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Review Special Issues

Identification of biomarkers associated with Parkinson’s disease by gene expression profiling studies and bioinformatics analysis

  • Received: 10 October 2019 Accepted: 24 December 2019 Published: 26 December 2019
  • Parkinson’s disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein–containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

    Citation: Marios G. Krokidis. Identification of biomarkers associated with Parkinson’s disease by gene expression profiling studies and bioinformatics analysis[J]. AIMS Neuroscience, 2019, 6(4): 333-345. doi: 10.3934/Neuroscience.2019.4.333

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  • Parkinson’s disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein–containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.


    1. Introduction

    In many infectious diseases, such as HIV, schistosomiasis, tuberculosis, the infectiousness of an infected individual can be very different at various stages of infection. Hence, the age of infection may be an important factor to consider in modeling transmission dynamics of infectious diseases. In the epidemic model of Kermack and Mckendrick [9], infectivity is allowed to depend on the age of infection. Because the age-structured epidemic model is described by first order PDEs, it is more difficult to theoretically analyze the dynamical behavior of the PDE models, particularly the global stability. Several recent studies [10,11,18] have focused on age structured models, and the results show that age of infection may play an important role in the transmission dynamics of infectious diseases.

    In our pervious work [4], we formulated an infection-age structured epidemic model to describe the transmission dynamics of vector-borne diseases. The model includes both incubation age of the exposed hosts and infection age of the infectious hosts, both of which describe the different removal rates in the latent period and the variable infectiousness in the infectious period, respectively. The results in [4] show that the basic reproduction number determines transmission dynamics of vector-borne diseases: the disease-free equilibrium is globally asymptotically stable if the basic reproduction number is less than 1, and the endemic equilibrium is globally asymptotically stable if the basic reproduction number is greater than 1. However, the vector-borne epidemic model formulated in [4] only incorporates a single strain. In reality, many diseases are caused by more than one antigenically different strains of the causative agent [15]. For instance, the dengue virus has 4 different serotypes [6], and bacterial pneumonia is caused by more than ninety different serotypes of Streptoccus pneumoniae. Therefore, it is necessary to study infection-age structured epidemic models with multiple strains.

    In this paper, we will extend the model with a single strain to the model with multiple strains, and obtain the following infection-age-structured vector-borne epidemic model with multiple strains:

    $ \left\{dSvdt=Λvnj=1Sv0βjv(a)Ijh(a,t)daμvSv,dIjvdt=Sv0βjv(a)Ijh(a,t)da(μv+αjv)Ijv,dRvdt=nj=1αjvIjvμvRv,dShdt=Λhnj=1βjhShIjvμhSh,Ejh(τ,t)τ+Ejh(τ,t)t=(μh+mjh(τ))Ejh(τ,t),Ejh(0,t)=βjhShIjv,Ijh(a,t)a+Ijh(a,t)t=(μh+αjh(a)+rjh(a))Ijh(a,t),Ijh(0,t)=0mjh(τ)Ejh(τ,t)dτ,dRhdt=nj=10rjh(a)Ijh(a,t)daμhRh.\right. $ (1)

    In the model (1), $S_h(t)$, $E^j_h(\tau, t)$, $I^j_h(a, t)$, $R_h(t)$ represent the number/density of the susceptible hosts, infected hosts with strain $j$ but not infectious, infected hosts with strain $j$ and infectious, and recovered hosts at time $t$, respectively. $S_v(t)$, $I^j_v(t)$ and $R_v(t)$ denote the number of the susceptible vectors, infected vectors with strain $j$ and infectious, and recovered vectors at time $t$, respectively. $\Lambda_v,\Lambda_h$ are the birth /recruitment rates of the vectors and hosts, respectively; $\mu_v, \mu_h$ are the natural death rates of the vectors and hosts, respectively. The parameter $m^j_h(\tau)$ denotes the removal rate of the infected hosts with strain $j$ of incubation age $\tau$ from the latent period; $\alpha^j_h(a)$ is the additional disease induced death rate due to the strain $j$ at age of infection $a$; $\alpha^j_v$ denotes the recovery rate of the infected vectors with strain $j$; $ r^j_h(a)$ denotes the recovery rate of the infected hosts of infection age $a$ with strain $j$; $\beta^j_v(a)$ is the transmission coefficient of the infected host individuals with strain $j$ at age of infection $a$, and $\beta^j_h$ is the transmission coefficient of strain $j$ from infected vectors to healthy host individuals.

    The dynamics of the epidemic model involving multiple strains has fascinated researchers for a long time (see [3,5,6,7,17] and the references therein), and one of the important results is the competitive exclusion principle. In epidemiology, the competitive exclusion principle states that if multiple strains circulate in the population, only the strain with the largest reproduction number persists and the strains with suboptimal reproduction numbers are eliminated [13]. Using a multiple-strain ODE model Bremermann and Thieme [2] first proved that the principle of competitive exclusion is valid under the assumption that infection with one strain precludes additional infections with other strains. In 2013, Maracheva and Li [13] extended the competitive exclusion principle to a multi-stain age-since-infection structured model of SIR/SI-type. The goal of this paper is to extend this principle to model (1).

    As we all know, the proof of competitive exclusion principle is based on the global stability of the single-strain equilibrium. The stability analysis of nonlinear dynamical systems has always been an important topic theoretically and practically since global stability is one of the most important issues related to their dynamic behaviors. Due to the lack of generically applicable tools proving the global stability is very challenging, especially for the continuous age-structured models which are described by first order PDEs. Although there are various approaches for some general nonlinear systems, the method of Lyapunov functions is the most common tool used to prove the global stability. In this paper, we will apply a class of Lyaponuv functions to study the global dynamics of system (1) and draw on the results to derive the competitive exclusion principle for infinite dimensional systems.

    This paper is organized as follows. In the next section we derive an explicit formula for the basic reproduction number $\mathcal R^j_0$ of strain $j$ for $j=1,\cdots,n$, and then we will show that strain $j$ will die out if its basic reproduction number is less than one. In section 3, we will define the disease reproduction number $\mathcal R_0$, and then prove that the disease-free equilibrium (DFE) of the system is globally asymptotically stable if $\mathcal R_0< 1$. In Section 4, we will investigate the existence of single-strain equilibria and their local stabilities. In section 5 we will devote to prove the principle of competitive exclusion. Without loss of generality, we assume that strain one has the maximal reproduction number and $\mathcal R^1_0>1$. Under the assumption, we will show that strain one is uniformly strong persistent while the remaining strains become extinct. In Section 6, we use a class of Lyapunov functions to derive the global stability of the strain one equilibrium under the condition that $\mathcal R^i_0/\mathcal R^1_0<b_i/b_1<1,i\neq 1$, where $b_j$ denotes the probability of a given susceptible vector being transmitted by an infected host with strain $j$, which implies that complete competitive exclusion holds for the system. Finally, a brief discussion is given in Section 7.


    2. The reproduction numbers and threshold dynamics

    In this section, we mainly derive the reproduction numbers for each strain, and show that the stain will die out if its basic reproduction number is less than one.

    Since the equations for the recovered individuals and the recovered vectors are decoupled from the system, it follows that the dynamical behavior of system (1) is equivalent to the dynamical behavior of the following system:

    $ \left\{dSvdt=Λvnj=1Sv0βjv(a)Ijh(a,t)daμvSv,dIjvdt=Sv0βjv(a)Ijh(a,t)da(μv+αjv)Ijv,dShdt=Λhnj=1βjhShIjvμhSh,Ejh(τ,t)τ+Ejh(τ,t)t=(μh+mjh(τ))Ejh(τ,t),Ejh(0,t)=βjhShIjv,Ijh(a,t)a+Ijh(a,t)t=(μh+αjh(a)+rjh(a))Ijh(a,t),Ijh(0,t)=0mjh(τ)Ejh(τ,t)dτ.\right. $ (2)

    Model (2) is equipped with the following initial conditions:

    $S_v(0)=S_{v_0}, I_v^j(0)=I_{v_0}^j, S_h(0)=S_{h_0} , E_h^j(\tau, 0)=\varphi_j(\tau), I_h^j(a, 0)=\psi_j(a).$

    All parameters are nonnegative, $\Lambda_v > 0, ~\Lambda_h > 0$, and $\mu_v > 0, ~\mu_h > 0$. We make the following assumptions on the parameter-functions.

    Assumption 2.1.

    1. The function $\beta_v^j(a)$ is bounded and uniformly continuous for every $j$. When $\beta_v^j(a)$ is of compact support, the support has non-zero Lebesgue measure;

    2. The functions $m_h^j(\tau),\ \alpha_h^j(a),\ r_h^j(a)$ belong to $L^\infty(0, \infty)$;

    3. The functions $ \varphi_j(\tau),\ \psi_j(a)$ are integrable.

    Let us define

    $X=R×nj=1R×R×nj=1(L1(0,)×L1(0,)).$

    It is easily verified that solutions of (2) with nonnegative initial conditions belong to the positive cone for $t \geq 0$. Adding the first and all equations for $I_v^j$ yields that

    $ddt(Sv(t)+nj=1Ijv(t))Λvμv(Sv(t)+nj=1Ijv(t)).$

    Hence,

    $lim supt+(Sv(t)+nj=1Ijv(t))Λvμv.$

    Similarly, adding the equation for $S_h$ and all equations for $E_h^j,I_h^j$, we have

    $ddt(Sh(t)+nj=10Ejh(τ,t)dτ+nj=10Ijh(a,t)da)$
    $Λhμh(Sh(t)+nj=10Ejh(τ,t)dτ+nj=10Ijh(a,t)da),$

    and it then follows that

    $lim supt+(Sh(t)+nj=10Ejh(τ,t)dτ+nj=10Ijh(a,t)da)Λhμh.$

    Therefore, the following set is positively invariant for system (2)

    $Ω={(Sv, I1v, , Inv, Sh, E1h, I1h, , Enh, Inh)X+|(Sv(t)+nj=1Ijv(t))Λvμv,(Sh(t)+nj=10Ejh(τ,t)dτ+nj=10Ijh(a,t)da)Λhμh}.$ (3)

    In what follows, we only consider the solutions of the system (2) with initial conditions which lie in the region $\Omega$. As we all know, the reproduction number is one of most important concepts in epidemiological model. Next, we will express the basic reproduction numbers for each strain. To simplify expression, let us introduce two notations.

    Definition 2.1. The exit rate of exposed host individuals with strain $j$ from the incubation compartment is given by $\mu_h+m_h^j(\tau)$, the probability of still being latent after $\tau$ time units, denoted by $\pi^j_1(\tau)$, is given by

    $πj1(τ)=eμhτeτ0mjh(σ))dσ. $ (4)

    Definition 2.2. The exit rate of infected individuals with strain $j$ from the infective compartment is given by $\mu_h+\alpha_h^j(a)+r_h^j(a)$, and it then follows that the probability of still being infectious after $a$ time units, denoted by $\pi^j_2(a)$, is given by

    $ \label{eq:4}πj2(a)=eμhaea0(αjh(σ)+rjh(σ))dσ. $ (5)

    Then we can give the expression for the basic reproduction number of strain $j$ which can be expressed as

    $Rj0=βjhΛvΛhμvμh(μv+αjv)0mjh(τ)πj1(τ)dτ0βjv(a)πj2(a)da. $ (6)

    The reproduction number of strain $j$ gives the number of secondary infections produced in an entirely susceptible population by a typical infected individual with strain $j$ during its entire infectious period. $\mathcal{R}^j_0$ gives the strength of strain $j$ to invade into the system when rare and alone. The reproduction number of strain $j$ consists of two terms:

    $Rjh=Λvμv0βjv(a)πj2(a)da,Rjv=βjhΛhμh(μv+αjv)0mjh(τ)πj1(τ)dτ.$

    The first term $\mathcal R^j_h$ represents the reproduction number of human-to-vector transmission of strain $j$, and the second term $\mathcal R^j_v$ is the reproduction number of vector-to-human transmission of strain $j$.

    Now we are able to state the results on threshold dynamics of strain $j$:

    Theorem 2.3. If $ \mathcal R^j_0<1$, strain $j$ will die out.

    Proof. Let

    $BjE(t)=Ejh(0,t),BjI(t)=Ijh(0,t).$

    Integrating along the characteristic lines of system (2) yields

    $ \begin{array}{ll}
    \displaystyle E_h^j(\tau, t)= \left\{\begin{array} {ll}
    \displaystyle B_E^j(t-\tau)\pi^j_1(\tau),
    \mbox{ }  & t>\tau,\\[1ex]
    \displaystyle
    \varphi_j(\tau-t)\frac{\pi^j_1(\tau)}{\pi^j_1(\tau-t)}, \mbox{
    }  & t<\tau,
    \end{array}
    \right.\\[3ex] \displaystyle I_h^j (a, t)= \left\{\begin{array} {ll} \displaystyle B_I^j(t-a)\pi^j_2(a), \mbox{ } & t>a,\\[1ex] \displaystyle \psi_j(a-t)\frac{\pi^j_2(a)}{\pi^j_2(a-t)}, \mbox{ } & t<a. \end{array}\right. \end{array} $
    (7)

    From the first and the third equations of system (2), we obtain

    $ \displaystyle \limsup\limits_{t\rightarrow +\infty} S_v(t)\leq\frac{\Lambda_v}{\mu_v}, \limsup\limits_{t\rightarrow +\infty} S_h(t)\leq\frac{\Lambda_h}{\mu_h}. $ (8)

    Thus, from system (2) and inequalities (8), we have

    $ \left\{ dIjv(t)dtΛvμv0βjv(a)Ijh(a,t)da(μv+αjv)Ijv,Ejh(τ,t)=Ejh(0,tτ)πj1(τ),t>τ,Ijh(a,t)=Ijh(0,ta)πj2(a),t>a.\right. $ (9)

    From the first inequality of (9), we obtain that

    $ Ijv(t)Ijv(0)e(μv+αjv)t+Λvμvt0e(μv+αjv)(ts)0βjv(a)Ijh(a,s)dadsIjv(0)e(μv+αjv)t+Λvμvt0e(μv+αjv)(ts)(s0βjv(a)Ijh(0,sa)πj2(a)da+tsβjv(a)ψj(as)πj2(a)πj2(as)da+tβjv(a)Ijh(a,s)da)ds. $ (10)

    Notice that

    $ lim supt+t0e(μv+αjv)(ts)s0βjv(a)Ijh(0,sa)πj2(a)dads(lim supt+t0e(μv+αjv)(ts)ds)0βjv(a)πj2(a)da(lim supt+Ijh(0,t))=1μv+αjv0βjv(a)πj2(a)da(lim supt+Ijh(0,t)), $ (11)
    $ lim supt+t0e(μv+αjv)(ts)tsβjv(a)ψj(as)πj2(a)πj2(as)dadsˉβlim supt+t0e(μv+αjv)(ts)tsψj(as)eaas(μh+αjh(σ)+rjh(σ))dσdadsˉβlim supt+t0e(μv+αjv)(ts)tsψj(as)eμhsdads $
    $ =ˉβlim supt+(e(μv+αjv)tt0e(μv+αjvμh)sts0ψj(a)dads)=ˉβ0ψj(a)dalim supt+(e(μv+αjv)te(μv+αjvμh)t1μv+αjvμh)=0, $ (12)

    and

    $ lim supt+t0e(μv+αjv)(ts)tβjv(a)Ijh(a,s)dads=0. $ (13)

    It then follows from (11), (12) and (13) that

    $ lim supt+Ijv(t)Λvμv(μv+αjv)0βjv(a)πj2(a)da(lim supt+Ijh(0,t))Λvμv(μv+αjv)0βjv(a)πj2(a)da(lim supt+0mjh(τ)Ejh(τ,t)dτ)Λvμv(μv+αjv)0βjv(a)πj2(a)dalim supt+(t0mjh(τ)Ejh(τ,t)dτ+tmjh(τ)Ejh(τ,t)dτ)=Λvμv(μv+αjv)0βjv(a)πj2(a)da(lim supt+t0mjh(τ)Ejh(0,tτ)πj1(τ)dτ)Λvμv(μv+αjv)0βjv(a)πj2(a)da0mjh(τ)πj1(τ)dτ(lim supt+Ejh(0,t))βjhΛvΛhμvμh(μv+αjv)0βjv(a)πj2(a)da0mjh(τ)πj1(τ)dτlim supt+Ijv(t)Rj0lim supt+Ijv(t). $ (14)

    Since $\mathcal R^j_0<1$ and $I_v^j(t),~j=1,\cdots,n$, are all bounded, the above expression implies that

    $ \displaystyle \limsup\limits_{t\rightarrow +\infty} I_v^j(t)=0, j=1,\cdots,n. $ (15)

    Hence, we have

    $ \displaystyle \limsup\limits_{t\rightarrow +\infty}E_h^j(0,t)=0, \limsup\limits_{t\rightarrow +\infty}E_h^j(\tau,t)=\limsup\limits_{t\rightarrow +\infty} E_h^j(0,t-\tau)\pi^j_1(\tau)=0. $ (16)

    By using the same argument, we have

    $ \displaystyle \limsup\limits_{t\rightarrow +\infty}I_h^j(0,t)=0, \limsup\limits_{t\rightarrow +\infty}I_h^j(a,t)=0. $ (17)

    Therefore, $(I_v^j(t),E_h^j(\tau,t),I_h^j(a,t))\rightarrow 0$ as $t\rightarrow \infty$. This means that strain $j$ will die out. The proof of Theorem 2.3 is completed.


    3. Global stability of the disease-free equilibrium

    In this section, we mainly define the disease reproduction number and show that the disease free equilibrium is globally asymptotically stable if the disease reproduction number $\mathcal R_0$ is less than one, where

    $R0=max{R10,,Rn0}.$

    System (2) always has a unique disease-free equilibrium $\mathcal{E}_0$, which is given by

    ${{\cal E}_0} = \left( {S_{{v_0}}^*,{\bf{0}},S_{{h_0}}^*,{\bf{0}},{\bf{0}}} \right),$

    where

    $Sv0=Λvμv,Sh0=Λhμh,$

    and $\bf{0}=(0,\cdots,0)$ is an $n$-dimensional zero vector.

    Now let us establish the local stability of the disease-free equilibrium. Let

    $Sv(t)=Sv0+xv(t),Ijv(t)=yjv(t),Sh(t)=Sh0+xh(t),Ejh(τ,t)=zjh(τ,t),Ijh(a,t)=yjh(a,t).$

    Then the linearized system of system (2) at the disease-free equilibrium $\mathcal{E}_0$ can be expressed as

    $\left\{dxv(t)dt=nj=1Sv00βjv(a)yjh(a,t)daμvxv(t),dyjv(t)dt=Sv00βjv(a)yjh(a,t)da(μv+αjv)yjv(t),dxh(t)dt=nj=1βjhSh0yjv(t)μhxh(t),zjh(τ,t)τ+zjh(τ,t)t=(μh+mjh(τ))zjh(τ,t),zjh(0,t)=βjhSh0yjv(t),yjh(a,t)a+yjh(a,t)t=(μh+αjh(a)+rjh(a))yjh(a,t),yjh(0,t)=0mjh(τ)zjh(τ,t)dτ.\right. $ (18)

    Let

    $ \label{eq:char} y_v^j{(t)}=\bar{y}_v^je^{\lambda t},\ z_h^j{(\tau, t)}=\bar{z}_h^j(\tau)e^{\lambda t}, \ y_h^j{(a, t)}=\bar{y}_h^j(a)e^{\lambda t}, $ (19)

    where $\bar{y}_v^j, \bar{z}_h^j(\tau)$ and $\bar{y}_h^j(a)$ are to be determined. Substituting (19) into (18), we obtain

    $ \left\{ λˉyjv=Sv00βjv(a)ˉyjh(a)da(μv+αjv)ˉyjv,dˉzjh(τ)dτ=(λ+μh+mjh(τ))ˉzjh(τ),ˉzjh(0)=βjhSh0ˉyjv,dˉyjh(a)da=(λ+μh+αjh(a)+rjh(a))ˉyjh(a),ˉyjh(0)=0mjh(τ)ˉzjh(τ)dτ.\right. $ (20)

    Solving the differential equation, we obtain

    $ˉzjh(τ)=ˉzjh(0) eλτπj1(τ)=βjhSh0ˉyjv eλτπj1(τ).$

    Substituting the expression for $\bar{z}_h^j(\tau)$ into the equation for $\bar{y}_h^j(0)$, expressing $\bar{y}_h^j(0)$ in term of $\bar{z}_h^j(0)$, and replacing $\bar{y}_h^j(0)$ in the equation for $\bar{y}_h^j(a)$, we obtain

    $ˉyjh(a)=ˉyjh(0) eλaπj2(a)=βjhSh0ˉyjv eλaπj2(a)0mjh(τ) eλτπj1(τ)dτ.$

    Substituting the above expression for $\bar{y}_h^j(a)$ into the first equation of (20), we can obtain

    $ λ+μv+αjv=βjhSv0Sh00mjh(τ)eλτπj1(τ)dτ0βjv(a)eλaπj2(a)da. $ (21)

    Now we are able to state the following result.

    Theorem 3.1. If

    $ \mathcal R_0=\max \{\mathcal R^1_0,\cdots,\mathcal R^n_0\}< 1, $

    then the disease-free equilibrium is locally asymptotically stable. If $\mathcal R_0>1$, it is unstable.

    Proof. We first prove the first result. Let us assume $\mathcal R_0 <1$. For ease of notation, set

    $ LHSdef=λ+μv+αjv,RHSdef=G1(λ)=βjhSv0Sh00mjh(τ)eλτπj1(τ)dτ0βjv(a)eλaπj2(a)da. $ (22)

    We can easily verify that

    $|LHS|μv+αjv,|RHS|G1(λ)G1(0)=βjhSv0Sh00mjh(τ)πj1(τ)dτ0βjv(a)πj2(a)da=βjhΛvΛhμvμh0mjh(τ)πj1(τ)dτ0βjv(a)πj2(a)da=Rj0(μv+αjv)<|LHS|,$

    for any $\lambda, \Re \lambda\geq 0$. There is a contradiction. The contradiction implies that the equation (21) cannot have any roots with non-negative real parts. Hence, the disease-free equilibrium is locally asymptotically stable.

    Next, let us assume $ \max \{\mathcal R^1_0,\cdots,\mathcal R^n_0\}=\mathcal R^{j_0}_0>1. $ We rewrite the characteristic equation (21) in the form

    $ \displaystyle \mathcal{G}_2(\lambda)=0, $ (23)

    where

    $G2(λ)=(λ+μv+αj0v)βj0hSv0Sh00mj0h(τ)eλτπj01(τ)dτ0βj0v(a)eλaπj02(a)da.$

    It is easily verified that

    $ G2(0)=(μv+αj0v)βj0hSv0Sh00mj0h(τ)πj01(τ)dτ0βj0v(a)πj02(a)da=(μv+αj0v)(1Rj00)<0, $

    and

    $\mathop {\lim }\limits_{\lambda \to + \infty } {{\cal G}_2}(\lambda ) = + \infty .$

    Hence, the characteristic equation (23) has a real positive root. Therefore, the disease free equilibrium $\mathcal{E}_0$ is unstable. This concludes the proof.

    We have proved that the disease-free equilibrium is locally stable if $\mathcal R_0<1$. It also follows from Theorem 2.3 that strain $j$ will die out if $\mathcal R^j_0<1$. Therefore we have the following result.

    Theorem 3.2. If

    $\mathcal R_0=\max \{\mathcal R^1_0,\cdots,\mathcal R^n_0\}< 1,$

    then the disease-free equilibrium $\mathcal{E}_0$ is globally asymptotically stable.


    4. Existence and stability of boundary equilibria

    In this section, we mainly investigate the existence and stability of the boundary equilibria. For ease of notation, let

    $ Δj=βjhΛhΛvμhμv(μv+αjv),bj=0mjh(τ)πj1(τ)dτ0βjv(a)πj2(a)da,bj(λ)=0mjh(τ)eλτπj1(τ)dτ0βjv(a)eλaπj2(a)da. $ (24)

    From Theorem 2.3, it follows that strain $j$ will die out if $\mathcal R_0^j<1$. Thus in later sections we always assume that $\mathcal R_0^j>1$ for all $j,j=1,2,\cdots,n$. If $\mathcal R_0^j>1$, straightforward computation yields that system (2) has a corresponding single-strain equilibrium $\mathcal{E}_j$ which is given by

    $ \mathcal{E}_j=(S_v^{j*},0,\cdots,0, I_v^{j*}, 0,\cdots,0,S_h^{j*}, 0,\cdots,0,E_h^{j*}(\tau), I_h^{j*}(a),0,\cdots,0). $

    The non-zero components $I_v^{j*},E_h^{j*}$ and $I_h^{j*}$ are in positions $j+1,n+2j+1$ and $n+2j+2$, respectively, and

    $ Ijv=μvμh(Rj01)βjh(Λhbj+μv),Sjv=Λv(μv+αjv)Ijvμv=βjhΛv(μv+Λhbj)μvμh(μv+αjv)(Rj01)βjhμv(μv+Λhbj), $
    $ Sjh=ΛhβjhIjv+μh=Λh(μv+Λhbj)μh(μvRj0+Λhbj),Ejh(τ)=Ejh(0)πj1(τ),Ejh(0)=βjhSjhIjv,Ijh(a)=Ijh(0)πj2(a),Ijh(0)=Ejh(0)0mjh(τ)πj1(τ)dτ. $ (25)

    The results on the local stability of single-strain equilibrium $\mathcal{E}_{j_0}$ are summarized below:

    Theorem 4.1. Assume $\mathcal{R}^{j_0}_0 > 1$ for a fixed $j_0$ and

    $ \mathcal{R}^j_0 < \mathcal{R}^{j_0}_0 \;for\;all \; j\neq j_0. $

    Then single-strain equilibrium $\mathcal{E}_{j_0}$ is locally asymptotically stable. If there exists $i_0$ such that

    $ \mathcal{R}^{i_0}_0 > \mathcal{R}^{j_0}_0, $

    then the single-strain equilibrium $\mathcal{E}_{j_0}$ is unstable.

    Proof. Without loss of generality, we assume that $\mathcal{R}^1_0 > 1$ and $ \mathcal{R}^i_0 < \mathcal{R}^1_0$ for $i= 2, \cdots, n$. Let

    $ Sv(t)=S1v+xv(t), Sh(t)=S1h+xh(t),I1v(t)=I1v+y1v(t), E1h(τ,t)=E1h(τ)+z1h(τ,t), I1h(a,t)=I1h(a)+y1h(a,t),Iiv(t)=yiv(t),Eih(τ,t)=zih(τ,t), Iih(a,t)=yih(a,t), $

    where $ i=2,\cdots,n.$ Then the linearization system of system (2) at the equilibrium $\mathcal{E}_{1}$ can be expressed as

    $\left\{ dxv(t)dt=S1v0β1v(a)y1h(a,t)daxv(t)0β1v(a)I1h(a)dani=2S1v0βiv(a)yih(a,t)daμvxv(t),dy1v(t)dt=S1v0β1v(a)y1h(a,t)da+xv(t)0β1v(a)I1h(a)da(μv+α1v)y1v(t),dyiv(t)dt=S1v0βiv(a)yih(a,t)da(μv+αiv)yiv(t),dxh(t)dt=β1hS1hy1v(t)β1hxh(t)I1vni=2βihS1hyiv(t)μhxh(t),zjh(τ,t)τ+zjh(τ,t)t=(μh+mjh(τ))zjh(τ,t),z1h(0,t)=β1hS1hy1v(t)+β1hxh(t)I1v,zih(0,t)=βihS1hyiv(t),yjh(a,t)a+yjh(a,t)t=(μh+αjh(a)+rjh(a))yjh(a,t),yjh(0,t)=0mjh(τ)zjh(τ,t)dτ.\right. $ (26)

    An approach similar to [14] (see Appendix B in [14]) can show that the linear stability of the system is determined by the eigenvalues of the linearized system (26). In order to investigate the linear stability of the linearized system (26), we consider exponential solutions (see the case of the disease-free equilibrium) and obtain a linear eigenvalue problem. For the whole system, we only consider the equations for strains $i, i=2, \cdots, n$, and obtain the following eigenvalue problem:

    $\left\{ dyiv(t)dt=S1v0βiv(a)yih(a,t)da(μv+αiv)yiv(t),zih(τ,t)τ+zih(τ,t)t=(μh+mih(τ))zih(τ,t),zih(0,t)=βihS1hyiv(t),yih(a,t)a+yih(a,t)t=(μh+αih(a)+rih(a))yih(a,t),yih(0,t)=0mih(τ)zih(τ,t)dτ.\right. $ (27)

    For each $i, i\neq 1$, by using the same argument to equation (21), we obtain the following characteristic equation

    $ λ+μv+αiv=βihS1vS1h0mih(τ)eλτπi1(τ)dτ0βiv(a)eλaπi2(a)da. $ (28)

    Notice that $S_v^{j*}$ and $S_h^{j*}$ satisfy

    $ \displaystyle \beta_h^jS_v^{j*}S_h^{j*}\int^\infty_0m_h^j(\tau)\pi^j_1(\tau)d\tau\int^\infty_0\beta_v^j(a)\pi^j_2(a)da=\mu_v+\alpha_v^j, $ (29)

    for $j=1,\cdots, n.$ It then follows from (6) and (24) that we have

    $ \displaystyle S_v^{1^*}S_h^{1^*}=\frac{\mu_v+\alpha_v^1}{\beta_h^1b_1}=\frac{\Lambda_v\Lambda_h}{\mu_v\mu_h\mathcal R^1_0}. $ (30)

    Substituting (30) into the equation (28), we get the following characteristic equation

    $ λ+μv+αiv=βihΛvΛhμvμhR10bi(λ), $ (31)

    where $b_i(\lambda)$ is defined in (24).

    First, assume that $ \mathcal{R}^{i_0}_0 > \mathcal{R}^1_0$ for some $i_0$, and set

    $\displaystyle \mathcal{G}_{i_0}(\lambda)\stackrel{\it def}{=}(\lambda+\mu_v+\alpha_v^{i_0}) -\beta_h^{i_0}\frac{\Lambda_v\Lambda_h}{\mu_v\mu_h\mathcal R^1_0}b_{i_0}(\lambda).$

    Straightforward computation yields that

    $ \displaystyle \mathcal{G}_{i_0}(0)=(\mu_v+\alpha_v^{i_0}) -\beta_h^{i_0}\frac{\Lambda_v\Lambda_h}{\mu_v\mu_h\mathcal R^1_0}b_{i_0}=(\mu_v+\alpha_v^{i_0})(1-\frac{R^{i_0}_0}{R^1_0})<0. $

    Furthermore, for $\lambda$ real, $\mathcal{G}_{i_0}(\lambda)$ is an increasing function of $\lambda$ such that $\lim \mathcal{G}_{i_0}(\lambda)\rightarrow +\infty$ as $\lambda\rightarrow+\infty$. Hence Intermediate Value Theorem implies that the equation (31) has a unique real positive solution. We conclude that in that case $\mathcal{E}_1$ is unstable.

    Next, assume $\mathcal{R}^i_0<\mathcal{R}^1_0$ for all $i=2,\cdots,n$, and set

    $ \displaystyle \mathcal{G}_3(\lambda)\stackrel{\it def}{=}\lambda+\mu_v+\alpha_v^i, \mathcal{G}_4(\lambda)\stackrel{\it def}{=}\displaystyle \beta_h^i\frac{\Lambda_v\Lambda_h}{\mu_v\mu_h\mathcal R^1_0}b_i(\lambda). $ (32)

    Consider $\lambda$ with $\Re\lambda \geq0$. For such $\lambda$, following from (32), we have

    $ |G3(λ)|μv+αiv,|G4(λ)|G4(λ)G4(0)=1R10βihΛvΛhμvμh0mih(τ)πi1(τ)dτ0βiv(a)πi2(a)da=Ri0R10(μv+αiv)<|G3(λ)|.$

    This gives a contradiction. Hence, the equation (31) have no solutions with positive real part and all eigenvalues of these equations have negative real parts. Therefore, the stability of $\mathcal{E}_1$ depends on the eigenvalues of the following system

    $\left\{ λxv=S1v0β1v(a)y1h(a)daxv0β1v(a)I1h(a)daμvxv,λy1v=S1v0β1v(a)y1h(a)da+xv0β1v(a)I1h(a)da(μv+α1v)y1v,λxh=z1h(0)μhxh,dz1h(τ)dτ=(λ+μh+m1h(τ))z1h(τ),z1h(0)=β1hS1hy1v+β1hI1vxh,dy1h(a)da=(λ+μh+α1h(a)+r1h(a))y1h(a),y1h(0)=0m1h(τ)z1h(τ)dτ.\right. $ (33)

    Solving the differential equation, we have

    $ z1h(τ)=z1h(0) eλτπ11(τ),y1h(a)=y1h(0) eλaπ12(a)=z1h(0) eλaπ12(a)0m1h(τ) eλτπ11(τ)dτ. $

    Substituting the above expression for $y_h^1(a)$ into the first and the second equations of (33) yileds that

    $\left\{ (λ+μv+0β1v(a)I1h(a)da)xv+S1vb1(λ)z1h(0)=0,xv0β1v(a)I1h(a)da+(λ+μv+α1v)y1vS1vb1(λ)z1h(0)=0,(λ+μh)xh+z1h(0)=0,β1hI1vxhβ1hS1hy1v+z1h(0)=0.\right. $ (34)

    Direct calculation yields the following characteristic equation

    $ (λ+μv+0β1v(a)I1h(a)da)(λ+μv+α1v)(λ+μh+β1hI1v)=β1hS1hS1vb1(λ)(λ+μv)(λ+μh). $ (35)

    Dividing both sides by $(\lambda+\mu_v)(\lambda+\mu_h)$ gives

    $ \displaystyle \mathcal{G}_5(\lambda)=\mathcal{G}_6(\lambda), $ (36)

    where

    $ G5(λ)=(λ+μv+0β1v(a)I1h(a)da)(λ+μv+α1v)(λ+μh+β1hI1v)(λ+μv)(λ+μh),G6(λ)=β1hS1hS1vb1(λ)=β1hS1hS1v0m1h(τ)eλτπ11(τ)dτ0β1v(a)eλaπ12(a)da. $ (37)

    If $\lambda$ is a root with $\Re\lambda\geq0$, it follows from equation (37) that

    $ |G5(λ)|>|λ+μv+α1v|μv+α1v. $ (38)

    From (29), we have

    $ |G6(λ)||G6(λ)|G6(0)=β1hS1hS1v0m1h(τ)π11(τ)dτ0β1v(a)π12(a)da=μv+α1v<|G5(λ)|. $ (39)

    This leads to a contradiction. The contradiction implies that (36) has no roots such that $\Re\lambda\geq0$. Thus, the characteristic equation for strain one has only roots with negative real parts. Thus, the single strain equilibrium $\mathcal{E}_1$ is locally asymptotically stable if $\mathcal{R}^1_0>1$ and $\mathcal{R}^i_0<\mathcal{R}^1_0,\ i=2,\cdots,n$. This concludes the proof.


    5. Preliminary results and uniform persistence

    In the previous section, we proved that if the disease reproduction number is less than one, all strains are eliminated and the disease dies out. Our next step is to show that the competitive exclusion principle holds for system (2). In the later sections, we always assume that $\mathcal R_0 >1$. Without loss of generality, we assume that

    $\displaystyle \mathcal R^1_0=\max \{\mathcal R^1_0,\cdots,\mathcal R^n_0\}>1.$

    In the following we will show that strain $1$ persists while the other strains die out if $\mathcal R^i_0/\mathcal R^1_0 < b_i/b_1 < 1,i\neq1$, where $b_j$ denotes the probability of a given susceptible vector being transmitted by an infected host with strain $j$. Hence, the strain with the maximal reproduction number eliminates all the rest and the competitive exclusion principle will be established for system (2).

    Mathematically speaking, establishing the competitive exclusion principle means establishing the global stability of the single-strain equilibrium $\mathcal{E}_1$. From Theorem 4.1 we know that if $\mathcal R^i_0/\mathcal R^1_0 < 1,i\neq 1,$ the equilibrium $\mathcal{E}_1$ is locally asymptotically stable. In the following we only need to show that $\mathcal{E}_1$ is a global attractor. The method used here to show this result is similar to the one used in [1,12,13,20].

    Set

    $f(x)=x-1-\ln x.$

    It is easy to check that $f(x)\geq0$ for all $x>0$ and $f(x)$ reaches its global minimum value $f(1)=0$ when $x=1$. Next, let us define the following Lyapunov function

    $ U(t)=U1(t)+U12(t)+ni=2Ui2(t)+U3(t)+U14(t)+ni=2Ui4(t)+U15(t)+ni=2Ui5(t), $ (40)

    where

    $ \left. U1(t)=1q1(0)0m1h(τ)π11(τ)dτf(SvS1v),U12(t)=1S1vq1(0)0m1h(τ)π11(τ)dτI1vf(I1vI1v),Ui2(t)=1S1vq1(0)0m1h(τ)π11(τ)dτIiv,U3(t)=S1hf(ShS1h),U14(t)=1R100p1(τ)E1h(τ)f(E1h(τ,t)E1h(τ))dτ,Ui4(t)=1Δiq1(0)0m1h(τ)π11(τ)dτ0pi(τ)Eih(τ,t)dτ,U15(t)=1q1(0)0m1h(τ)π11(τ)dτ0q1(a)I1h(a)f(I1h(a,t)I1h(a))da.Ui5(t)=1q1(0)0m1h(τ)π11(τ)dτ0qi(a)Iih(a,t)da,\right. $ (41)

    and

    $ qj(a)=aβjv(s)esa(μh+αjh(σ)+rjh(σ))dσds,pj(τ)=Δjqj(0)τmjh(s)esτ(μh+mjh(σ))dσds. $ (42)

    Direct computation gives

    $ \displaystyle p_j(0)=\mathcal R^j_0, $

    and

    $ qj(a)=βjv(a)+(μh+αjh(a)+rjh(a))qj(a),pj(τ)=Δjqj(0)mjh(τ)+(μh+mjh(τ))pj(τ). $ (43)

    The main difficulty with the Lyapunov function $U$ above is that the Lyapunov function $U$ is well defined. Thus in the following we first show that strain one persists both in the hosts and in the vectors as the other strains die out. Let

    $ \displaystyle \hat{X }_1=\bigg\{\varphi_1\in L^1_+(0, \infty)\bigg|\exists s\geq0:\ \int^\infty_0 m_h^1(\tau+s)\varphi_1(\tau)d\tau>0\bigg\}, $
    $ \displaystyle \hat{X }_2=\bigg\{\psi_1\in L^1_+(0, \infty)\bigg|\exists s\geq0:\ \int^\infty_0 \beta_v^1(a+s)\psi_1(a)da>0\bigg\}, $

    and define

    $ \displaystyle X_0=\mathbb{R}_+\times \prod^n_{j=1}\mathbb{R}_+\times \mathbb{R}_+\times \hat{X }_1\times \hat{X }_2\times \prod^n_{i=2}(L^1(0,\infty)\times L^1(0,\infty)), $
    $ \Omega_0=\Omega\cap X_0.$

    Note that $\Omega_0$ is forward invariant. This is because (3) show that $\Omega$ is forward invariant. To see $X_0$ is forward invariant, we firstly demonstrate that $\hat{X }_2$ is forward invariant. Let us assume that the inequality holds for the initial condition. The inequality says that the support of $\beta_v^1(a)$ will intersect the support of the initial condition if it is transferred $s$ units to the right. Since the support of the initial condition only moves to the right, the intersection will take place for any other time if that happens for the initial time. Similarly, $\hat{X }_1$ is also forward invariant. Therefore, $\Omega_0$ is forward invariant.

    Now let us recall two important definitions.

    Definition 5.1. Strain one is called uniformly weakly persistence if there exists some $\gamma>0$ independent of the initial conditions such that

    $ \displaystyle \limsup\limits_{t\rightarrow\infty}\int^\infty_0E_h^1(\tau, t)d\tau > \gamma \text{whenever}\int^\infty_0 \varphi_1(\tau)d\tau > 0, $
    $ \displaystyle \limsup\limits_{t\rightarrow\infty}\int^\infty_0I_h^1(a,t)da > \gamma \text{whenever} \int^\infty_0\psi_1(a)da> 0, $

    and

    $ \displaystyle \limsup\limits_{t\rightarrow\infty}I_v^1(t) > \gamma \text{whenever} I_{v_0}^1> 0, $

    for all solutions of system (2).

    One of the important implications of uniform weak persistence of the disease is that the disease-free equilibrium is unstable.

    Definition 5.2. Strain one is uniformly strongly persistence if there exists some $\gamma>0$ independent of the initial conditions such that

    $ \displaystyle \liminf\limits_{t\rightarrow\infty}\int^\infty_0E_h^1(\tau, t)d\tau > \gamma \text{whenever}\int^\infty_0\varphi_1(\tau)d\tau> 0, $
    $ \displaystyle \liminf\limits_{t\rightarrow\infty}\int^\infty_0I_h^1(a,t)da > \gamma \text{whenever} \int^\infty_0\psi_1(a)da > 0, $

    and

    $ \displaystyle \liminf\limits_{t\rightarrow\infty}I_v^1(t) > \gamma \text{whenever} I_{v_0}^1 > 0, $

    for all solutions of model (2).

    It is evident from the definitions that, if the disease is uniformly strongly persistent, it is also uniformly weakly persistent.

    Now we are able to state the main results in this section.

    Theorem 5.3. Assume $\mathcal R^1_0 >1$ and $\mathcal R^i_0 <\mathcal R^1_0$ for $i=2, \cdots, n$. Furthermore, assume that the other strains except stain 1 will die out, i.e.,

    $ \displaystyle\limsup\limits_{t\rightarrow +\infty}I_v^i(t)=0,\ \limsup\limits_{t\rightarrow +\infty}\int^\infty_0 E_h^i(\tau, t)d\tau=0 \ \;and\;\ \limsup\limits_{t\rightarrow +\infty}\int^\infty_0I_h^i(a, t)da=0, $

    for $ i=2, \cdots, n$. Then strain $1$ is uniformly weakly persistent for the initial conditions that belong to $\Omega_0$, i.e., there exists $\gamma>0$ such that

    $ \displaystyle \limsup\limits_{t\rightarrow+\infty}\beta_h^1I_v^1(t)\geq\gamma, \limsup\limits_{t\rightarrow+\infty}\int^\infty_0 m_h^1(\tau)E_h^1(\tau, t)d\tau\geq\gamma, \limsup\limits_{t\rightarrow+\infty}\int^\infty_0 \beta_v^1(a)I_h^1(a, t)da\geq\gamma. $ }

    Proof. We argue by contradiction. Assume that strain $1$ also dies out. For any $\varepsilon>0$ and every initial condition in $\Omega_0$ such that

    $ \displaystyle \limsup\limits_{t\rightarrow+\infty}\beta_h^1I_v^1(t) < \varepsilon, \limsup\limits_{t\rightarrow+\infty}\int^\infty_0 m_h^1(\tau)E_h^1(\tau, t)d\tau < \varepsilon, \limsup\limits_{t\rightarrow+\infty}\int^\infty_0 \beta_v^1(a)I_h^1(a, t)da<\varepsilon. $

    Following that there exist $T>0$ such that for all $t>T$ we have

    $ \displaystyle \beta_h^jI_v^j(t)<\varepsilon,\ \int^\infty_0 m_h^j(\tau)E_h^j(\tau, t)d\tau<\varepsilon,\ \int^\infty_0 \beta_v^j(a)I_h^j(a, t)da<\varepsilon,\ j=1,\cdots,n. $

    We may assume that the above inequality holds for all $t\geq0$ by shifting the dynamical system. From the first equation in (2) we have

    $ \displaystyle S_v'(t) \geq\Lambda_v-n\varepsilon S_v-\mu_v S_v, S_h'(t) \geq\Lambda_h-n\varepsilon S_h-\mu_h S_h. $

    Exploiting the comparison principle, we have

    $ \displaystyle \limsup\limits_{t\rightarrow+\infty}S_v(t)\geq \liminf\limits_{t\rightarrow+\infty}S_v(t)\geq\frac{\Lambda_v}{n\varepsilon+\mu_v},\ \limsup\limits_{t\rightarrow+\infty}S_h(t)\geq \liminf\limits_{t\rightarrow+\infty}S_h(t)\geq\frac{\Lambda_h}{n\varepsilon+\mu_h}. $

    Since $B_ E^1(t)=E_h^1(0, t),\ B_ I^1(t)=I_h^1(0, t) $, it then follows from system (2) that

    $ \left\{ B1E(t)=E1h(0,t)=β1hShI1v(t)β1hΛhnε+μhI1v(t),dI1v(t)dtΛvnε+μv0β1v(a)I1h(a,t)da(μv+α1v)I1v(t).\right. $ (44)

    By using the equations in (7), we can easily obtain the following inequalities on $B_ E^1(t),\ B_ I^1(t)$ and $I_v^1(t)$:

    $ \left\{ B1E(t)β1hΛhnε+μhI1v(t),B1I(t)=0m1h(τ)E1h(τ,t)dτt0m1h(τ)B1E(tτ)π11(τ)dτ,dI1v(t)dtΛvnε+μvt0β1v(a)B1I(ta)π12(a)da(μv+α1v)I1v(t).\right. $ (45)

    Let us take the Laplace transform of both sides of inequalities (45). Since all functions above are bounded, the Laplace transforms of the functions exist for $\lambda>0$. Denote the Laplace transforms of the functions $ B_E^1(t)$, $ B_I^1(t)$ and $ I_v^1(t)$ by $\hat{B}_E^1(\lambda)$, $\hat{B}_I^1(\lambda)$ and $\hat{I}_v^1(\lambda)$, respectively. Furthermore, set

    $ \displaystyle \hat{K}_1(\lambda)=\int^\infty_0m_h^1(\tau)\pi^1_1(\tau)e^{-\lambda\tau}d\tau, \displaystyle \hat{K}_2(\lambda)=\int^\infty_0\beta_v^1(a)\pi^1_2(a)e^{-\lambda a}da. $ (46)

    Using the convolution property of the Laplace transform, we obtain the following inequalities for $\hat{B}_E^1(\lambda),\ \hat{B}_I^1(\lambda)$ and $\hat{I}_v^1(\lambda)$:

    $ \left\{ ˆB1E(λ)β1hΛhnε+μhˆI1v(λ),ˆB1I(λ)ˆK1(λ)ˆB1E(λ),λˆI1v(λ)I1v(0)Λvnε+μvˆK2(λ)ˆB1I(λ)(μv+α1v)ˆI1v(λ).\right. $ (47)

    Eliminating $\hat{B}_I^1(\lambda)$ and $ \hat{I}_v^1(\lambda)$ yields

    $ ˆB1E(λ)β1hΛvΛhˆK1(λ)ˆK2(λ)(nε+μv)(nε+μh)(λ+μv+α1v)ˆB1E(λ)+β1hΛh(nε+μh)(λ+μv+α1v)I1v(0). $ (48)

    This is impossible since

    $ \displaystyle \frac{\beta_h^1\Lambda_v\Lambda_h\hat{K}_1(0)\hat{K}_2(0)} {\mu_v\mu_h(\mu_v+\alpha_v^1)}:=\mathcal R^1_0>1, $

    we can choose $\varepsilon$ and $\lambda$ small enough such that

    $ \displaystyle \frac{\beta_h^1\Lambda_v\Lambda_h\hat{K}_1(\lambda)\hat{K}_2(\lambda)} {(n\varepsilon+\mu_v)(n\varepsilon+\mu_h)(\lambda+\mu_v+\alpha_v^1)}>1. $

    The contradiction implies that there exists $\gamma>0$ such that for any initial condition in $\Omega_0$, we have

    $ \displaystyle \limsup\limits_{t\rightarrow+\infty}\beta_h^1I_v^1(t)\geq\gamma, \limsup\limits_{t\rightarrow+\infty}\int^\infty_0 m_h^1(\tau)E_h^1(\tau, t)d\tau\geq\gamma, \limsup\limits_{t\rightarrow+\infty}\int^\infty_0 \beta_v^1(a)I_h^1(a, t)da\geq\gamma. $

    In addition, the equation for $I_v^1$ can be rewritten in the form

    $ \displaystyle \frac{dI_v^1}{dt}\geq\frac{\Lambda_v\gamma}{n\gamma+\mu_v}-(\mu_v+\alpha_v^1)I_v^1,$

    which implies a lower bound for $I_v^1$. This concludes the proof.

    Next, we claim that system (2) has a global compact attractor $\mathfrak{T}$. Firstly, define the semiflow $\Psi: [0, \infty)\times \Omega_0\rightarrow \Omega_0$ generated by the solutions of system (2)

    $ Ψ(t;Sv0,I1v0,,Inv0,Sh0,φ1(),ψ1(),,φn(),ψn())=(Sv(t),I1v(t),,Inv(t),Sh(t),E1h(τ,t),I1h(a,t),,Enh(τ,t),Inh(a,t)). $

    Definition 5.4. A set $\mathfrak{T}$ in $\Omega_0$ is called a global compact attractor for $\Psi$ if $\mathfrak{T}$ is a maximal compact invariant set and for all open sets $\mathfrak{U}$ containing $\mathfrak{T}$ and all bounded sets $\mathcal{B}$ of $\Omega_0$ there exists some $T>0$ such that $\Psi(t, \mathcal{B})\subseteq\mathfrak{U}$ holds for $t>T$.

    Theorem 5.5. Under the hypothesis of Theorem 5.3, there exists $\mathfrak{T}$, a compact subset of $\Omega_0$, which is a global attractor for the semiflow $\Psi$ on $\Omega_0$. Moreover, we have

    $ \displaystyle \Psi(t, x^0)\subseteq\mathfrak{T} \text{for every } x^0\in\mathfrak{T},\ \forall t\geq0. $

    Proof. We split the solution semiflow into two components. For an initial condition $x^0\in \Omega_0$, }let $\Psi(t, x^0)=\hat{\Psi}(t, x^0)+\tilde{\Psi}(t, x^0)$, where

    $ ˆΨ(t;Sv0,I1v0,,Inv0,Sh0,φ1(),ψ1(),,φn(),ψn())=(0,0,,0,0,ˆE1h(τ,t),ˆI1h(a,t),,ˆEnh(τ,t),ˆInh(a,t)), $ (49)
    $ ˜Ψ(t;Sv0,I1v0,,Inv0,Sh0,φ1(),ψ1(),,φn(),ψn())=(Sv(t),I1v(t),,Inv(t),Sh(t),˜E1h(τ,t),˜I1h(a,t),,˜Enh(τ,t),˜Inh(a,t)), $ (50)

    and $\displaystyle E_h^j(\tau, t)=\hat{E}_h^j(\tau, t)+\tilde{E}_h^j(\tau, t),\ I_h^j(a, t)=\hat{I}_h^j(a, t)+\tilde{I}_h^j(a, t)$ for $j=1,\cdots,n$. $\hat{E}_h^j(\tau, t),$ $\hat{I}_h^j(a, t)$, $\tilde{E}_h^j(\tau, t)$, $\tilde{I}_h^j (a, t)$ are the solutions of the following equations

    $ \left\{ ˆEjht+ˆEjhτ=(μh+mjh(τ))ˆEjh(τ,t),ˆEjh(0,t)=0,ˆEjh(τ,0)=φj(τ),\right. $ (51)
    $ \left\{ ˆIjht+ˆIjha=(μh+αjh(a)+rjh(a))ˆIjh(a,t),ˆIjh(0,t)=0,ˆIjh(a,0)=ψj(a),\right. $ (52)

    and

    $ \left\{ ˜Ejht+˜Ejhτ=(μh+mjh(τ))˜Ejh(τ,t),˜Ejh(0,t)=βjhShIjv,˜Ejh(τ,0)=0,\right. $ (53)
    $ \left\{ ˜Ijht+˜Ijha=(μh+αjh(a)+rjh(a))˜Ijh(a,t),˜Ijh(0,t)=0mjh(τ)˜Ejh(τ,t)dτ,˜Ijh(a,0)=0.\right. $ (54)

    We can easily see that system (51) and (52) are decoupled from the remaining equations. Using the formula (7) to integrate along the characteristic lines, we obtain

    $ \begin{array}{ll}
    \displaystyle \hat{E}_h^j(\tau, t)=\left\{\begin{array}{ll}
     \displaystyle 0,\mbox{ }  & t>\tau,\\[2ex]
     \displaystyle \varphi_j(\tau-t)\frac{\pi^j_1(\tau)}{\pi^j_1(\tau-t)},\mbox{ }
      & t<\tau,
    \end{array}
    \right. \end{array} $
    (55)
    $ \begin{array}{ll}
    \displaystyle \hat{I}_h^j(a, t)=\left\{\begin{array}{ll}
     \displaystyle 0,\mbox{ }  & t>a,\\[2ex]
     \displaystyle \psi_j(a-t)\frac{\pi^j_2(a)}{\pi^j_2(a-t)},\mbox{ }
      & t<a.
    \end{array}
    \right.\\[2ex] \end{array} $
    (56)

    Integrating $\hat{E}_h^j$ with respect to $\tau$ yields

    $ \displaystyle \int^\infty_t \varphi_j(\tau-t)\frac{\pi^j_1(\tau)}{\pi^j_1(\tau-t)}d\tau=\int^\infty_0 \varphi_j(\tau)\frac{\pi^j_1(t+\tau)}{\pi^j_1(\tau)}d\tau\leq e^{-\mu_h t}\int^\infty_0 \varphi_j(\tau)d\tau\rightarrow 0 $

    as $t\rightarrow\infty$. Integrating $\hat{I}_h^j$ with respect to $a$, we have

    $ \displaystyle \int^\infty_t \psi_j(a-t)\frac{\pi^j_2(a)}{\pi^j_2(a-t)}da=\int^\infty_0 \psi_j(a)\frac{\pi^j_2(t+a)}{\pi^j_2(a)}da\leq e^{-\mu_h t}\int^\infty_0 \psi_j(a)da \rightarrow 0 $

    as $t\rightarrow\infty$. This implies that $\hat{\Psi}(t, x^0)\rightarrow0$ as $t\rightarrow\infty$ uniformly for every $x^0\in\mathcal{B}\subseteq \Omega_0$, where $\mathcal{B}$ is a ball of a given radius.

    In the following we need to show $\tilde{\Psi}(t, x)$ is completely continuous. We fix $t$ and let $x^0\in \Omega_0$. Note that $\Omega_0$ is bounded. We have to show that the family of functions defined by

    $ ˜Ψ(t,x0)=(Sv(t),I1v(t),,Inv(t),Sh(t),˜E1h(τ,t),˜I1h(a,t),,˜Enh(τ,t),˜Inh(a,t)) $

    is a compact family of functions for that fixed $t$, which are obtained by taking different initial conditions in $\Omega_0$. The family

    $ \{\tilde{\Psi}(t, x^0)|x^0\in \Omega_0, t-\text{fixed}\}\subseteq \Omega_0, $

    and, therefore, it is bounded. Thus, we have established the boundedness of the set. To show that $\tilde{\Psi}(t, x)$ is precompact, we first see the third condition of $\lim_{t\rightarrow\infty}\int^\infty_t\tilde{E}_h^j(\tau, t)d\tau=0$ and $\lim_{t\rightarrow\infty}\int^\infty_t\tilde{I}_h^j(a, t)da=0$ in the Frechet-Kolmogorov Theorem of [21]. The third condition in [21] is trivially satisfied since $ \tilde{E}_h^j(\tau, t)=0$ for $ \tau>t$ and $ \tilde{I}_h^j(a, t)=0$ for $ a>t$. To use the second condition of the Frechet-Kolmogorov Theorem in [21], we must bound by two constants the L$^1$-norms of $\partial E_h^j/\partial\tau$ and $\partial I_h^j/\partial a$. Notice that

    $ \begin{array}{ll}
    \displaystyle \tilde{E}_h^j(\tau, t)=\left\{\begin{array}{ll}
     \displaystyle \tilde{B}_E^j(t-\tau)\pi^j_1(\tau),\mbox{ }  & t>\tau,\\[2ex]
     \displaystyle 0,\mbox{ }
      & t<\tau,\\[2ex]
    \end{array}
    \right.\\[2ex] \displaystyle \tilde{I}_h^j(a, t)=\left\{\begin{array}{ll} \displaystyle \tilde{B}_I^j(t-a)\pi^j_2(a),\mbox{ } & t>a,\\[2ex] \displaystyle 0,\mbox{ } & t<a, \end{array}\right. \end{array} $
    (57)

    where

    $ ˜BjE(t)=βjhSh(t)Ijv(t),˜BjI(t)=0mjh(τ)˜Ejh(τ,t)dτ=t0mjh(τ)˜BjE(tτ)πj1(τ)dτ. $ (58)

    $\tilde{B}_E^j(t)$ is bounded because of the boundedness of $S_h$ and $I_v^j$. Hence, the $\tilde{B}_E^j(t)$ satisfies

    $ ˜BjE(t)k1. $

    Therefore, we obtain

    $ ˜BjI(t)=t0mjh(τ)˜BjE(tτ)πj1(τ)dτk2t0˜BjE(tτ)dτ=k2t0˜BjE(τ)dτk1k2t. $

    Next, we differentiate (57) with respect to $\tau $ and $a$:

    $ \begin{array}{ll}
    \displaystyle \bigg|\frac{\partial\tilde{E}_h^j(\tau,
    t)}{\partial\tau}\bigg|\leq\left\{\begin{array}{ll}
    |(\tilde{B}_E^j(t-\tau))'|\pi^j_1(\tau)+\tilde{B}_E^j(t-\tau)|(\pi^j_1(\tau))'|,\mbox{}  & t>\tau,\\[2ex]
    \displaystyle 0,\mbox{ }  & t<\tau,
    \end{array}
    \right.\\[2ex] \displaystyle \bigg|\frac{\partial\tilde{I}_h^j(a, t)}{\partial a}\bigg|\leq\left\{\begin{array}{ll} |(\tilde{B}_I^j(t-a))'|\pi^j_2(a)+\tilde{B}_I^j(t-a)|(\pi^j_2(a))'|,\mbox{} & t>a,\\[2ex] \displaystyle 0,\mbox{ } & t<a. \end{array}\right.\\[2ex] \end{array} $

    We see that $|(\tilde{B}_E^j(t-\tau))'|,\ |(\tilde{B}_I^j(t-a))'|$ are bounded. Differentiating (58), we obtain

    $ (˜BjE(t))=βjh(Sh(t)Ijv(t)+Sh(t)(Ijv(t))),(˜BjI(t))=mjh(t)˜BjE(0)πj1(t)+t0mjh(τ)(˜BjE(tτ))πj1(τ)dτ. $ (59)

    Taking an absolute value and bounding all terms, we can rewrite the above equality as the following inequality:

    $ |(˜BjE(t))|k3,|(˜BjI(t))|k4. $

    Putting all these bounds together, we have

    $ τ˜Ejhk30πj1(τ)dτ+k1(μh+ˉmh)0πj1(τ)dτ<b1,a˜Ijhk40πj2(a)da+k1k2(μh+ˉαh+ˉrh)t0πj2(a)da<b2, $

    where $\bar{m}_h=\sup_{\tau,j}\{m_h^j(\tau)\},\ \bar{\alpha}_h=\sup_{a,j}\{\alpha_h^j(a)\},\ \bar{r}_h=\sup_{a,j}\{r_h^j(a)\}$. To complete the proof, we notice that

    $ 0|˜Ejh(τ+h,t)˜Ejh(τ,t)|dτ≤∥τ˜Ejh|h|b1|h|,0|˜Ijh(a+h,t)˜Ijh(a,t)|da≤∥a˜Ijh|h|b2|h|. $

    Thus, the integral can be made arbitrary small uniformly in the family of functions. That establishes the second condition of the Frechet-Kolmogorov Theorem. We conclude that the family is asymptotically smooth.

    (3) means that the semigroup $\Psi(t)$ is point dissipative and the forward orbit of boundedness sets is bounded in $\Omega_0$. Thus, we prove Theorem 5.5 in accordance with Lemma 3.1.3 and Theorem 3.4.6 in [8].

    Now we have all components to establish the uniform strong persistence. The next proposition states the uniform strong persistence of $I_v^1,E_h^1$ and $I_h^1$.

    Theorem 5.6. Under the hypothesis of Theorem 5.3 strain one is uniformly strongly persistent for all initial conditions that belong to $\Omega_0$, that is, there exists $\gamma>0$ such that

    $ \displaystyle \liminf\limits_{t\rightarrow+\infty}\beta_h^1I_v^1(t)\geq\gamma, \liminf\limits_{t\rightarrow+\infty}\int^\infty_0 m_h^1(\tau)E_h^1(\tau, t)d\tau\geq\gamma, \liminf\limits_{t\rightarrow+\infty}\int^\infty_0 \beta_v^1(a)I_h^1(a, t)da\geq\gamma. $ }

    Proof. We apply Theorem 2.6 in [19]. We consider the solution semiflow $\Psi$ on $\Omega_0$. Let us define three functionals $\rho_l: \Omega_0\rightarrow$R$_+,\ l=1,2,3$ as follows:

    $ \left. ρ1(Ψ(t,x0))=β1hI1v(t),ρ2(Ψ(t,x0))=0m1h(τ)˜E1h(τ,t)dτ,ρ3(Ψ(t,x0))=0β1v(a)˜I1h(a,t)da.\right. $

    Theorem 5.3 implies that the semiflow is uniformly weakly $\rho$-persistent. Theorem 5.5 shows that the solution semiflow has a global compact attractor $\mathfrak{T}$. Total orbits are solutions to the system (2) defined for all times $t\in\mathbb{R}$. Since the solution semiflow is nonnegative, we have

    $\displaystyle \beta_h^1I_v^1(t)= \beta_h^1I_v^1(s)e^{-(\mu_v+\alpha_v^1)(t-s)},$
    $ 0m1h(τ)˜E1h(τ,t)dτ=˜B1I(t)=t0m1h(τ)˜B1E(tτ)π11(τ)dτk1t0˜B1E(tτ)dτ=k1t0˜B1E(τ)dτ=k1t0β1hSh(τ)I1v(τ)dτk2t0I1v(τ)dτ $
    $ =k2t0I1v(s)e(μv+α1v)(τs)dτ=k2I1v(s)μv+α1ve(μv+α1v)s(1e(μv+α1v)t),0β1v(a)˜I1h(a,t)da=t0β1v(a)˜B1I(ta)π12(a)dak3t0˜B1I(ta)da=k3t0˜B1I(a)dak2k3I1v(s)μv+α1ve(μv+α1v)st0(1e(μv+α1v)a)da, $

    for any $s$ and any $t>s$. Therefore,

    $\beta_h^1I_v^1(t)>0, \int^\infty_0 m_h^1(\tau)\tilde{E}_h^1(\tau, t)d\tau>0, \int^\infty_0\beta_v^1(a)\tilde{I}_h^1(a, t)da>0$

    for all $t>s$ provided $\tilde{I}_v^1(s)>0$. Theorem 2.6 in [19] now implies that the semiflow is uniformly strongly $\rho$-persistent. Hence, there exists $\gamma$ such that

    $ \displaystyle \liminf\limits_{t \rightarrow +\infty}\beta_h^1I_v^1(t)\geq\gamma, \liminf\limits_{t \rightarrow +\infty}\int^\infty_0 m_h^1(\tau)E_h^1(\tau, t)d\tau\geq\gamma, \liminf\limits_{t \rightarrow +\infty}\int^\infty_0 \beta_v^1(a)I_h^1(a, t)da\geq\gamma. $

    According to Theorem 5.6, we obtain that for all initial conditions that belong to $\Omega_0$, strain 1 persists. Furthermore we had verified that the solutions of (2) with nonnegative initial conditions belong to the positive cone for all $t\geq0$. All the solutions are in a positively invariant set. Therefore we can obtain the following Theorem 5.7 from Theorem 5.6.

    Theorem 5.7. Under the hypothesis of Theorem 5.3, $\forall t\in\mathrm{R}$, there exists constants $\vartheta>0$ and $M>0$ such that

    $ \vartheta\leq S_v(t)\leq M, \vartheta\leq S_h(t)\leq M,$

    and

    $ \displaystyle\vartheta\leq \beta_h^1I_v^1(t)\leq M,\ \displaystyle\vartheta\leq \int^\infty_0 m_h^1(\tau)E_h^1(\tau, t)d\tau\leq M,\ \displaystyle\vartheta\leq \int^\infty_0 \beta_v^1(a)I_h^1(a, t)da\leq M, $

    for each orbit $( S_v(t), I_v^1(t),\cdots, I_v^n(t), S_h(t), E_h^1(\tau, t), I_h^1(a,t),\cdots,E_h^n(\tau, t), I_h^n(a,t))$ of $\Psi$ in $\mathfrak{T}$.


    6. Principle of competitive exclusion

    In this section we mainly state the main result of the paper.

    Theorem 6.1. Assume $\mathcal R^1_0>1,\mathcal R^i_0/\mathcal R^1_0<b_i/b_1<1,i=2,\cdots,n$. Then the equilibrium $\mathcal{E}_1$ is globally asymptotically stable.

    Proof. From Theorem 4.1 we know that the endemic equilibrium $\mathcal{E}_1$ is locally asymptotically stable. In the following we only need to show that the endemic equilibrium $\mathcal{E}_1$ is global attractor. From Theorem 5.5 there exists an invariant compact set $\mathfrak{T}$ which is global attractor of system (2). Furthermore, it follows from Theorem 5.7 that there exist $\varepsilon_1>0$ and $M_1>0$ such that

    $ \displaystyle \varepsilon_1\leq\frac{I_v^1}{I_v^{1^*}}\leq M_1, \varepsilon_1\leq \frac{E_h^1(\tau, t)}{E_h^{1^*}(\tau)}\leq M_1, \varepsilon_1\leq \frac{I_h^1(a, t)}{I_h^{1^*}(a)}\leq M_1 $

    for any solution in $\Psi$. This makes the Lyapunov function defined in (40) well defined.

    After extensive computation, differentiating $U(t)$ along the solution of system (2) yields that

    $ dU1(t)dt=1S1vq1(0)0m1h(τ)π11(τ)dτ(1S1vSv)[S1v0β1v(a)I1h(a)da+μvS1vSv0β1v(a)I1h(a,t)daμvSvni=2Sv0βiv(a)Iih(a,t)da]=μv(SvS1v)2S1vSvq1(0)0m1h(τ)π11(τ)dτ+1q1(0)0m1h(τ)π11(τ)dτ0β1v(a)I1h(a)(1S1vSvSvI1h(a,t)S1vI1h(a)+I1h(a,t)I1h(a))dani=2Sv0βiv(a)Iih(a,t)daS1v0βiv(a)Iih(a,t)daS1vq1(0)0m1h(τ)π11(τ)dτ; $ (60)
    $ dU12(t)dt=(1I1vI1v)(Sv0β1v(a)I1h(a,t)daS1v0β1v(a)I1h(a)daI1vI1v)S1vq1(0)0m1h(τ)π11(τ)dτ=(1I1vI1v)S1v0β1v(a)I1h(a)(SvI1h(a,t)S1vI1h(a)I1vI1v)daS1vq1(0)0m1h(τ)π11(τ)dτ=0β1v(a)I1h(a)(SvI1h(a,t)S1vI1h(a)I1vI1vSvI1h(a,t)I1vS1vI1h(a)I1v+1)daq1(0)0m1h(τ)π11(τ)dτ; $ (61)
    $ dUi2(t)dt=Sv0βiv(a)Iih(a,t)da(μv+αiv)IivS1vq1(0)0m1h(τ)π11(τ)dτ; $ (62)
    $ dU3(t)dt=(1S1hSh)(E1h(0)+μhS1hE1h(0,t)μhShni=2βihShIiv)=μh(ShS1h)2Sh+(E1h(0)E1h(0,t)S1hShE1h(0)+S1hShE1h(0,t))ni=2(Eih(0,t)βihS1hIiv), $ (63)

    and

    $ dU14(t)dt=1R100p1(τ)E1h(τ)f(E1h(τ,t)E1h(τ))1E1h(τ)E1h(τ,t)tdτ=1R100p1(τ)E1h(τ)f(E1h(τ,t)E1h(τ))1E1h(τ)(E1h(τ,t)τ+(μh+m1h(τ))E1h(τ,t))dτ=1R100p1(τ)E1h(τ)df(E1h(τ,t)E1h(τ))=1R10[p1(τ)E1h(τ)f(E1h(τ,t)E1h(τ))|00f(E1h(τ,t)E1h(τ))d(p1(τ)E1h(τ))]=1R10[p1(0)E1h(0)f(E1h(0,t)E1h(0))Δ1q1(0)0m1h(τ)E1h(τ)f(E1h(τ,t)E1h(τ))dτ]=E1h(0)f(E1h(0,t)E1h(0))0m1h(τ)E1h(τ)f(E1h(τ,t)E1h(τ))dτ0m1h(τ)π11(τ)dτ=E1h(0,t)E1h(0)E1h(0)lnE1h(0,t)E1h(0)0m1h(τ)E1h(τ)f(E1h(τ,t)E1h(τ))dτ0m1h(τ)π11(τ)dτ. $ (64)

    The above equality follows from (24) and the fact

    $ p1(τ)E1h(τ)+p1(τ)(E1h(τ))=[Δ1q1(0)m1h(τ)+(μh+m1h(τ))p1(τ)]E1h(τ)p1(τ)(μh+m1h(τ))E1h(τ)=Δ1q1(0)m1h(τ)E1h(τ). $

    We also have

    $ q1(a)I1h(a)+q1(a)(I1h(a))=[β1v(a)+(μh+α1h(a)+r1h(a))q1(a)]I1h(a)q1(a)(μh+α1h(a)+r1h(a))I1h(a)=β1v(a)I1h(a). $

    Similar to the differentiation of $U^1_4(t)$, we have

    $ dU15(t)dt=1q1(0)0m1h(τ)π11(τ)dτ0q1(a)I1h(a)f(I1h(a,t)I1h(a))1I1h(a)I1h(a,t)tda=1q1(0)0m1h(τ)π11(τ)dτ0q1(a)I1h(a)df(I1h(a,t)I1h(a))=q1(0)I1h(0)f(I1h(0,t)I1h(0))0β1v(a)I1h(a)f(I1h(a,t)I1h(a))daq1(0)0m1h(τ)π11(τ)dτ=0m1h(τ)E1h(τ)(I1h(0,t)I1h(0)1lnI1h(0,t)I1h(0))dτ0m1h(τ)π11(τ)dτ0β1v(a)I1h(a)f(I1h(a,t)I1h(a))daq1(0)0m1h(τ)π11(τ)dτ. $ (65)

    Noting that (43), we differentiate the last two terms with respect to $t$, and have

    $ dUi4(t)dt=1Δiq1(0)0m1h(τ)π11(τ)dτ0pi(τ)Eih(τ,t)tdτ=1Δiq1(0)0m1h(τ)π11(τ)dτ0pi(τ)[Eih(τ,t)τ+(μh+mih(τ))Eih(τ,t)]dτ=0pi(τ)dEih(τ,t)+0(μh+mih(τ))pi(τ)Eih(τ,t)dτΔiq1(0)0m1h(τ)π11(τ)dτ=pi(τ)Eih(τ,t)|00Eih(τ,t)dpi(τ)+0(μh+mih(τ))pi(τ)Eih(τ,t)dτΔiq1(0)0m1h(τ)π11(τ)dτ=pi(0)Eih(0,t)Δiqi(0)0mih(τ)Eih(τ,t)dτΔiq1(0)0m1h(τ)π11(τ)dτ=Ri0Eih(0,t)Δiq1(0)0m1h(τ)π11(τ)dτqi(0)Iih(0,t)q1(0)0m1h(τ)π11(τ)dτ=bib1Eih(0,t)qi(0)Iih(0,t)q1(0)0m1h(τ)π11(τ)dτ. $ (66)

    Similarly, we have

    $ dUi5(t)dt=0qi(a)[Iih(a,t)a+(μh+αih(a)+rih(a))Iih(a,t)]daq1(0)0m1h(τ)π11(τ)dτ=qi(0)Iih(0,t)q1(0)0m1h(τ)π11(τ)dτ0βiv(a)Iih(a,t)daq1(0)0m1h(τ)π11(τ)dτ. $ (67)

    Adding all five components of the Lyapunov function, we have

    $ \displaystyle U'(t)=U^1+U^2, $

    where

    $ U1(t)=μv(SvS1v)2S1vSvq1(0)0m1h(τ)π11(τ)dτ+0β1v(a)I1h(a)(1S1vSvSvI1h(a,t)S1vI1h(a)+I1h(a,t)I1h(a))daq1(0)0m1h(τ)π11(τ)dτ+0β1v(a)I1h(a)(SvI1h(a,t)S1vI1h(a)I1vI1vSvI1h(a,t)I1vS1vI1h(a)I1v+1)daq1(0)0m1h(τ)π11(τ)dτμh(ShS1h)2Sh+(E1h(0)E1h(0,t)S1hShE1h(0)+S1hShE1h(0,t)) $
    $ +E1h(0,t)E1h(0)E1h(0)lnE1h(0,t)E1h(0)0m1h(τ)E1h(τ)f(E1h(τ,t)E1h(τ))dτ0m1h(τ)π11(τ)dτ+0m1h(τ)E1h(τ)(I1h(0,t)I1h(0)1lnI1h(0,t)I1h(0))dτ0m1h(τ)π11(τ)dτ0β1v(a)I1h(a)f(I1h(a,t)I1h(a))daq1(0)0m1h(τ)π11(τ)dτ, $ (68)

    and

    $ U2(t)=ni=2Sv0βiv(a)Iih(a,t)daS1v0βiv(a)Iih(a,t)daS1vq1(0)0m1h(τ)π11(τ)dτ+ni=2Sv0βiv(a)Iih(a,t)da(μv+αiv)IivS1vq1(0)0m1h(τ)π11(τ)dτni=2(Eih(0,t)βihS1hIiv)+ni=2(bib1Eih(0,t)qi(0)Iih(0,t)q1(0)0m1h(τ)π11(τ)dτ)+ni=2(qi(0)Iih(0,t)q1(0)0m1h(τ)π11(τ)dτ0βiv(a)Iih(a,t)daq1(0)0m1h(τ)π11(τ)dτ). $ (69)

    Canceling terms, (68) can be simplified as

    $ U1(t)=μv(SvS1v)2S1vSvq1(0)0m1h(τ)π11(τ)dτμh(ShS1h)2Sh+0β1v(a)I1h(a)(3S1vSvI1vI1vSvI1h(a,t)I1vS1vI1h(a)I1v+lnI1h(a,t)I1h(a))daq1(0)0m1h(τ)π11(τ)dτ+E1h(0)(S1hSh+S1hE1h(0,t)ShE1h(0)lnE1h(0,t)E1h(0))+0m1h(τ)E1h(τ)(I1h(0,t)I1h(0)E1h(τ,t)E1h(τ)+lnE1h(τ,t)E1h(τ)I1h(0)I1h(0,t))dτ0m1h(τ)π11(τ)dτ. $ (70)

    Direct computation yields that

    $ 0m1h(τ)E1h(τ)(I1h(0,t)I1h(0)E1h(τ,t)E1h(τ))dτ=I1h(0,t)I1h(0)0m1h(τ)E1h(τ)dτ0m1h(τ)E1h(τ,t)dτ=I1h(0,t)I1h(0)I1h(0)I1h(0,t)=0,0m1h(τ)E1h(τ)(E1h(τ,t)E1h(τ)I1h(0)I1h(0,t)1)=I1h(0)I1h(0,t)0m1h(τ)E1h(τ,t)dτ0m1h(τ)E1h(τ)dτ=I1h(0)I1h(0,t)I1h(0,t)I1h(0)=0. $ (71)

    By using (71), (70) can be simplified as

    $ U1(t)=μv(SvS1v)2S1vSvq1(0)0m1h(τ)π11(τ)dτμh(ShS1h)2Sh0β1v(a)I1h(a)[f(S1vSv)+f(I1vI1v)+f(SvI1h(a,t)I1vS1vI1h(a)I1v)]daq1(0)0m1h(τ)π11(τ)dτ10m1h(τ)π11(τ)dτ0m1h(τ)E1h(τ)f(E1h(τ,t)I1h(0)E1h(τ)I1h(0,t))dτ+E1h(0)[f(S1hSh)+f(S1hE1h(0,t)ShE1h(0))]. $ (72)

    Noting that $E_h^1(0, t)=\beta_h^1 S_h I_v^1,\ E_h^{1^*}(0)=\beta_h^1 S_h^{1^*} I_v^{1^*}$, we get

    $ \displaystyle \frac{S_h^{1^*}E_h^1(0,t)}{S_hE_h^{1^*}(0)}=\frac{S_h^{1^*}\beta_h^1 S_h I_v^1}{S_h\beta_h^1 S_h^{1^*} I_v^{1^*}}=\frac{I_v^1}{I_v^{1^*}}. $ (73)

    Furthermore, from (25) and (42) we have

    $ 0β1v(a)I1h(a)f(I1vI1v)daq1(0)0m1h(τ)π11(τ)dτ=0β1v(a)I1h(a)daq1(0)0m1h(τ)π11(τ)dτf(I1vI1v)=I1h(0)0m1h(τ)π11(τ)dτf(I1vI1v)=E1h(0)f(I1vI1v). $ (74)

    Finally, simplifying (72) with (73) and (74), we obtain

    $ U1(t)=μv(SvS1v)2S1vSvq1(0)0m1h(τ)π11(τ)dτμh(ShS1h)2Sh0β1v(a)I1h(a)[f(S1vSv)+f(SvI1h(a,t)I1vS1vI1h(a)I1v)]daq1(0)0m1h(τ)π11(τ)dτ0m1h(τ)E1h(τ)f(E1h(τ,t)I1h(0)E1h(τ)I1h(0,t))dτ0m1h(τ)π11(τ)dτE1h(0)f(S1hSh). $ (75)

    Canceling terms, (69) can be simplified as

    $ U2(t)=ni=2[(bib11)Eih(0,t)+(βihS1hμv+αivS1vq1(0)0m1h(τ)π11(τ)dτ)Iiv]. $ (76)

    Simplifying (76) with (25), we get

    $ U2(t)=ni=2[(bib11)Eih(0,t)+βihΛh(μv+Λhb1)μh(μvR10+Λhb1)(1R10biRi0b1)Iiv]. $ (77)

    Hence, by using (75) and (77) we obtain

    $ U(t)=μv(SvS1v)2S1vSvq1(0)0m1h(τ)π11(τ)dτμh(ShS1h)2Sh0β1v(a)I1h(a)[f(S1vSv)+f(SvI1h(a,t)I1vS1vI1h(a)I1v)]daq1(0)0m1h(τ)π11(τ)dτ0m1h(τ)E1h(τ)f(E1h(τ,t)I1h(0)E1h(τ)I1h(0,t))dτ0m1h(τ)π11(τ)dτE1h(0)f(S1hSh)+ni=2[(bib11)Eih(0,t)+βihΛh(μv+Λhb1)μh(μvR10+Λhb1)(1R10biRi0b1)Iiv]. $ (78)

    Since $f(x)\geq0$ for $x>0$, $\mathcal R^i_0/\mathcal R^1_0<b_i/b_1<1,i\neq1$ we have $U'\leq0$. Define,

    $ \displaystyle \Theta_2= \bigg\{(S_v, I_v^1,\cdots,I_v^n, S_h, E_h^1, I_h^1,\cdots,E_h^n, I_h^n)\in \Omega_0\bigg|U'(t)=0\bigg\}. $

    We want to show that the largest invariant set in $\Theta_2$ is the singleton $\mathcal{E}_1$. First, we notice that equality in (78) occurs if and only if $S_v( t)=S_v^{1^*},\ S_h( t)=S_h^{1^*},\ E_h^i(0,t)=0,\ I_v^i=0$, and

    $ \displaystyle \frac{I_h^1(a, t)I_v^{1^*}} {I_h^{1^*}(a)I_v^1}=1, \frac{E_h^1(\tau, t)I_h^{1^*}(0)}{E_h^{1^*}(\tau)I_h^1(0, t)}=1. $ (79)

    Thus, we obtain

    $ \displaystyle \frac{I_h^1(a, t)} {I_h^{1^*}(a)}=\frac{I_v^1(t)} {I_v^{1^*}}. $ (80)

    It is obvious that the left term $\frac{I_h^1(a, t)} {I_h^{1^*}(a)}$ of (80) is a function with $a, t$, while the right term $\frac{I_v^1(t)} {I_v^{1^*}}$ is a function with $t$. So we can assume that $I_h^1(a, t)=I_h^{1^*}(a)g(t)$. Thus we have

    $ \displaystyle I_v^1=I_v^{1^*}g(t). $ (81)

    It follows from (2) we can also obtain

    $ I1v(t)=Sv0β1v(a)I1h(a,t)da(μv+α1v)I1v,=S1v0β1v(a)I1h(a)g(t)da(μv+α1v)I1v,=g(t)S1v0β1v(a)I1h(a)da(μv+α1v)I1v,=g(t)(μv+α1v)I1v(μv+α1v)I1v,=(μv+α1v)(I1vg(t)I1v)=0. $ (82)

    Therefore, we can get

    $I_v^1=I_v^{1^*}.$

    Subsequently, it follows from (80) we have

    $I_h^1(a,t)=I_h^{1^*}(a).$

    Specially, when $a=0$, we have $I_h^1(0,t)=I_h^{1^*}(0).$ Thus from (79) we get

    $ E_h^1(\tau, t)=E_h^{1^*}(\tau). $

    Since $E_h^i(0,t)=0$, then $E_h^i(\tau,t)=E_h^i(0,t-\tau)\pi^i_1(\tau)=0$ for $t>\tau,i=2,\cdots,n$. Similarly, we also have $I_h^i(0,t)=\int^\infty_0m_h^i(\tau)E_h^i(\tau,t)d\tau=0,\ I_h^i(a,t)=I_h^i(0,t-a)\pi^i_2(a)=0$ for $t>a$. At last we conclude that the largest invariant set in $\Theta_2$ is the singleton $\mathcal{E}_1$. Since $\Psi(t)\Omega_0^+\subset \Omega^+_0$, the global attractor, $\mathfrak{T}$, is actually contained in $\Omega^+_0$. Furthermore, the interior global attractor $\mathfrak{T}$ is invariant. By using the above result, we show that the compact global attractor $\mathfrak{T}=\{\mathcal{E}_1\}$. This completes the proof of Theorem 6.1.


    7. Discussion

    In this paper, we formulate a multi-strain partial differential equation (PDE) model describing the transmission dynamics of a vector-borne disease that incorporates both incubation age of the exposed hosts and infection age of the infectious hosts, respectively. The formulas for the reproduction number $\mathcal R^j_0$ of strain $j, ~j=1,\cdots,n$ are obtained from the biological meanings of models. And we define the basic number of the disease as the maximum of the reproduction numbers of each strain. We show that if $\mathcal R_0<1$, the disease-free equilibrium is locally and globally asymptotically stable. That means the disease dies out and the number of infected with each strain goes to zero. If $\mathcal R_0>1$, without loss of generality, assuming $\mathcal R^1_0=\max\{\mathcal R^1_0,\cdots,\mathcal R^n_0\}>1$, we show that the single-strain equilibrium $\mathcal E_1$ corresponding to strain one exists. The single-strain equilibrium $\mathcal E_1$ is locally asymptotically stable when $\mathcal R^1_0>1$ and $\mathcal R^i_0<\mathcal R^1_0, ~i=2,\cdots,n$.

    The main purpose in this article is to extend the competitive exclusion result established by Bremermann and Thieme in [2], who using a multiple-strain ODE model derives that if multiple strains circulate in the population only the strain with the largest reproduction number persists, the strains with suboptimal reproduction numbers are eliminated. The proof of the competitive exclusion principle is based on the proof of the global stability of the single-strain equilibrium $\mathcal E_1$. We approach the result by using a Lyapunov function under a stronger condition that

    $ \displaystyle\frac{\mathcal R^i_0}{\mathcal R^1_0}<\displaystyle\frac{b_i}{b_1}<1, i\neq1. $ (83)

    Our results do not include the case of

    $ \max\{\mathcal R^1_0,\cdots,\mathcal R^n_0\}=\mathcal R^1_0=\mathcal R^2_0=\cdots=\mathcal R^m_0>1, m\leq n, m\geq 2. $

    According to Proposition 3.3 in [16], where the authors proved and simulated by data that if there is no mutation between two strains and if the basic reproduction numbers corresponding to the two strains are the same, then for the two strain epidemic model there exist many coexistence equilibria, we guess that the coexistence of multi-strains may occur and it is impossible for competitive exclusion in this case.

    From the expression (6) of the basic reproduction number $\mathcal R^j_0$ corresponding to strain $j$ and the inequality $ \mathcal R^i_0/\mathcal R^1_0<b_i/b_1,i\neq 1$, it follows that

    ${r_i}<{r_1},$

    where

    $r_j=\frac{\beta_h^j}{\mu_v+\alpha^j_v},~\text{for} ~~j=1,2, \cdots, n.$

    $r_j$ represents the transmission rate of an infectious vector with strain $j$ during its entire infectious period. The condition (83) implies that the following three inequalities hold at the same time,

    $ \mathcal R^i_0<\mathcal R^1_0, {r_i}<{r_1}, {b_i}<{b_1}, i\neq1. $

    Recall that $b_j$ denotes the probability of a given susceptible vector being transmitted by an infected host with strain $j$. Then the condition (83) for the occurrence of competition exclusion of strain $1$ means that the basic reproduction number corresponding to strain $1$, the transmission rate of an infectious vector with strain $1$ during its entire infectious period, and the probability of a given susceptible vector being transmitted by an infected host with strain $1$ are all biggest comparing to three quantities of other strains.


    Acknowledgments

    Y. Dang is supported by NSF of Henan Province 142300410350, Z. Qiu's research is supported by NSFC grants No. 11671206 and No. 11271190 and X. Li is supported by NSF of China grant 11271314 and Plan For Scientific Innovation Talent of Henan Province 144200510021. We are very grateful to two anonymous referees for their careful reading, valuable comments and helpful suggestions, which help us to improve the presentation of this work significantly.



    Acknowledgments



    This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-05029).

    Conflict of interest



    The author declares no conflicts of interest.

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