{2}}{\partial s^{2}} \right )|_{(0, 0)},

    and

    \mu_{2} = \left(\frac{1}{6} \frac{\partial^{3}F_{2}}{\partial s^{3}}+\left(\frac{1}{2} \frac{\partial^{2}F_{2}}{\partial s^{2}}\right)^{2} \right )|_{(0, 0)}

    which can be showed in [38]. Then provided with Theorem 3.1 in [38], the following result can be given as

    Theorem 3.1. Assume that \mu_{1} and \mu_{2} are not zero, then a flip bifurcation can occur at E^{\ast}(x^{\ast}, y^{\ast}) of map (3) if the parameter \alpha^{\ast} varies in a small neighborhood of origin. And that when \mu_{2} > 0 (<0), the period-2 orbit bifurcated from E^{\ast}(x^{\ast}, y^{\ast}) of map (3) is stable (unstable).

    As application, we now give an example to support the theoretical results of this paper by using MATLAB. Let \beta_{1} = 1, \beta_{2} = 0.5, h_{1} = 0.05, h_{2} = 0.1, then we get from (5) that map (3) has only one positive point E^{\ast}(0.0113, 0.2226). And we further have \mu_{1} = e_{2} = 0.1134 \neq 0, \mu_{2} = e_{5}+e_{1}^{2} = -4.4869 \neq 0, which implies that all conditions of Theorem 3.1 hold, a flip bifurcation comes from E^{\ast} at the bifurcation parameter \alpha = 2.2238 , so the flip bifurcation is supercritical, i.e., the period-2 orbit is unstable.

    According to Figures 1 and 2, the positive point E^{\ast}(0.0113, 0.2226) is stable for 2\leq \alpha \leq 2.4 and loses its stability at the bifurcation parameter value \alpha = 2.2238. Which implies that map (3) has complex dynamical properties.

    Figure 1.  Flip bifurcation diagram of map (3) in the ( \alpha , x) plane for \beta_{1} = 1, \beta_{2} = 0.5, h_{1} = 0.05, h_{2} = 0.1. The initial value is (0.0213, 0.2326).
    Figure 2.  Flip bifurcation diagram of map (3) in the ( \alpha , y) plane for \beta_{1} = 1, \beta_{2} = 0.5, h_{1} = 0.05, h_{2} = 0.1. The initial value is (0.0213, 0.2326).

    In this paper, a predator-prey model with modified Leslie-Gower and Holling-type Ⅲ schemes is considered from another aspect. The complex behavior of the corresponding discrete time dynamic system is investigated. we have obtained that the fixed point E_{1} of map (4) is a saddle, and the fixed points E_{2} and E^{\ast} of map (4) can undergo flip bifurcation. Moreover, Theorem 3.1 tell us that the period-2 orbit bifurcated from E^{\ast}(x^{\ast}, y^{\ast}) of map (3) is stable under some sufficient conditions, which means that the predator and prey can coexist on the stable period-2 orbit. So, compared with previous studies [28] on the continuous predator-prey model, our discrete model shows more irregular and complex dynamic characteristics. The present research can be regarded as the continuation and development of the former studies in [28].

    This work is supported by the National Natural Science Foundation of China (60672085), Natural Foundation of Shandong Province (ZR2016EEB07) and the Reform of Undergraduate Education in Shandong Province Research Projects (2015M139).

    The authors would like to thank the referee for his/her valuable suggestions and comments which led to improvement of the manuscript.

    The authors declare that they have no competing interests.

    YYL carried out the proofs of main results in the manuscript. FXZ and XLZ participated in the design of the study and drafted the manuscripts. All the authors read and approved the final manuscripts.



    [1] R. Gao, B. Cao, Y. Hu, et al, Human infection with a novel avian-origin influenza A (H7N9) virus, N. Engl. J. Med., 368 (2013), 1888–1897.
    [2] Q. Li, L. Zhou, M. Zhou, et al, Epidemiology of Human Infections with Avian Influenza A (H7N9) Virus in China, N. Engl. J. Med., 370 (2014), 520–532.
    [3] L. Fang, X. Li, K. Liu, et al, Mapping Spread and Risk of Avian Influenza A (H7N9) in China, Sci. Rep., 3 (2013), 2722.
    [4] Avian Influenza Report, Report of the government of the Hong Kong Special Administrative Region, Available from: https://www.chp.gov.hk/sc/resources/29/332.html.
    [5] X. Wang, H. Jiang, P. Wu, et al, Epidemiology of avian influenza A H7N9 virus in human beings across five epidemics in mainland China, 2013-17: an epidemiological study of laboratory- confirmed case series, Lancet Infect. Dis., 17 (2017), 822–832.
    [6] A. Badulak, Human Infections with the Emerging Avian Influenza A H7N9 Virus from Wet Market Poultry: Clinical Analysis and Characterization of Viral Genome, J. Emerg. Med., 45 (2013), 484.
    [7] Z. Chen, H. Liu, J. Lu, et al, Asymptomatic, Mild, and Severe Influenza A(H7N9) Virus Infection in Humans, Guangzhou, China, Emerg. Infect. Dis., 20 (2014), 1535–1540.
    [8] Human infection with avian influenza A(H7N9) virus - China, Report of World Health Organiza- tion, Available from: http://www.who.int/csr/don/27-february-2017-ah7n9-china/ en/.
    [9] W. Zhu, J. Zhou, Z. Li, et al, Biological characterisation of the emerged highly pathogenic avian influenza (HPAI) A(H7N9) viruses in humans, in mainland China, 2016 to 2017, Euro. Surveill., 22 (2017).
    [10] L. Yang , W. Zhu, X. Li, et al, Genesis and Spread of Newly Emerged Highly Pathogenic H7N9 Avian Viruses in Mainland China, J. Virol., 91(2017), JVI.01277-17.
    [11] C. Quan, W. Shi, Y. Yang, et al, New threats of H7N9 influenza virus: the spread and evolution of highly and low pathogenic variants with high genomic diversity in Wave Five, J. Virol., 92 (2018), JVI.00301-18.
    [12] Official Veterinary Bulletin, Report of Ministry of Agriculture of the People's Republic of China, Available from: http://jiuban.moa.gov.cn/zwllm/tzgg/gb/sygb/.
    [13] R. Mu and Y. Yang, Global Dynamics of an Avian Influenza A(H7N9) Epidemic Model with Latent Period and Nonlinear Recovery Rate, Comput. Math. Methods Med., 2018 (2018), 1–11.
    [14] Y. Chen and Y. Wen, Global dynamic analysis of a H7N9 avian-human influenza model in an outbreak region, J. Theor. Biol., 367 (2015), 180–188.
    [15] J. Zhang, Z. Jin, G. Sun, et al, Determination of original infection source of H7N9 avian influenza by dynamical model, Sci. Rep., 4 (2014), 4846.
    [16] Z. Liu and C. Fang, A modeling study of human infections with avian influenza A H7N9 virus in mainland China, Int. J. Infect. Dis., 41 (2015), 73–78.
    [17] Y. Xing, L. Song, G. Sun, et al, Assessing reappearance factors of H7N9 avian influenza in China, Appl. Math. Comput., 309 (2017), 192–204.
    [18] M. Kang, L. Ehy, W. Guan, et al, Epidemiology of human infections with highly pathogenic avian influenza A(H7N9) virus in Guangdong, 2016 to 2017, Euro. Surveill., 22 (2017).
    [19] W. Su, K. Cheng, D. Chu, et al, Genetic analysis of H7N9 highly pathogenic avian influenza virus in Guangdong, China, 2016C2017, J. Infect., 76 (2018).
    [20] L. Cao, K.B. Li, Y. Liu, et al, Surveillance for circulation and genetic evolution of avian influenza A (H7N9) virus in poultry markets in Guangzhou, 2014 - 2017, Dis. Survell., 33 (2018), 897–901.
    [21] C. Li, R.Ren, D. Li, et al, Epidemic and death case analysis on human infection with highly pathogenic avian influenza A (H7N9) virus in the mainland of China, 2016 - 2018, Dis. Survell., 33 (2018), 985–989.
    [22] D. Wu, S. Zou, T. Bai, et al, Poultry farms as a source of avian influenza A (H7N9) virus reassortment and human infection, Sci. Rep., 5 (2015), 7630.
    [23] K.Tan, S. Jacob, K. Chan, et al, An overview of the characteristics of the novel avian influenza A H7N9 virus in humans, Front Microbiol., 6 (2015), 140.
    [24] Y. Bi, J. Liu, H. Xiong, et al, A new reassortment of influenza A (H7N9) virus causing human infection in Beijing, 2014, Sci. Rep., 6 (2016), 26624.
    [25] A. Handel, J. Brown, D. Stallknecht, et al, A Multi-scale Analysis of Influenza A Virus Fit- ness Trade-offs due to Temperature-dependent Virus Persistence, PLoS Comput. Biol., 9 (2013), e1002989.
    [26] Guangdong statistical yearbook, Report of Guangdong statistical information network, Available from: http://www.gdstats.gov.cn/tjsj/gdtjnj/.
    [27] Nation data, Report of Nation Bureau of Statistics of China, Available from: http://data. stats.gov.cn/easyquery.htm?cn=E0103.
    [28] J. Ma and Z. Ma, Epidemic threshold conditions for seasonally forced SEIR models, Math. Biosci. Eng., 3 (2017), 161–172.
    [29] X. Hu, Threshold dynamics for a Tuberculosis model with seasonality, Math. Biosci. Eng., 9 (2011), 111–122.
    [30] W. Wang and X. Zhao, Threshold Dynamics for Compartmental Epidemic Models in Periodic Environments, J. Dyn. Differ. Equ., 20 (2008), 699–717.
    [31] P. van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equi- libria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48.
  • This article has been cited by:

    1. 桂珍 王, A Class of Mathematical Model Concerning Impulsive Pest Control Strategies, 2021, 10, 2324-7991, 548, 10.12677/AAM.2021.102060
    2. Liyan Zhong, Jianhe Shen, Degenerate Transcritical Bifurcation Point can be an Attractor: A Case Study in a Slow–Fast Modified Leslie–Gower Model, 2022, 21, 1575-5460, 10.1007/s12346-022-00608-8
    3. Naqi Abbas, Rizwan Ahmed, Stability and bifurcation analysis of a discrete Leslie predator-prey model with fear effect, 2024, 12, 2309-0022, 16, 10.21015/vtm.v12i1.1686
    4. Fethi Souna, Salih Djilali, Sultan Alyobi, Anwar Zeb, Nadia Gul, Suliman Alsaeed, Kottakkaran Sooppy Nisar, Spatiotemporal dynamics of a diffusive predator-prey system incorporating social behavior, 2023, 8, 2473-6988, 15723, 10.3934/math.2023803
    5. Parvaiz Ahmad Naik, Muhammad Amer, Rizwan Ahmed, Sania Qureshi, Zhengxin Huang, Stability and bifurcation analysis of a discrete predator-prey system of Ricker type with refuge effect, 2024, 21, 1551-0018, 4554, 10.3934/mbe.2024201
    6. Neriman Kartal, Multiple Bifurcations and Chaos Control in a Coupled Network of Discrete Fractional Order Predator–Prey System, 2024, 2731-8095, 10.1007/s40995-024-01665-1
    7. Parvaiz Ahmad Naik, Rizwan Ahmed, Aniqa Faizan, Theoretical and Numerical Bifurcation Analysis of a Discrete Predator–Prey System of Ricker Type with Weak Allee Effect, 2024, 23, 1575-5460, 10.1007/s12346-024-01124-7
    8. Saud Fahad Aldosary, Rizwan Ahmed, Stability and bifurcation analysis of a discrete Leslie predator-prey system via piecewise constant argument method, 2024, 9, 2473-6988, 4684, 10.3934/math.2024226
    9. Parvaiz Ahmad Naik, Yashra Javaid, Rizwan Ahmed, Zohreh Eskandari, Abdul Hamid Ganie, Stability and bifurcation analysis of a population dynamic model with Allee effect via piecewise constant argument method, 2024, 70, 1598-5865, 4189, 10.1007/s12190-024-02119-y
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5684) PDF downloads(663) Cited by(2)

Figures and Tables

Figures(11)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog