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Original Articles

Competitive exclusion in a multi-strain immuno-epidemiological influenza model with environmental transmission

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Pages 416-456 | Received 31 Jan 2015, Accepted 22 Jul 2016, Published online: 16 Aug 2016

ABSTRACT

In this paper, a multi-strain model that links immunological and epidemiological dynamics across scales is formulated. On the within-host scale, the n strains eliminate each other with the strain having the largest immunological reproduction number persisting. However, on the population scale, we extend the competitive exclusion principle to a multi-strain model of SI-type for the dynamics of highly pathogenic flu in poultry that incorporates both the infection age of infectious individuals and biological age of pathogen in the environment. The two models are linked through the age-since-infection structure of the epidemiological variables. In addition the between-host transmission rate, the shedding rate of individuals infected by strain j and the disease-induced death rate depend on the within-host viral load. The immunological reproduction numbers Rj and the epidemiological reproduction numbers Rj are computed. By constructing a suitable Lyapunov function, the global stability of the infection-free equilibrium in the system is obtained if all reproduction numbers are smaller or equal to one. If Rj, the reproduction number of strain j is larger than one, then a single-strain equilibrium, corresponding to strain j exists. This single-strain equilibrium is globally stable whenever Rj>1 and Rj is the unique maximal reproduction number and all of the reproduction numbers are distinct. That is, the strain with the maximal basic reproduction number competitively excludes all other strains.

AMS SUBJECT CLASSIFICATION:

1. Introduction

Human infections with new avian influenza A(H7N9) virus were first reported in China in March 2013. Most of the H7N9 infections are believed to result from exposure to infected poultry or contaminated environments. These infections have raised our awareness of potential deadly pandemic that highly pathogenic flu viruses may create. Avian influenza (AI) is an infectious viral disease of birds (especially wild fowl such as ducks and geese) often causing no apparent signs of illness. AI viruses can sometimes spread to domestic poultry and cause large-scale outbreaks of serious disease. Some of these AI viruses, such as A(H5N1) and A(H7N9) viruses, have also been reported to cross the species barrier and cause disease or sub-clinical infections in humans [Citation13] and other mammals. To understand the transmission of highly pathogenic flu among poultry and its potential ability to transform into a deadly human-to-human transmissible strain, we investigate a model of highly pathogenic flu in poultry.

We assume that the death rate of infected poultry depends on the infected within-host individual's viral load. This assumption allows us to study the impact of the viral load on the reproduction number and the disease prevalence. These are important questions in terms of providing insight for disease control and understanding characteristics of the disease. For example, this assumption was applicable to immunological and epidemiological dynamics of HIV. In addition, the persistence and the pandemic threat of avian influenza as well as the very publicized cholera outbreak in Haiti have increased the awareness of diseases that transmit both directly and indirectly through environment. Therefore, in this paper, we formulate a multi-scale immuno-epidemiological of flu viruses with direct and environmental transmission.

The importance of linking mathematical immunology and mathematical epidemiology cannot be underestimated particularly in the light of pathogen evolution and competition [Citation2, Citation16]. Since the seminal paper of Gilchrist and Sasaki [Citation12] that nests a simple within-host model within an SIR epidemic model, a number of authors have considered the connection between these two scales, accounting for the epidemiological scale mostly through the epidemiological reproduction number [Citation1, Citation17, Citation23]. Another approach that commonly has been applied is to link the within-host dynamics with the between-host dynamics when both are described by ordinary differential equation models. Such approach has been used in a number of articles but it typically does not separate the different time scales on which immunological and epidemiological processes progress [Citation8].

In this paper, we use the dynamical approach in [Citation12, Citation21] to formulate a multi-scale immuno-epidemiological model of flu with different time scales. Here we assume that the high pathogenic avian influenza viruses come from the wild birds. HPAI viruses often kill birds within two days of onset of symptoms with mortality rate 90–100%. We then suppose the whole course of the infections is very short, so we do not include super-infection in the immune model. We further assume that all the HPAI viruses are distinct, therefore no co-infection occurs in the within-host model. In the present article, we investigate a scenario where the n strains are subjected to a complete competitive exclusion on the within-host scale. The strain with the maximal immunological reproduction number dominates. Because in our framework the strain with the higher within-host reproduction number takes over almost instantaneously, individuals in the population are infected with only one of the strains.

We aim to show the dependence of the epidemic reproduction number and disease prevalence on the infected within-host individual's viral load just when the poultry begins to be infected with the HPAI viruses and the infection begins to prevail. Thus, we do not consider super-infection in the epidemiological model. The epidemiological model we introduce here, describes the distribution of highly pathogenic flu among poultry. Since H5N1 is deadly to poultry, and chickens do not recover from it, we use an SI epidemic model with environmental transmission. The within-host model includes recruitment and clearance of target cells and allows for an infectious equilibrium.

There have been various approaches developed for continuous age-structured models that are described by first-order PDEs. The general idea is to study the nonlinear semigroup generated by the family of solutions. One approach is to use the theory of integrated semigroups [Citation11, Citation28]. Another method is to integrate solutions along the characteristics to obtain an equivalent integro-differential equation [Citation6, Citation30], and references therein. The goal of this paper is to extend the complete competitive exclusion result established by Bremermann and Thieme [Citation5] to a multi-strain immuno-epidemiological flu model with environmental transmission. Mathematically speaking, the proof of a competitive exclusion principle is based on a proof of global stability of single-strain equilibrium. The stability analysis of nonlinear dynamical systems has always been a topic of both theoretical and practical importance since global stability is one of the most important issues related to their dynamic behaviours.

McCluskey and others, who have incorporated an integration term into a Lyapunov functional form often utilized for Lotka-Volterra type ODE models [Citation20], have paved the way to establish global stability of the endemic equilibrium. The application of the Lyapunov function in age-structured models [Citation6, Citation11], and references therein, requires more delicate analysis than the case of ODE models. This often entails proving asymptotic smoothness of the semigroup generated by the family of solutions and proving existence of an interior global attractor [Citation14], and then defining a Lyapunov function on this attractor. To show the Lyapunov function valid, uniform persistence of the single strain with the maximal reproduction number must be established. In [Citation6, Citation11], and references therein, authors have applied the persistence theory for infinite-dimensional systems by Hale and Waltmamm [Citation15].

We use the immuno-epidemiological model that we formulate to address two questions:

First, do periodic solutions exist in the immuno-epidemiological model of high pathogenic avian influenza (HPAI)? Our model takes the dependence of the epidemiological rates on the viral load explicitly. For the simplest dependence of the transmission and disease-induced death rate on the dynamic within-host viral load, the single-strain equilibrium is globally asymptotically stable and oscillations do not occur. Thus we extend the competitive exclusion principle to a multi-strain model of SI-type for the dynamics of highly pathogenic flu in poultry that incorporates both the infection age of infectious individuals and biological age of pathogen in the environment.

The second question that we address is: How the infected within-host individual's viral load affect the epidemiological reproduction number and the disease prevalence? We find that the epidemiological reproduction number increases with the viral load, and decreases with the clearance rate δj(θ) of the virus strain j of age θ from the environment. In addition, we give another threshold Rj linked to the epidemiological reproduction number Rj. We show that the prevalence seems to increase with the increase in the viral load if Rj>Rj>1. But if Rj>Rj>1, we draw a conclusion that increasing the viral load will decrease the disease prevalence. The prevalence seems to depend on the viral load in an exactly opposite manner. It is an interesting trade-off scenario which can be observed in HIV.

This paper is organized as follows. In Section 2, we formulate a standard immunological and a standard SI epidemiological models with direct and environmental transmission. The two models are linked through age-since-infection and through the epidemiological parameters which depend on the within-host viral load. In Section 3, we discuss the trivial, semi-trivial equilibria and the global stability of the disease-free equilibria. In Section 4, we show the existence of C0 semigroup generated by solutions of the model. We prove some important propositions of the semigroup. In Section 5, we construct a Lyapunov function to show that under the assumption of S(t)>ε, i1(τ,t)>ε, W1(θ,t)>ε, the complete orbit z(t) is the equilibrium E1. In Section 6, we prove that competitive exclusion occurs. How the within-host individuals' HPAI virus load impacts the disease prevalence and the reproduction number, is revealed in Section 7. Finally, a brief discussion is given in Section 8.

2. The model formulation

In this section we formulate an n-strain immuno-epidemiological model of flu with direct and environmental transmission. Individuals infected with strain j experience within-host dynamics. Within a host, the time variable is denoted by τ and signifies time-since-infection. Upon infection, flu virus attacks the lymphocytes, which we call the target cells and we denote them by x when healthy and by ynj when infected by the jth strain of the virus. The number of free virions of strain j is denoted by Vj.

As H5N1 for poultry ultimately leads to death rather than recovery, in our study, unlike most of the models of flu [Citation3], we consider a model incorporating target cell recruitment r and clearance m. Uninfected target cells are infected by interactions with free virions of strain j according to a mass action law with rate constant βnj. Productively infected cells, ynj, produce new virions at rate νnj of strain j and die at rate dnj for strain j. Viral clearance rate is given by δnj for strain j. The parameter snj is the shedding rate of strain j. The within-host model of strain j is given by the following system of ordinary differential equations: (1) dxdτ=rj=1nβnjVjxmx,dynjdτ=βnjVjxdnjynj,dVjdτ=νnjdnjynj(δnj+snj)VjβnjVjx,j=1,2,n.(1) Variations of Equation (Equation1) have been studied extensively and have been commonly used in the investigation of HIV [Citation25, Citation26] and Hepatitis C dynamics [Citation24]. Mathematically, the HIV/HCV version of Equation (Equation1) has been completely analysed [Citation10].

The within-host reproduction number of strain j in Equation (Equation1) is computed as follows: (2) Rj=βnj(νnj1)δnj+snjrm.(2) The reproduction number of strain j gives the number of secondary infections that virus j-infected cell will produce in an entirely healthy cell during its lifespan as infected. Equation (Equation1) always has an infection-free equilibrium E0=(r/m,0,,0), and if Rj>1, then the infected equilibrium Ej=(xnj,0,,0,ynj,0,,0,0,,0,Vj,0,,0) exists with xnj=δnj+snjβnj(νnj1),ynj=βnjdnjVjxnj,Vj=mβnj(Rj1). The long-term outcome of the competition of the n strains in Equation (Equation1) is competitive exclusion. The strain with the larger reproduction number wins and eliminates the strain with the smaller reproduction number [Citation9].

We now introduce an n-strain epidemiological model with direct and environmental transmission. We assume that the pathogen causing the disease is represented by multiple strains. To introduce the model, we denote by S(t) the number of susceptible poultry individuals, where t is the chronological time. We structure the infected individuals by age-since-infection τ. Let ij(τ,t) be the density of individuals infected by strain j. Individuals in the class ij(τ,t) experience the same within-host dynamics given by Equation (Equation1) for strain j. We assume that for individuals in class j, the immunological reproduction number Rj is maximal so strain j is the main strain that infects them. Let Wj(θ,t) denote the number of virions of age θ0 of strain j at time t in the environment.

With the above notations, we study the following infection-age-structured model with environmental transmission. (3) dSdt=Λj=1nS0βj(τ)ij(τ,t)dτj=1nS0ξj(θ)Wj(θ,t)dθμS,ij(τ,t)τ+ij(τ,t)t=(μ+μjVj(τ))ij(τ,t),ij(0,t)=S0βj(τ)ij(τ,t)dτ+S0ξj(θ)Wj(θ,t)dθ,Wj(θ,t)θ+Wj(θ,t)t=δj(θ)Wj(θ,t),Wj(0,t)=0ηj(τ)ij(τ,t)dτ,j=1,2,,n.(3) Let Λ be the birth/recruitment rate, μ be the natural death rate of susceptible hosts at zero viral load, μjVj(τ) gives the additional host mortality due to the virus, and here we assume the simplest form. Gilchrist and Sasaki [Citation12] use a different form but our within-host model does not involve the immune response. We also assume that all susceptible individuals have approximately the same equilibrium level of healthy lymphocytes. The transmission coefficient of βj(τ) of strain j and the shedding rate of ηj(τ) of an infected individual of infection age τ infected by strain j are also dependent on the within-host viral load. In influenza, infectivity and shedding of the virus are associated with higher within-host viral load. For simplicity, we may assume that βj(τ) and ηj(τ) are proportional to the viral load at a given age-since-infection τ, although other functional forms are also possible. Hence, βj(τ)=cjVj(τ),where cj=ljsj,ηj(τ)=bjVj(τ),where bj=kjsj,j=1,2,,n, where Vj(τ) is the viral load of the strain j. lj and kj represent the probability of successful transmission. sj is the shedding rate of strain j. ξj(θ) is the transmission rate of strain j from the environmental contamination. δj(θ) is the clearance rate of the virus strain j of age θ from the environment.

Here the susceptible individuals are recruited at a rate Λ. Susceptible individuals can become infected with strain j either through a direct contact with an infected individual with strain j or through coming into contact with viral particles of strain j that are in the environment. Infection through direct contact with infected individuals can happen through contact with individuals of any age-since-infection at a specific age-specific transmission rate. As a consequence, the force of infection of susceptible individuals through direct contact is given by the integral over all ages-since-infection. So is the force of infection of susceptible individuals through the contaminated environment. Upon infection through direct or indirect transmission, the newly infected individuals move to the infected with strain j class with age-since-infection equal to zero, that is, they move to the boundary condition. Infected individuals with strain j shed the virus into the environment at a rate ηj(τ). All viral particles shed by individuals infected with strain j of all age classes are given by the integral. This is the number of virions with strain j class with age-since-infection equal to zero at time t in the environment.

Model (Equation3) is equipped with the following initial conditions: S(0)=S0,ij(τ,0)=ij0(τ),Wj(θ,0)=Wj0(θ). Initial conditions are determined in advance and do not depend on the within-host model. All parameters are nonnegative, Λ>0,μ>0, and μj>0. We make the following assumptions on the parameter functions.

Assumption 2.1.

The parameter functions satisfy the following:

  1. The functions βj(τ) and ξj(θ) are bounded and uniformly continuous for every j. When βj(τ) and ξj(θ) are of compact support, the support has non-zero Lebesgue measure.

  2. The functions δj(θ),ηj(τ) belong to L(0,).

  3. The functions ij0(τ) and Wj0(θ) are integrable.

    Define the space of functions X=R×j=1n(L1(0,)×L1(0,)). Note that X is a closed subset of a Banach Space, and hence is a complete metric space. The norm on X is taken to be: x=|S|+0|i1(τ)|dτ+0|W1(θ)|dθ++0|in(τ)|dτ+0|Wn(θ)|dθ, for x=(S,i1(τ),W1(θ),,in(τ),Wn(θ))X.

It can be verified that solutions of Equation (Equation3) with nonnegative initial conditions belong to the positive cone for t0. Furthermore, adding the first and all equations for ij, we have ddtS(t)+j=1n0ij(τ,t)dτΛμS(t)+j=1n0ij(τ,t)dτ. Hence lim suptS(t)+j=1n0ij(τ,t)daΛμ. The free virus in the environment can be bounded as follows: ddtj=1n0Wj(θ,t)dθj=1nWj(0,t)δ˙j=1n0Wj(θ,t)dθ=j=1n0ηj(τ)ij(τ,t)dτδ˙j=1n0Wj(θ,t)dθΛμη¯δ˙j=1n0Wj(θ,t)dθ. Where η¯=max{η1(τ),,ηn(τ)} and δ˙=min{δ1(θ),,δn(θ)}. Hence lim suptj=1n0Wj(θ,t)dθη¯Λμδ˙=η¯Λδ˙μ. Therefore, the following set is positively invariant for system: Ω=(S,i1,W1,,in,Wn)X+S(t)+j=1n0ij(τ,t)dτΛμ, j=1n0Wj(θ,t)dθη¯Λδ˙μ. Finally, since the exit rate from the infectious compartment is given by μ+μjVj(τ), the probability of still being infectious after τ time units is given by (4) πj1(τ)=eμτeμj0τVj(s)ds,πj2(θ)=e0θδj(s)ds,(4) where πj2(θ) denotes the probability of the virus with strain j of age θ still being in the environment.

The reproduction number of strain j in system (Equation3) is given by the following expression: (5) Rj=Λμ0βj(τ)πj1(τ)dτ+0ηj(τ)πj1(τ)dτ0ξj(θ)πj2(θ)dθ.(5) The reproduction number of strain j gives the number of secondary infections that one strain j-infected individual will produce in an entirely susceptible population. Rj gives the strength of strain j to invade when rare and alone. The reproduction number Rj consists of two terms. The first term accounts for the number of secondary infections that one strain j-infected individual will produce through direct transmission. The second term accounts for the number of secondary infections that one strain-j-infected individual will produce through environmental transmission. One may define a reproduction number of the whole system: R0=max{R1,,Rn}. In the next section we compute explicit expressions for the equilibria and establish the global stability of the disease-free equilibrium.

3. Equilibria and the global stability of the DFE

System (Equation3) always has a unique disease-free equilibrium E0, which is given by E0=Λμ,0,0, where 0=(0,,0) is n-dimensional vector of zeroes. In addition, for each j there is a corresponding single-strain equilibrium Ej given by Ej=(Sj,0,0,,0,0,ij(τ),Wj(θ),0,0,,0,0). where the non-zero components ij and Vj are in positions 2j and 2j+1, respectively. The endemic equilibrium Ej exists if and only if Rj>1. The non-zero components of the equilibrium Ej are given by (6) Sj=ΛμRj,ij(τ)=ij(0)πj1(τ),Wj(θ)=Wj(0)πj2(θ),(6) where ij(0)=Λ11Rj,Wj(0)=ij(0)0ηj(τ)πj1(τ)dτ. Next, we turn to the linearized equations of the disease-free equilibrium. To introduce the linearization at the disease-free equilibrium E0, let S(t)=Λ/μ+x(t),ıj(τ,t)=yj(τ,t), Wj(θ,t)=zj(θ,t). The linearized system becomes (7) dxdt=j=1nΛμ0βj(τ)yj(τ,t)dτj=1nΛμ0ξj(θ)zj(θ,t)dθμx(t),yj(τ,t)τ+yj(τ,t)t=(μ+μjVj(τ))yj(τ,t),yj(0,t)=Λμ0βj(τ)yj(τ,t)dτ+Λμ0ξj(θ)zj(θ,t)dθ,zj(θ,t)θ+zj(θ,t)t=δj(θ)zj(θ,t),zj(0,t)=0ηj(τ)yj(τ,t)dτ,j=1,2,,n.(7) To study system (Equation7), we notice that the system for yj and zj is decoupled from the equation for x. Hence, the equations for each j are independent from the equations for the other strains. We look for solutions of the form x(t)=x¯eλt, yj(τ,t)=y¯j(τ)eλt and zj(θ,t)=z¯j(θ)eλt. For each j we obtain the following eigenvalue problem: (8) dy¯j(τ)dτ=(λ+μ+μjVj(τ))y¯j(τ),y¯j(0)=Λμ0βj(τ)y¯j(τ)dτ+Λμ0ξj(θ)z¯j(θ)dθ,dz¯j(θ)dθ=(λ+δj(θ))z¯j(θ),z¯j(0)=0ηj(τ)y¯j(τ)dτ.(8) Solving each differential equation, we obtain y¯j(τ)=y¯j(0)eλτπj1(τ),z¯j(θ)=z¯j(0)eλθπj2(θ). Substituting for y¯j(τ) in the equation for z¯j(0), expressing z¯j(0) in term of y¯j(0), and replacing z¯j(0) in the equation for z¯j(θ), we obtain z¯j(θ)=y¯j(0)eλθπj2(θ)0ηj(τ)eλτπj1(τ)dτ. Similarly, substituting for y¯j(τ) and z¯j(θ) in the equation for y¯j(0) and then dividing y¯j(0) from both side of the resulting equation, we get the following characteristic equation for strain j: Gj(λ)=1, where (9) Gj(λ)=Λμ0βj(τ)eλτπj1(τ)dτ+0ηj(τ)eλτπj1(τ)dτ0ξj(θ)eλθπj2(θ)dθ.(9) Now we are ready to establish the following result.

Proposition 3.1.

If R0=max{R1,,Rn}<1, then the disease-free equilibrium is locally asymptotically stable. If R0>1, the disease-free equilibrium is unstable.

Proof.

Assume max{R1,,Rn}<1. Consider λ with λ0. For such λ and each j, we have |Gj(λ)|Gj(λ)Gj(0)=Rj<1. Hence, the equations Gj(λ)=1 do not have a solution with nonnegative real part and the disease-free equilibrium is locally asymptotically stable.

Now assume max{R1,,Rn}>1. For that fixed j0 and λ real, we have Gj0(0)=Rj0>1. Furthermore, limλGj0(λ)=0. Hence, the equation Gj0(λ)=1 has a real positive root. Therefore, the disease-free equilibrium is unstable.

We have established that the disease-free equilibrium is locally stable, i.e. given the conditions on the parameters, if the initial conditions are close enough to the equilibrium, the solution will converge to that equilibrium. In the following paragraphs our objective is to extend these results to global stability results. That is, given the conditions on the parameters, convergence to the equilibrium occurs independent of the initial conditions.

We will use a Lyapunov function to establish the global stability of the disease-free equilibrium. We need to integrate the differential equation along the characteristic lines. Denote the initial condition by Bj(t): ij(0,t)=Bj1(t),Wj(0,t)=Bj2(t). Integrating along the characteristic lines, we obtain (10) ij(τ,t)=Bj1(tτ)πj1(τ),t>τ,ij0(τt)πj1(τ)πj1(τt),t<τ,Wj(θ,t)=Bj2(tθ)πj2(θ),t>θ,Wj0(θt)πj2(θ)πj2(θt),t<θ.(10)

Proposition 3.2.

Assume R0=max{R1,,Rn}1. Then the disease-free equilibrium E0 is globally asymptotically stable.

Proof.

We will use a Lyapunov function. We adopt the logistic function used in [Citation20, Citation22]. Define f(x)=x1lnx. We note that f(x)0 for all x>0. f(x) achieves its global minimum at one, with f(1)=0. Let (11) pj(θ)=θξj(s)πj2(s)ds,qj(τ)=τ(βj(s)+pj(0)ηj(s))πj1(s)ds.(11) We denote S0=Λ/μ. We define the following Lyapunov function: V(t)=fSS0+j=1n0qj(τ)ij(τ,t)πj1(τ)dτ+j=1n0pj(θ)Wj(θ,t)πj2(θ)dθ. Before differentiating, we rewrite the middle term using Equation (Equation10). (12) V(t)=fSS0+j=1n0tqj(τ)Bj1(tτ)πj1(τ)πj1(τ)dτ+j=1ntqj(τ)ij0(τt)πj1(τ)πj1(τt)πj1(τ)dτ+j=1n0tpj(θ)Bj2(tθ)πj2(θ)πj2(θ)dθ+j=1ntpj(θ)Wj0(θt)πj2(θ)πj2(θt)πj2(θ)dθ=fSS0+j=1n0tqj(τ)Bj1(tτ)dτ+j=1ntqj(τ)ij0(τt)πj1(τt)dτ+j=1n0tpj(θ)Bj2(tθ)dθ+j=1ntpj(θ)Wj0(θt)πj2(θt)dθ.(12) Changing variables, we have V(t)=fSS0+j=1n0tqj(tτ)Bj1(τ)dτ+j=1n0qj(t+τ)ij0(τ)πj1(τ)dτ+j=1n0tpj(tθ)Bj2(θ)dθ+j=1n0pj(t+θ)Wj0(θ)πj2(θ)dθ. Differentiating above, we have V(t)=1S0S1S0S+j=1nqj(0)Bj1(t)+0tqj(tτ)Bj1(τ)dτ+j=1n0qj(t+τ)ij0(τ)πj1(τ)dτ+j=1npj(0)Bj2(t)+0tpj(tθ)Bj2(θ)dθ+j=1n0pj(t+θ)Wj0(θ)πj2(θ)dθ=1S0S1S0S+j=1nqj(0)Bj1(t)+0tqj(τ)Bj1(tτ)dτ+j=1ntqj(τ)ij0(τt)πj1(τt)dτ+j=1npj(0)Bj2(t)+0tpj(θ)Bj2(tθ)dθ+j=1ntpj(θ)Wj0(θt)πj2(θt)dθ. Noting that pj(θ)=ξj(θ)πj2(θ),qj(τ)=(βj(τ)+pj(0)ηj(τ))πj1(τ), we have (13) V(t)=1S01SS+j=1n0(βj(τ)+pj(0)ηj(τ))πj1(τ)dτij(0,t)0t(βj(τ)+pj(0)ηj(τ))πj1(τ)Bj1(tτ)dτj=1nt(βj(τ)+pj(0)ηj(τ))πj1(τ)ij0(τt)πj1(τt)dτ+j=1npj(0)Wj(0,t)0tξj(θ)πj2(θ)Bj2(tθ)dθj=1ntξj(θ)πj2(θ)Wj0(θt)πj2(θt)dθ.(13) Merging some middle terms, we have (14) V(t)=1S01SS+j=1n0(βj(τ)+pj(0)ηj(τ))πj1(τ)dτij(0,t)j=1n0t(βj(τ)+pj(0)ηj(τ))ij(τ,t)dτ+t(βj(τ)+pj(0)ηj(τ))ij(τ,t)dτ+j=1npj(0)Wj(0,t)j=1n0tξj(θ)Wj(θ,t)dθ+tξj(θ)Wj(θ,t)dθ=1S01SΛj=1nS0βj(τ)ij(τ,t)dτj=1nS0ξj(θ)Wj(θ,t)dθμS+j=1n0(βj(τ)+pj(0)ηj(τ))πj1(τ)dτij(0,t)j=1n0(βj(τ)+pj(0)ηj(τ))ij(τ,t)dτ+j=1npj(0)Wj(0,t)j=1n0ξj(θ)Wj(θ,t)dθ.(14) Note that Λ=μS0,Wj(0,t)=0ηj(τ)ij(τ,t)dτ. Canceling all terms that cancel, we simplify the above expression as (15) V(t)=μ(SS0)2SS0j=1nSS00βj(τ)ij(τ,t)dτ+j=1nSS00ξj(θ)Wj(θ,t)dθ+j=1nij(0,t)0(βj(τ)+pj(0)ηj(τ))πj1(τ)dτ=μ(SS0)2SS01S0j=1nij(0,t)+1S0j=1nRjij(0,t)=μ(SS0)2SS01S0j=1n(1Rj)ij(0,t)0.(15) The last inequality follows from the fact that R01. Notice that V equals zero if and only if the first term equals zero, i.e. S=S0, and if each of the terms in the sum is zero. We define a set Θ1={(S,i1,W1,,in,Wn)Ω|V(t)=0}. LaSalle's invariance principle [Citation18] implies that the bounded solutions of Equation (Equation3) converge to the largest compact invariant set of Θ1. We will show that this largest compact invariant set is the singleton given by the disease-free equilibrium. First, we saw that S=S0. From the first equation in (Equation3), we have that j=1nS0βj(τ)ij(τ,t)dτ+S0ξj(θ)Wj(θ,t)dθ=0. Noting that all parameters are non-negative, we have ij(0,t)=S0βj(τ)ij(τ,t)dτ+S0ξj(θ)Wj(θ,t)dθ=0. Thus, from the solutions for the equations along the characteristic lines (Equation10), we have that ij(τ,t)=0 for all t>τ. Hence, for t>τ, we have limtij(τ,t)=0,Wj(0,t)=0ηj(τ)ij(τ,t)dτ0,t. Similarly, we also have limtWj(θ,t)=0,for t>max{θ,τ}. We conclude that the disease-free equilibrium is globally stable. This completes the proof.

From the above discussion, we saw that if all reproduction numbers are less or equal to one, all strains are eliminated and the disease dies out. In the next section, we will show the existence and properties of semigroup.

4. Existence and properties of semigroup

Let us define the transformation ij(τ,t)=ij(0,tτ)πj1(τ){t>τ}+ij(τt,0)πj1(τ)πj1(τt){t<τ},Wj(θ,t)=Wj(0,tθ)πj2(θ){t>θ}+Wj(θt,0)πj2(θ)πj2(θt){t<θ}. Let x0Ω. For any neighbourhood B0Ω with x0B0, in accordance with the contraction mapping theorem, for every sufficiently small ε>0, there exists a unique continuous function ψ:[0,ε)×B0Ω, where ψ(t,x) is the solution to the system (Equation3) with ψ(0,x)=x. Here existence and uniqueness can be proved by formulating the solutions to system (Equation3) as a fixed point of an integral operator ψ on an appropriate closed subset of C([0,ε)×B0,Ω), the set of continuous functions from [0,ε)×B0 to Ω, where Ω=(S,i1,W1,,in,Wn)X+S(t)+j=1n0ij(τ,t)dτΛμ, j=1n0Wj(θ,t)dθη¯Λδ˙μ.

For t0, define the continuous mapping T(t):ΩΩ as T(t)x=ψ(t,x), where ψ(t,x) is the solution to system (Equation3) with ψ(0,x)=x. From the discussion in Section 2, solutions to system (Equation3) can be shown to remain bounded in forward time, then existence of the semigroup {T(t)}{t0} can be established. The family of functions {T(t)}{t0} satisfies the properties of a C0 semigroup on Ω [Citation6, Citation14], i.e. to say, TtTs=Tt+s,t,s0,andT0(x)=x,xΩ. Moreover, the semigroup T(t) is point dissipative. Hence, we suppose that the solutions are forward complete, i.e. the solutions exist on the time interval [0,).

In order to prove that system (Equation3) has a global compact attractor T0, we need to establish asymptotic smoothness of the semigroup. As a first step, we define the semiflow ψ of the solutions of system (Equation3) ψ(t:S0,i10(),W10(),,in0(),Wn0())=(S(t),i1(τ,t),W1(θ,t),,in(τ,t),Wn(θ,t)). The semiflow is a mapping ψ:[0,)×ΩΩ. A set T0 in Ω is called a global compact attractor for ψ, if T0 is a maximal compact invariant set and if for all open sets U containing T0 and all bounded sets B of Ω there exists some t>0 such that ψ(t,B)U, for all t>t. We can establish the following proposition.

Proposition 4.1.

The semigroup T(t) is asymptotically smooth.

Proof.

To establish this result, we will apply Lemma 3.1.3 and Theorem 3.4.6 in [Citation14]. To show the assumptions of Lemma 3.1.3 and Theorem 3.4.6 in [Citation14], we split the solution semiflow into two components. For an initial condition x0Ω, we have ψ(t,x0)=ψˆ(t,x0)+ψ~(t,x0). The splitting is done in such a way that ψˆ(t,x0)0 as t for every x0Ω, and for a fixed t and any bounded set B in Ω, the set {ψ~(t,x0):x0B} is precompact. The two components of the semiflow are defined as follows: (16) ψˆ(t:S0,i10(),W10(),,in0(),Wn0())=(0,iˆ1(τ,t),Wˆ1(θ,t),,iˆn(τ,t),Wˆn(θ,t)),ψ~(t:S0,i10(),W10(),,in0(),Wn0())=(S(t),i~1(τ,t),W~1(θ,t),,i~n(τ,t),W~n(θ,t)).(16) where ij(τ,t)=iˆj(τ,t)+i~j(τ,t), Wj(θ,t)=Wˆj(θ,t)+W~j(θ,t), for j=1,,n. iˆj(τ,t),i~j(τ,t) and Wˆj(θ,t),W~j(θ,t) are the solutions of the following equations (the remaining equations are as in system (Equation3)) (17) iˆjt+iˆjτ=(μ+μjVj(τ))iˆj(τ,t),iˆj(0,t)=0,iˆj(τ,0)=ij0(τ),Wˆjt+Wˆjθ=δj(θ)Wˆj(θ,t),Wˆj(0,t)=0,Wˆj(θ,0)=Wj0(θ)(17) and (18) i~jt+i~jτ=(μ+μjVj(τ))i~j(τ,t),i~j(0,t)=S0βj(τ)i~j(τ,t)dτ+0ξj(θ)W~j(θ,t)dθ,i~j(τ,0)=0.W~jt+W~jθ=δj(θ)W~j(θ,t),W~j(0,t)=0ηj(θ)i~j(τ,t)dτ,W~j(θ,0)=0.(18) System (Equation17) is decoupled from the remaining equations. Using the formula (Equation10) to integrate along the characteristic lines, we obtain (19) iˆj(τ,t)=0,t>τ,ij0(τt)πj1(τ)πj1(τt),t<τ.Wˆj(θ,t)=0,t>θ,Wj0(θt)πj2(θ)πj2(θt),t<θ.(19) Integrating iˆj with respect to τ, we obtain tij0(τt)πj1(τ)πj1(τt)dτ=0ij0(τ)πj1(t+τ)πj1(τ)dτeμt0ij0(τ)dτ0, as t. Similarly, tWj0(θt)πj2(θ)πj2(θt)dθ=0Wj0(θ)πj2(t+θ)πj2(θ)dθeδ˙t0Wj0(θ)dθ0. This shows the first claim, i.e. it shows that ψˆ(t,x0)0 as t uniformly for every x0BΩ, where B is a ball of a given radius.

To show the second claim, we need to show compactness. We fix t and let x0Ω. Note that Ω is bounded. We have to show that for that fixed t the family of functions defined by ψ~(t,x0)=(S(t),i~1(τ,t),W~1(θ,t),,i~n(τ,t),W~n(θ,t)) obtained by taking different initial conditions in Ω is a compact family of functions. The family {ψ~(t,x0)|x0Ω,tfixed}Ω, and, therefore, it is bounded. Thus, we have established the boundedness of the set. To show compactness, we first see that the third condition in the Frechét–Kolmogorov theorem [Citation31] is trivially satisfied since i~j(τ,t)=0, W~j(θ,t)=0 for min{θ,τ}>t. To see the second condition, we have to bound by a constant the L1-norm of ij/τ,Wj/θ. To derive that bound, first notice that (20) i~j(τ,t)=B~j1(tτ)πj1(τ),t>τ,0,t<τ,W~j(θ,t)=B~j2(tθ)πj2(θ),t>θ,0,t<θ,(20) where (21) B~j1(t)=S(t)0βj(τ)i~j(τ,t)dτ+S(t)0ξj(θ)W~j(θ,t)dθ,=S(t)0tβj(τ)B~j1(tτ)πj1(τ)dτ+S(t)0tξj(θ)B~j2(tθ)πj2(θ)dθ,B~j2(t)=0ηj(τ)i~j(τ,t)dτ=0tηj(τ)B~j1(tτ)πj1(τ)dτ.(21) First, we notice that for x0Ω, B~j1(t) is bounded. We can observe this by recalling that S is bounded. Hence, the B~j1(t) satisfies the following inequality: (22) B~j1(t)=S(t)0tβj(τ)B~j1(tτ)πj1(τ)dτ+S(t)0tξj(θ)B~j2(tθ)πj2(θ)dθk10tB~j1(tτ)dτ+S(t)0tξj(θ)πj2(θ)0tθηj(τ)B~j1(tθτ)πj1(τ)dτdθ=k10tB~j1(τ)dτ+S(t)0tηj(τ)πj1(τ)0tτξj(θ)πj2(θ)B~j1(tθτ)dθdτk10tB~j1(τ)dτ+S(t)0tηj(τ)πj1(τ)×0tτξj(tτθ)πj2(tτθ)B~j1(θ)dθdτk10tB~j1(τ)dτ+k10teμτdτ0tB~j1(θ)dθk1+k10teμτdτ0tB~j1(τ)dτk1+k1μ0tB~j1(τ)dτ=(k1+k2)0tB~j1(τ)dτ=k30tB~j1(τ)dτ,k2=k1μ,k3=k1+k2.(22) Here k1,k2 and k3 are constants that depend on the bounds of the parameters as well as the bounds of the solution. Gronwall's inequality implies that B~j1(t)B~j1(0)ek3t. In the following, we derive that for x0Ω, B~j2(t) is also bounded. B~j2(t)=0ηj(τ)i~j(τ,t)dτ=0tηj(τ)B~j1(tτ)πj1(τ)dτK20tB~j1(tτ)dτ=K20tB~j1(τ)dτK20tB~j1(0)ek3τdτ=K2B~j1(0)k3(ek3t1)k4ek3t. Next, we differentiate Equation (Equation20) with respect to (τ,θ): i~j(τ,t)τ|(B~j1(tτ))πj1(τ)+B~j1(tτ)(πj1(τ))|,t>τ,0,t<τ.W~j(θ,t)θ|(B~j2(tθ))πj2(θ)+B~j2(tθ)(πj2(θ))|,t>θ,0,t<θ. We have to see that |(B~j1(tτ))| and |(B~j2(tθ))| are bounded. Differentiating Equation (Equation21), we obtain (23) (B~j1(t))=S0tβj(τ)B~j1(tτ)πj1(τ)dτ+Sβj(t)B~j1(0)πj1(t)+S0tβj(τ)(B~j1(tτ))πj1(τ)dτ+S(t)0tξj(θ)B~j2(tθ)πj2(θ)dθ+Sξj(t)B~j2(0)πj2(t)+S0tξj(θ)(B~j2(tθ))πj2(θ)dθ,(B~j2(t))=ηj(t)B~j1(0)πj1(t)+0tηj(τ)(B~j1(tτ))πj1(τ)dτ.(23) Similar to Equation (Equation22), we can rewrite and simplify the above equality as the following inequality: (B~j1(t))k30tB~j1(tτ)dτ+k40tB~j2(tθ)dθ+k6=k30tB~j1(τ)dτ+k40t0tθηj(τ)B~j1(tθτ)πj1(τ)dτdθ+k6k50tB~j1(τ)dτ+k6. Gronwall's inequality then implies that, |(B~j1(t))|k6ek5t. It is evident that |(B~j2(t))|k7. Putting all these bounds together, we have τi~jk6ek5t0πj1(τ)dτ+B~j1(0)ek3t(μ+μjV¯j)0πj1(τ)dτ<k8,θW~jk70πj2(θ)dθ+k4ek3tδ¯j0πj2(θ)dθ<k9, where V¯j=supτ{Vj(τ)}, δ¯j=supθ{δj(θ)}.

To complete the proof, we notice that 0|i~j(τ+h,t)i~j(τ,t)|dτ≤∥τi~j|h|k8|h|,0|W~j(θ+h,t)W~j(θ,t)|dθ≤∥θW~j|h|k9|h|. Thus, the integral can be made arbitrary small uniformly in the family of functions. That establishes the second requirement of the Frechet–Kolmogorov theorem. We conclude that the family is compact. Therefore, we prove that T(t) is asymptotically smooth.

Now we have shown that the semigroup T(t) generated by the system (Equation3) is asymptotically smooth and point dissipative on the state space Ω. We also notice that the forward orbit of bounded sets is bounded in Ω. Then, in accordance with the sufficient condition by Hale [Citation14], we have all components to establish the following proposition.

Proposition 4.2.

Let T(t) be the semigroup generated by system (Equation3) on the state space Ω defined previously. Then there is a strong global attractor T0 in Ω.

Next, we recall several definitions concerning semigroup dynamics in infinite dimension.

A complete orbit through x is a function z:RΩ with z(0)=x and, for any sR, T(t)z(s)=z(t+s) for every t0. The omega limit set of x, ω(x), is defined as ω(x):={yΩ:tn such that T(tn)xy}. The alpha limit set corresponding to the complete orbit z(t) through x is denoted by αz(x), and defined to be the following: αz(x):={yΩ:tn such that z(tn)xy}. A set MΩ is said to be forward invariant if T(t)MM for all t0. A set MΩ is said to be invariant if T(t)M=M for all t0. The following equivalent definition will be important: M is invariant if and only if, for any xM, a complete orbit through x exists and γ(x)M. The stable manifold of a compact invariant set A is denoted by Ws and is defined as Ws(A)={xΩ:ω(x) and ω(x)A}. If there exists a backward orbit z(t) through x, the unstable manifold is defined by Wu(A)={xΩ:αz(x) and αz(x)A}.

Now, we will prove two propositions concerning limits sets in forward and backward time, respectively. First, we will prove a simple result about the stable manifold of the singleton Ei,Ws({Ei}), which will be applied later in the proof of uniform persistence for our system.

Proposition 4.3.

Let xΩ. If xWs({Ej}), then T(t)xEj as t.

Proof.

We will show xWs({Ej})limtT(t)xEj. Suppose by way of contradiction, ϵ>0, tn such that T(tn)xEjϵ. As shown in the proof of Proposition 4.1, the semiflow T(t) can be written as T(t)=Tˆ(t)+T~(t). Since {T~(tn)} is pre-compact, there exists a convergent subsequence: T~(tnk)E. Then T(tnk)E because Tˆ(tnk)0. Then Eω(x), but EEj, which is a contradiction to the definition of the stable manifold.

Second, we consider the alpha limit set corresponding to a complete orbit corresponding to solutions of system (Equation3). The following result is utilized in the application of a Lyapunov functional to our system.

Proposition 4.4.

Let xΩ and consider system (Equation3). If there is complete orbit z(t) through x, then the set {z(t):tR} is pre-compact, and αz(x) is non-empty, compact, and invariant. In addition, if αz(x)=Ei, then z(t)Ei as t.

Proof.

Suppose that z(t)=(S(t),i1(τ,t),W1(θ,t),,in(τ,t),Wn(θ,t)). is a complete orbit through x. Integrating along the characteristics, we get the following more general formulation: (24) dSdt=Λj=1nBj1(t)μS,ij(τ,t)=Bj1(tτ)πj1(τ),Bj1(t)=S0βj(τ)Bj1(tτ)πj1(τ)dτ+S0ξj(θ)Bj2(tθ)πj2(θ)dθ,Wj(θ,t)=Bj2(tθ)πj2(θ),Bj2(t)=0ηj(τ)Bj1(tτ)πj1(τ)dτ,j=1,2,,n.(24) Modifying the arguments in the proof of Proposition 4.1, we can prove that limh00|ij(τ+h,t)ij(τ,t)|dτ=0,limh00|Wj(θ+h,t)Wj(θ,t)|dθ=0, and limhhij(τ,t)dτ=limhhBj1(tτ)πj1(τ)dτK1limhheμτdτ=0,limhhWj(θ,t)dθ=limhhBj2(tθ)πj2(θ)dθK2limhheδ˙θdθ=0, where δ˙=min{δ1(θ),,δn(θ)}.

Clearly the convergence is uniform tR, so {z(t):tR} is pre-compact. Then αz(x) is non-empty, compact. The remaining conclusions follow from Theorem 2.48 in [Citation27].

In order to prove the competitive exclusion principle, we need to use a Lyapunov function to establish that the complete orbit through x is the equilibrium E1 under some stronger condition.

5. Lyapunov function

We will consider the case where all the strains have different reproduction numbers which are greater than one. If the reproduction number Rj, j=1,2,,n are smaller than one, establishing the extinction of strain j can be approached with other more direct methods. Therefore, all of the following results hold for the case where some strains have reproduction number less than unity. But in order to make the notation simpler, we assume that miniRi>1.

Without loss of generality, we assume that (25) R1>R2>>Rn>1.(25) We consider complete orbits for our system to analyse the global dynamics by a Lyapunov function. Suppose that a complete orbit z(t) is defined as in Proposition 4.4 and it suffice system (Equation24).

In the proof of the following proposition, we find a Lyapunov function for a complete orbit z(t), which is well-defined and bounded when z(t) satisfies certain criteria, namely z(t) is bounded from above and bounded away from an appropriate boundary set. Under these criteria, a LaSalle invariance-type argument can be invoked to show that the complete orbit z(t) must be in fact the equilibrium E1.

Proposition 5.1.

Let 0<ε<M< be arbitrary. Assume that xΩ and there exists a complete orbit z(t) through x such that z(t)M, S(t)>ε, i1(τ,t)ε, W1(θ,t)ε, tR. Then, x=E1.

Proof.

We expect to show this result using a Lyapunov function, similar to the one used in [Citation4, Citation20, Citation22]. With f(x)=x1lnx, we define the following Lyapunov function: U(t)=US(t)+UI(t)+UW(t)+Uθτ(t), where (26) US(t)=S1fSS1,UI(t)=0Q(τ)i1(τ)fi1(τ,t)i1(τ)dτ,Q(τ)=τ(S1β1(s)+P(0)η1(s))π11(s)π11(τ)ds,UW(t)=0P(θ)W1(θ)fW1(θ,t)W1(θ)dθ,P(θ)=S1θξ1(s)π12(s)π12(θ)ds,Uθτ(t)=S1j=2n0qj(τ)ij(τ,t)πj1(τ)dτ+S1j=2n0pj(θ)Wj(θ,t)πj2(θ)dθ,pj(θ)=θξj(s)πj2(s)ds,qj(τ)=τ(βj(s)+pj(0)ηj(s))πj1(s)ds.(26) One difficulty with the Lyapunov function U above is that the component US is not defined if S=0, the component UI is not defined if i1(τ,t)=0 on a set of non-zero measure, and component UW is not defined if W1(θ,t)=0. To show that the Lyapunov function above is valid, according to the assumption, we can get B11(t)=S0β1(τ)B11(tτ)π11(τ)dτ+S0ξ1(θ)B12(tθ)π12(θ)dθβ1¯M0i1(0,tτ)dτ+ξ1¯M0W1(0,tθ)dθ(β1¯+ξ1¯)M2, where β1¯=supτ{β1(τ)},ξ1¯=supθ{ξ1(θ)}. From i1(τ,t)=B11(tτ)π11(τ), we have ε1εΛ(11R1)π11(τ)i1(τ,t)i1(τ)(β1¯+ξ1¯)M2π11(τ)Λ(11R1)π11(τ)M1, and W1(θ,t)=W1(0,tθ)π12(θ)=0η1(τ)i1(τ,tθ)dτπ12(θ)η¯0i1(τ,tθ)dτπ12(θ)η¯Mπ12(θ). From the above equality, we have ε2εΛ(11R1)0η1(τ)π11(τ)dτπ12(θ)W1(θ,t)W1(θ)η¯Mπ12(θ)Λ(11R1)0η1(τ)π11(τ)dτπ12(θ)M2. Also, we have ε3εΛμR1SS1MΛμR1M3. This makes the Lyapunov function defined in Equation (Equation26) well-defined.

Because of the complexity of the expressions, we differentiate each component of the Lyapunov function separately (see Equation (Equation26)). (27) US(t)=1S1SS10β1(τ)i1(τ)dτ+S10ξ1(θ)W1(θ)dθ+μS1j=2nS0β1(τ)i1(τ,t)dτS0ξ1(θ)W1(θ,t)dθμSj=2nij(0,t)=μ(SS1)2S+S10β1(τ)i1(τ)1S1SSS1i1(τ,t)i1(τ)+i1(τ,t)i1(τ)dτ+S10ξ1(θ)W1(θ)1S1SSS1W1(θ,t)W1(θ)+W1(θ,t)W1(θ)dθ1S1Sj=2nij(0,t).(27) Next, we need to take the time derivative of UI. Before we do that, we need to transform UI in such a way that it is more convenient for differentiation. We will use the representation of i1, given in Equation (Equation10) [Citation20]: (28) UI(t)=0tQ(τ)i1(τ)fB11(tτ)π11(τ)i1(τ)dτ+tQ(τ)i1(τ)fi10(τt)π11(τ)i1(τ)π11(τt)dτ=0tQ(tτ)i1(tτ)fB11(τ)π11(tτ)i1(tτ)dτ+0Q(t+τ)i1(t+τ)fi10(τ)π11(t+τ)i1(t+τ)π11(τ)dτ.(28) Differentiating the last expression above, we have (29) UI(t)=Q(0)i1(0)fB11(t)i1(0)+0t[Q(tτ)i1(tτ)+Q(tτ)i1(tτ)]fB11(τ)π11(tτ)i1(tτ)dτ+0[Q(t+τ)i1(t+τ)+Q(t+τ)i1(t+τ)]fi10(τ)π11(t+τ)i1(t+τ)π11(τ)dτ=Q(0)i1(0)fi1(0,t)i1(0)+0t[Q(τ)i1(τ)+Q(τ)i1(τ)]fB11(tτ)π11(τ)i1(τ)dτ+t[Q(τ)i1(τ)+Q(τ)i1(τ)]fi10(τt)π11(τ)i1(τ)π11(τt)dτ=Q(0)i1(0)fi1(0,t)i1(0)+0[Q(τ)i1(τ)+Q(τ)i1(τ)]fi1(τ,t)i1(τ)dτ.(29) The above equality follows from Equation (Equation26) and the fact Q(τ)i1(τ)+Q(τ)i1(τ)=i1(τ)(S1β1(τ)+P(0)η1(τ))τ+τ(S1β1(s)+P(0)η1(s))π11(s)π11(τ)(μ+μ1V1(τ))ds+τ(S1β1(s)+P(0)η1(s))π11(s)π11(τ)ds[(μ+μ1V1(τ))]i1(τ)=(S1β1(τ)+P(0)η1(τ))i1(τ). Hence, UI can be simplified as follows: (30) UI(t)=0(S1β1(τ)+P(0)η1(τ))π11(τ)i1(0)fi1(0,t)i1(0)dτ0(S1β1(τ)+P(0)η1(τ))i1(τ)fi1(τ,t)i1(τ)dτ=0(S1β1(τ)+P(0)η1(τ))i1(τ)×i1(0,t)i1(0)i1(τ,t)i1(τ)lni1(0,t)i1(0)+lni1(τ,t)i1(τ)dτ.(30) Now we turn to the derivative of the environmental component. Similarly, we first transform UW to make it more convenient for differentiation. We will use the representation of W1, given in Equation (Equation10) [Citation20]: (31) UW(t)=0tP(θ)W1(θ)fB12(tθ)π12(θ)W1(θ)dθ+tP(θ)W1(θ)fW10(θt)π12(θ)W1(θ)π12(θt)dθ=0tP(tθ)W1(tθ)fB12(θ)π12(tθ)W1(tθ)dθ+0P(t+θ)W1(t+θ)fW10(θ)π12(t+θ)W1(t+θ)π12(θ)dθ.(31) Similar to Equation (Equation29), we differentiate the last expression above and we have (32) UW(t)=P(0)W1(0)fW1(0,t)W1(0)+0[P(θ)W1(θ)+P(θ)W1(θ)]fW1(θ,t)W1(θ)dθ=P(0)W1(0)fW1(0,t)W1(0)0S1ξ1(θ)W1(θ)fW1(θ,t)W1(θ)dθ.(32) The above equality follows from Equation (Equation26) and the fact P(θ)W1(θ)+P(θ)W1(θ)=S1ξ1(θ)+θξ1(s)eθsδ1(σ)dσδ1(θ)dsW1(θ)+S1θξ1(s)eθsδ1(σ)dσds(δ1(θ)W1(θ))=S1ξ1(θ)W1(θ). Hence, (33) UW(t)=0S1ξ1(θ)W1(θ)W1(0,t)W1(0)W1(θ,t)W1(θ)lnW1(0,t)W1(0)+lnW1(θ,t)W1(θ)dθ.(33) Finally, we consider the expression that corresponds to strains two to n. First, we rewrite Uθτ(t) to prepare it for differentiation. (34) Uθτ(t)=j=2nS10qj(τ)ij(τ,t)πj1(τ)dτ+j=2nS10pj(θ)Wj(θ,t)πj2(θ)dθ=j=2nS10tqj(τ)Bj1(tτ)dτ+j=2nS1tqj(τ)ij0(τt)πj1(τt)dτ+j=2nS10tpj(θ)Bj2(tθ)dθ+j=2nS1tpj(θ)Wj0(θt)πj2(θt)dθ=j=2nS10tqj(tτ)Bj1(τ)dτ+j=2nS10qj(t+τ)ij0(τ)πj1(τ)dτ+j=2nS10tpj(tθ)Bj2(θ)dθ+j=2nS10pj(t+θ)Wj0(θ)πj2(θ)dθ.(34) Similar to Equation (Equation29), differentiating the last expression with respect to t, we have (35) Uθτ(t)=j=2nRjR1ij(0,t)j=2nS1SS0βj(τ)ij(τ,t)dτ+S0ξj(θ)Wj(θ,t)dθ=j=2nRjR1ij(0,t)j=2nS1Sij(0,t).(35) Adding all four components of the Lyapunov function, we have (36) U(t)=μ(SS1)2S+S10β1(τ)i1(τ)1S1SSS1i1(τ,t)i1(τ)+i1(τ,t)i1(τ)dτ+S10ξ1(θ)W1(θ)1S1SSS1W1(θ,t)W1(θ)+W1(θ,t)W1(θ)dθ1S1Sj=2nij(0,t)+0(S1β1(τ)+P(0)η1(τ))i1(τ)×i1(0,t)i1(0)i1(τ,t)i1(τ)lni1(0,t)i1(0)+lni1(τ,t)i1(τ)dτ+0S1ξ1(θ)W1(θ)W1(0,t)W1(0)W1(θ,t)W1(θ)lnW1(0,t)W1(0)+lnW1(θ,t)W1(θ)dθ+j=2nRjR1ij(0,t)j=2nS1Sij(0,t)=μ(SS1)2S+S10β1(τ)i1(τ)1S1SSS1i1(τ,t)i1(τ)+i1(0,t)i1(0)+lni1(τ,t)i1(τ)i1(0)i1(0,t)dτ+S10ξ1(θ)W1(θ)1S1SSS1W1(θ,t)W1(θ)+W1(0,t)W1(0)+lnW1(θ,t)W1(θ)+lnW1(0)W1(0,t)dθ+0P(0)η1(τ)i1(τ)i1(0,t)i1(0)i1(τ,t)i1(τ)+lni1(0)i1(0,t)+lni1(τ,t)i1(τ)dτ+j=2nRjR1ij(0,t)j=2nij(0,t).(36) Notice that (37) S10β1(τ)i1(τ)SS1i1(τ,t)i1(τ)+i1(0,t)i1(0)dτ+S10ξ1(θ)W1(θ)SS1W1(θ,t)W1(θ)+W1(0,t)W1(0)dθ+0P(0)η1(τ)i1(τ)i1(0,t)i1(0)i1(τ,t)i1(τ)dτ=S0β1(τ)i1(τ,t)dτS0ξ1(θ)W1(θ,t)dθ+i1(0,t)i1(0)S10β1(τ)i1(τ)dτ+P(0)0η1(τ)i1(τ)dτ+S1W1(0,t)0ξ1(θ)π12(θ)dθP(0)0η1(τ)i1(τ,t)dτ=i1(0,t)+i1(0,t)i1(0)i1(0)+W1(0,t)P(0)P(0)W1(0,t)=0(37) and (38) S10ξ1(θ)W1(θ)lnW1(θ,t)W1(θ)+lnW1(0)W1(0,t)dθ+0P(0)η1(τ)i1(τ)lni1(0)i1(0,t)+lni1(τ,t)i1(τ)dτ=S10ξ1(θ)W1(θ)lnW1(θ,t)W1(θ)dθ+W1(0)lnW1(0)W1(0,t)S10ξ1(θ)π12(θ)dθ+0P(0)η1(τ)i1(τ)lni1(τ,t)i1(τ)dτ+lni1(0)i1(0,t)P(0)0η1(τ)i1(τ)dτ=S10ξ1(θ)W1(θ)lnW1(θ,t)W1(θ)dθ+W1(0)lnW1(0)W1(0,t)P(0)+0P(0)η1(τ)i1(τ)lni1(τ,t)i1(τ)dτ+lni1(0)i1(0,t)P(0)W1(0)=S10ξ1(θ)W1(θ)lnW1(θ,t)W1(θ)dθ+0η1(τ)i1(τ)P(0)lnW1(0)W1(0,t)dτ+0P(0)η1(τ)i1(τ)lni1(τ,t)i1(τ)dτ+S10ξ1(θ)π12(θ)lni1(0)i1(0,t)W1(0)dθ=S10ξ1(θ)W1(θ)lnW1(θ,t)W1(θ)+lni1(0)i1(0,t)dθ+0P(0)η1(τ)i1(τ)lni1(τ,t)i1(τ)+lnW1(0)W1(0,t)dτ=S10ξ1(θ)W1(θ)lni1(0)i1(0,t)W1(θ,t)W1(θ)dθ+0P(0)η1(τ)i1(τ)lni1(τ,t)i1(τ)W1(0)W1(0,t).(38) Using Equations (Equation37) and (Equation38), Equation (Equation36) can be simplified as follows: (39) U(t)=μ(SS1)2S+S10β1(τ)i1(τ)1S1S+lni1(τ,t)i1(τ)i1(0)i1(0,t)dτ+S10ξ1(θ)W1(θ)1S1S+lni1(0)i1(0,t)W1(θ,t)W1(θ)dθ+0P(0)η1(τ)i1(τ)lni1(τ,t)i1(τ)W1(0)W1(0,t)dτ+j=2nRjR11ij(0,t).(39) Next, we notice that (40) S10β1(τ)i1(τ)1SS1i1(τ,t)i1(τ)i1(0)i1(0,t)dτ+S10ξ1(θ)W1(θ)1SS1i1(0)i1(0,t)W1(θ,t)W1(θ)dθ=0,0η1(τ)i1(τ)1i1(τ,t)i1(τ)W1(0)W1(0,t)dτ=0.(40) Indeed, S10β1(τ)i1(τ)1SS1i1(τ,t)i1(τ)i1(0)i1(0,t)dτ+S10ξ1(θ)W1(θ)1SS1i1(0)i1(0,t)W1(θ,t)W1(θ)dθ=i1(0)i1(0)i1(0,t)i1(0,t)=0,0η1(τ)i1(τ)1i1(τ,t)i1(τ)W1(0)W1(0,t)dτ=W1(0)W1(0)W1(0,t)W1(0,t)=0. Using Equation (Equation40) to simplify (Equation39), we obtain (41) U(t)=μ(SS1)2S+S10β1(τ)i1(τ)2S1SSS1i1(τ,t)i1(τ)i1(0)i1(0,t)+lni1(τ,t)i1(τ)i1(0)i1(0,t)dτ+S10ξ1(θ)W1(θ)2S1SSS1i1(0)i1(0,t)W1(θ,t)W1(θ)+lni1(0)i1(0,t)W1(θ,t)W1(θ)dθ+P(0)0η1(τ)i1(τ)1i1(τ,t)i1(τ)W1(0)W1(0,t)+lni1(τ,t)i1(τ)W1(0)W1(0,t)dτ+j=2nRjR11ij(0,t)=μ(SS1)2SS10β1(τ)i1(τ)fS1S+fSS1i1(τ,t)i1(τ)i1(0)i1(0,t)dτS10ξ1(θ)W1(θ)fS1S+fSS1i1(0)i1(0,t)W1(θ,t)W1(θ)dθP(0)0η1(τ)i1(τ)fi1(τ,t)i1(τ)W1(0)W1(0,t)]dτ+j=2nRjR11ij(0,t).(41) Hence, U(t)0. Θ2={(S,i1,W1,,in,Wn)Ω|V(t)=0}. We want to show that the largest invariant set in Θ2 is the singleton E1. First, we notice that equality in Equation (Equation41) occurs if and only if S=S1, ij(0,t)=0 for j=2,,n, and (42) i1(τ,t)i1(τ)i1(0)i1(0,t)=1,i1(0)i1(0,t)W1(θ,t)W1(θ)=1.(42)

Since ij(τ,t)=ij(0,tτ)πj1(τ), we have ij=0 for j=2,,n. From the conditions (Equation42), we have (43) i1(τ,t)=i1(0,t)i1(0)i1(τ),W1(θ,t)=i1(0,t)i1(0)W1(θ).(43) Furthermore, S=S1 implies that dS/dt=0. Consequently, using Equation (Equation43), we have 0=ΛS10β1(τ)i1(τ,t)dτS10ξ1(θ)W1(θ,t)dθμS1=ΛS1i1(0,t)i1(0)0β1(τ)i1(τ)dτS1i1(0,t)i1(0)0ξ1(θ)W1(θ)dθμS1=S10β1(τ)i1(τ)dτ+S10ξ1(θ)W1(θ)dθS1i1(0,t)i1(0)0β1(τ)i1(τ)dτS1i1(0,t)i1(0)0ξ1(θ)W1(θ)dθ=S10β1(τ)i1(τ)dτ+S10ξ1(θ)W1(θ)dθ1i1(0,t)i1(0). Hence, we must have i1(0,t)=i1(0). Thus, from the equality (Equation43), we have i1(τ,t)=i1(τ), W1(θ,t)=W1(θ). We conclude that the largest invariant set in Θ2 is the singleton E1.

By Proposition 4.4, αz(x) is compact, non-empty, and invariant. Let x~αz(x), there exist tn such that xn:=z(tn)x~. In particular, i1(τ,tn)i~1(τ),W1(θ,tn)W~1(θ) in L1 as tn. Then, we claim UI(i1(τ,tn))UI(i~1(τ)) in L1 as tn. Indeed, |UI(i1(τ,tn))UI(i~1(τ))|=0Q(τ)i1(τ)fi1(τ,tn)i1(τ)fi~1(τ)i1(τ)dτ=0τ(S1β1(s)+P(0)η1(s))π11(s)π11(τ)dsi1(τ)fi1(τ,tn)i1(τ)fi~1(τ)i1(τ)dτ=k10τπ11(s)π11(τ)dsi1(τ)maxε1σM1|f(σ)|i1(τ,tn)i1(τ)i~1(τ)i1(τ)dτk20τeμ(sτ)ds|i1(τ,tn)i~1(τ)|dτk2μ0|i1(τ,tn)i~1(τ)|dτ0, as tn.

In a similar way, with W1(θ,tn)W~1(θ), we have UW(W1(θ,tn))UW(W~1(θ)) in L1 as tn. The convergence of the other components of U is a consequence of the continuity of f. Then, U(z(tn))U(x~) as tn. Since U(z(t)) is a non-increasing map, which is bounded above, we conclude that U(z(t))c< as t. Therefore, U(xˆ)=c for all xˆαz(x). Combining this with the fact that αz(x) is invariant, we get that U(g(t))=c for all tR, where g(t) is a complete orbit through xˆ with g(0)=xˆ. Hence, (d/dt)U(g(t))=0 for all tR. This implies that {g(t),tR} is an invariant set with the property that dU/dt=0. Therefore, g(t)=E1 for all t, in particular when t=0. So, xˆ=E1. This show that αz(x)={E1}. Thus, U(z(t))U(E1) for all tR. Since E1 is the unique minimizer of U, z(t)=E1,tR, i.e. x=E1.

6. Competitive exclusion

Proposition 5.1 states that the only complete orbit z(t) in an appropriate subset is the equilibrium E1, which satisfies z(t)M, S(t)>ε, i1(τ,t)ε, W1(θ,t)ε, tR for system (Equation3). If we find a global attractor on this appropriate subset, then due to its invariance, the global attractor will reduce to the equilibrium E1. To follow this strategy, we apply the persistence theory by Hale and Waltmann [Citation15] for infinite-dimensional systems and a result from Magal and Zhao [Citation19] on existence of an interior global attractor. The methods and techniques have been recently employed by other authors. [Citation6, Citation11].

Persistence theory provides a mathematical formalism for determining whether a species will ultimately go extinct or persist in a dynamical model. Consider Ω as the closure of an open set X1, that is, Ω=X1X1, where X1 (assumed to be non-empty) is the boundary of X1. Assume that the C0 semigroup T(t) on Ω satisfies (44) T(t):X1X1,T(t):X1X1.(44)

Let T|:=T(t)|X1 and A be the global attractor for T|. The particular invariant sets of interest are A~=xAω(x). The following result is discussed in [Citation15, Theorem 4.2]:

Theorem 6.1.

Suppose that T(t) satisfies Equation (Equation44) and the following conditions:

  1. T(t) is asymptotically smooth,

  2. T(t) is point dissipative in Ω,

  3. γ+(x):=t>0{T(t)x} is bounded if T(t)x in Ω,

  4. A~ is isolated and has an acyclic covering M~, where M~={M1,M2,,Mn},

  5. Ws({Mj})X1=, j=1,2,,n.

  6. Then T(t) is a uniform repeller with respect to X1, i.e. there is an ε>0 such that xX1,lim inftd(T(t)x,X1)ε.

The following theorem relates uniform persistence to existence of a global attractor in X1.

Theorem 6.2

Magal and Zhao [Citation19]

Assume that the semigroup T(t) satisfying Condition (Equation44), is asymptotically smooth and uniformly persistent, and has a global attractor T0. Then the restriction of T(t) to X1,T(t)|X1, has a global attractor T.

In order to proceed, we need to be precise about considering various forward invariant subset of X. Then, we can define our uniformly persistent set and complementary boundary, and utilize mathematical induction to characterize the dynamics on the boundary set.

For j=1,2,,n, define the following sets: τ¯j=sup{τ(0,):βj(τ)>0, ηj(τ)>0},θ¯j=sup{θ(0,):ξj(θ)>0},Mj0={ζ(τ)L+1:0τ¯jζ(τ)dτ=0},Mj0=L+1Mj0,Nj0=ς(θ)L+1:0θ¯jς(θ)dθ=0,Nj0=L+1Nj0,Xj=R+×1j{Mj0×Nj0}×j+1n(L+1(0,+)×L+1(0,+)),X0=Xn,X0=Ω∖X0,X1=Ω∖∂X1,Xl=(Ω∖∂Xl)Xl1,l=2,3,,n,Zj=R+×1j1(L+1(0,+)×L+1(0,+))×(Mj0×Nj0)×j+1n(L+1(0,+)×L+1(0,+)),(Xj)+=XjZj. Note that τ¯j, θ¯j are allowed to be infinity.

Proposition 6.1.

For j=1,2,,n,Xj and Xj are forward invariant under the semigroup T(t). Also, X0 is forward invariant, and if xX0, then T(t)xE0 as t. In addition, T(t)Xj(Xj)+, t>0.

Proof.

First, we show that Xj is forward invariant under the semigroup T(t) for j=1,2,,n. Assume that on the contrary for some j0 there exists xXj0 and t1>0 such that T(t1)xΩ∖∂Xj0. Let t0=inf{t>0:T(t)Ω∖∂Xj0}. Since Ω∖∂Xj0 is an open set in Ω and by the continuity of the semigroup T(t), we obtain that T(t0)xXj0 and for some i0j0, ii0(τ,t0+ε)Mj00 or Wi0(θ,t0+ε)Nj00 for every ε>0 sufficiently small. Then for this i0, we get ii0(τ,t0)=ii0(0,t0τ)πi01(τ)+ii0(τt0,0)πi01(τ)πi01(τt0)Mi00,Wi0(θ,t0)=Wi0(0,t0θ)πi02(θ)+Wi0(θt0,0)πi02(θ)πi02(θt0)Ni00. For t0, define xi0(τ,t)=ii0(τ,t+t0),yi0(θ,t)=Wi0(θ,t+t0). Then, ϑ(t):=(S(t+t0),i1(τ,t+t0),W1(θ,t+t0),,xi0(τ,t),yi0(θ,t),,in(τ,t+t0),Wn(θ,t+t0)) is a solution to system (Equation3) with initial condition ϑ(0)=T(t0)x. By forward uniqueness of solutions, ii0(τ,t+t0)Mi00 or Wi0(θ,t+t0)Ni00, t0, which is a contradiction to a previous statement. Thus Xj is forward invariant.

Now we have to show Xj is forward invariant. If xXj, then we have case (i):ij(τ,0)Mj0, or case (ii):Wj(θ,0)Nj0. In case (i), if ij(τ,0)Mj0, by the continuity of ij(τ,0), then δ>0, ε>0 such that 0<τδ<τε<τ+δ, ij(τε,0)Mj0. Therefore, we get Mj0ij(τ,ε)ij(τε,0)πj1(τ)πj1(τε). From the properties of the C0-semigroup T(t), we have T(t2+ε)=T(t2)T(ε), i.e. T(t2+ε)x is a solution to system (Equation3) with initial condition T(ε)x for every t20. In accordance with the following inequality, we have Mj0ij(τ,t2+ε)ij(τt2,ε)πj1(τ)πj1(τt2). As a result, we get ij(τ,t)Mj0, t0. Moreover, from Wj(0,t)=0ηj(τ)ij(τ,t)dτ, we can obtain Wj(0,t)Nj0,t0. Therefore, Nj0Wj(θ,t)=Wj(0,tθ)πj2(θ)+Wj(θt,0)πj2(θ)πj2(θt)Wj(0,tθ)πj2(θ).

In case (ii), similarly to the above proof, we can get Wj(θ,t)Nj0, t0 if Wj(θ,0)Nj0. Thus, we have Mj0ij(0,t)=S0βj(τ)ij(τ,t)dτ+S0ηj(θ)Wj(θ,t)dθS0ηj(θ)Wj(θ,t)dθ. By ij(0,t)Mj0, we can obtain ij(τ,t)Mj0 using the same argument as in case (i). This shows forward invariance for j=1. When j>1, notice that Xj1 is forward invariant, which implies forward invariance of Xj. Further, we have T(t)Xj(Xj)+,t>0, j=1,2,,n.

Since XjX0, j=1,2,,n, we conclude that X0 is forward invariant. Also, X0=Xn is forward invariant. In view of our system, it is clear that xX0, we have T(t)xE0 as t, where E0 is the infection-free equilibrium.

Our next goal is to prove the main result, i.e. solutions with initial conditions corresponding to positive productive infected individuals ij(τ,t) or positive concentration of virion in the environment Wj(θ,t) will converge to the equilibrium E1 (the single-strain equilibrium belonging to the strain with maximal basic reproduction number).

Proposition 6.2.

Assume that R1>R2>>Rn. Then, E1 is globally asymptotically stable for system (Equation3) with respect to initial conditions satisfying 0β1(τ)i1(τ,t)dτ+0ξ1(θ)W1(θ,t)dθ>0.

Proof.

We prove the proposition by induction on the number of strains, n, in system (Equation3).

n=1: We note that the proof for this case is contained inside following arguments. We will not repeat that proof but we will simply state that the case n=1 was proved in [Citation7].

Induction Step: Assume that Proposition 6.2 is true for all n<m. We will prove the proposition is true for n=m.

Lemma 1.

If xXl where l2, then T(t)xEl as t.

Proof.

For l=2,3,,m, define the projection operator Pl:(S,i1,W1,,in,Wn)(S,il,Wl,il+1,Wl+1,,im,Wm). Furthermore, define a semigroup of the projected system as follows: Tl(t) is the semigroup on R+×Π1ml+1(L+1×L+1) generated by the solutions to system (Equation3) with n=ml+1 strains, which matches the m strains model except that the first l−1 strains are eliminated. Then, Xl is projection invariant with respect to system (Equation3), i.e. Pl(T(t)x)=Tl(t)Pl(x), xXl, t0. Following from our induction hypothesis, we get that for any xXl, Tl(t)Pl(x)Pl(El) as t. Therefore, Pl(T(t)x)Pl(El) as t. By Proposition 6.1 for T(t)xXl, i(τ,t)0L+10 and W(θ,t)0L+10 as t for all 1l1. Hence, for each l2, T(t)xEl, xXl.

Lemma 2.

The semigroup T(t) is uniformly persistent with respect to X1 and X1. Moreover, there exists a compact set T(X1)+, which is a global attractor for {T(t)}t0 in X1, and u>0 such that lim inftd(i1(τ,t),M10)u,lim inftd(W1(θ,t),N10)u.

Proof.

We will apply Theorem 6.1. Let A be the strong global attractor of X1. Noting that X1=X0l=2mXl and A~:=Aw(x), from Lemma 1 and Proposition 6.1, we obtain that A~={E0,E2,E3,,Em}. We will show that each {Ek}A~, k=0,2,3,,m is an isolated invariant set. Let B:=Br(Ek) be an open ball of sufficiently small radius r around Ek. We claim that B is an isolating neighbourhood. Suppose by way of contradiction that there exists some κ{2,3,,m} or κ=0 contenting with {Ek} being not a maximal invariant set. Let MB be an invariant set with M{Ek}. Then, there exists a complete orbit γ(x)M for xM{Ek}. If xXκXκ+1Xm, by Proposition 5.1, x=Eκ, which is a contradiction. If xXk˙,k˙=0,2,3,,κ1, then x=Ek˙ by Lemma 1 and Proposition 6.1, again a contradiction since Ek˙¯B.

To show that A~ is acyclic, we need to institute that no subset of A~ forms a cycle. From Lemma 1, we know that when xXl, T(t)xEl as t. Hence, by Proposition 4.3 and the definition of stable manifold, l=2,3,,m, xWs({El})xXl. By Proposition 6.1, if xX0,T(t)xE0, t. And we also have xWs({E0})xX0.

First, Let us consider the possibility of a cycle with length greater than or equal to 2. This cycle must include a chain with {Eb}{El} where b<l. If xWu({Eb})Ws({El}), where b<l and b{0,2,3,,m1}, l{2,3,,m}, then, xXl, i.e. ib(τ,0)Mb0,Wb(θ,0)Nb0. The forward invariance of Xl requires that ib(τ,t)Mb0, Wb(θ,t)Nb0 for any negative t on a backward orbit through x. Thus, α(x)Xb implies that x¯Wu({Eb}).

For the above b and l, if the cycle is a chain with {El}{Eb}, then, for xWu({El})Ws({Eb}), xX0 or xXl, l=2,3,,m1. If xX0, it means il(τ,0)Ml0 and Wl(θ,0)Nl0. Similarly to the argument in the above paragraph, we have x¯Wu({El}). If xXl, il(τ,0)Ml0 or Wl(θ,0)Nl0. Since Xl is forward invariant and T(t)Xl(Xl)+, for any negative t on a backward orbit through x, il(τ,t)Ml0 or Wl(θ,t)Nl0. In accordance with the continuity of il(τ,t) and Wl(θ,t), limtil(τ,t)Ml0 or limtWl(θ,t)Nl0. That is to say, x¯Wu({El}), l<l. This excludes the possibility of cycles of length greater than or equal to 2 for T(t)|X1.

Now we consider the possibility of a 1-cycle for T(t)|X1. Then, {Ek}{Ek} for some k=0,2,3,,m. First, we show that we can not have a 1-cycle for E0. It suffice to show that (X0{E0})Wu({E0})=. Let xX0{E0}. Any backward orbit of x must stay in X0 since X0 is forward invariant. If ie(τ,0)Me0, We(θ,0)Ne0, e=1,2,,m, then we have a scalar ODE with a unique positive equilibrium and limtT(t)=0 or ∞. The forward invariance of X0 requires 0τe¯ie(τ,t)dτ=0 and 0θe¯We(θ,t)dθ=0 for any negative t on a backward orbit through x. If τe¯ie(τ,0)dτ>0 or θe¯We(θ,0)dθ>0 for e, then 0τe¯ie(τ,t)dτ>0 or 0θe¯We(θ,t)dθ>0 for some negative t on a backward orbit through x, which is a contradiction. Therefore, there can be no backward orbit through x if τe¯ie(τ,0)dτ>0 or θe¯We(θ,0)dθ>0. Hence, E0 can not be an αlimit point of any xX0{E0}.

Now consider the case xXl for some l{2,3,,m}. We prove by contradiction that {El}{El}. Thus, for x(Wu({El})Ws({El})){El}, there exists a complete orbit z(t) through x such that z(t)El as t±. Hence, z(t) is a homoclinic orbit. The continuity of il(τ,t) and Wl(θ,t) with the fact that limt±il(τ,t)=il(τ)Ml0,limt±Wl(θ,t)=Wl(θ)Nl0 implies that there exists ε>0 such that il(τ,t)ε, Wl(θ,t)ε, tR. In a similar fashion, we can show S(t)ε for all tR. Since both Xl and Ω∖Xl are forward invariant, we can get z(t)Xl,tR. Xl is a projection invariant with respect to Pj and Tj as defined earlier. In other words, in Xl, we can consider an equivalent n=ml+1 strain model with Rl as the maximal reproduction number. In this case, Proposition 5.1 applies to Pl(z(t)) and semigroup Tl(t). We can conclude that x=El, which is a contradiction. Hence, A~ is acyclic for T(t)|X1.

To complete the proof of uniform persistence, we need to prove: Ws({Ek})X1=,k=0,2,3,,m. Assume that on the contrary, there exists xX1 with i1(τ,0)M10 or W1(θ,0)N10 such that xWs({Ek}) where k=0,2,3,,m. Then, T(t)xEk as t. By proposition 6.1, X1 is forward invariant under the semigroup T(t) and T(t)X1(X1)+, t>0. That is, i1(τ,t)M10 or W1(θ,t)N10, t>0. Furthermore, we can get as t, limti1(τ,t)M10,orlimtW1(θ,t)N10. It means that T(t)xEk. It is a contradiction. Thus, Ws({Ek})X1=. By Theorem 6.1, we find that T(t) is uniformly persistent with respect to X1 and X1, i.e. X1 is uniform strong repeller. Then, by Theorem 6.2, we can conclude that there exists a compact set TX1 which is a global attractor for {T(t)}|t0 in X1. Since T(t)X1(X1)+, the global attractor, T, is actually contained in (X1)+. Because of this, u>0 such that lim inftS(t)u,lim inftd(i1(τ,t),M10)u,lim inftd(W1(θ,t),N10)u.

Because the interior global attractor T is invariant, we can find a complete orbit through any point contained in T. For any complete orbit z(t):tRT(X1)+, there exists ε>0 such that S(t)>ε, i1(τ,t)>ε and W1(θ,t)>ε for all tR. Hence, by Proposition 5.1, T=E1. Thus, E1 is the globally attractor for system (Equation3) with respect to initial conditions satisfying 0β1(τ)i1(τ,t)dτ+0ξ1(θ)W1(θ,t)dθ>0. A global attractor is also locally stable by definition, therefore, E1 is indeed globally asymptotically stable.

Since the equilibrium value of the susceptible in the single-strain equilibrium Ej is inversely proportional to the reproduction number of strain j, then the strain that can persist on the smallest number of the susceptible is the strain that persists in the population. That is, the competitive exclusion principle has been established in the context of system (Equation3) and the strain with the maximal reproduction number eliminates all the rest.

We have established the global stability of strain one. This is in contrast with many other age-since-infection models where the age-since-infection has been found to destabilize the equilibrium and sustained oscillations were present [Citation29]. It should be noted that the dramatic difference in the dynamical behaviour is due to the mass action incidence that we assume in model (Equation3). In [Citation29] and other articles where destabilization occurs, standard incidence has been used.

7. How does the infected individual's within-host viral load affect the disease prevalence?

In this section we show how the infected individual's within-host viral load affects the disease prevalence and the epidemiological reproduction number.

First we need to transform the epidemiological reproduction number Rj, to make it more convenient to visualize the link between it and the within-host viral load. We will use the representation of Rj given in Equations (Equation4) and (Equation5). Rj=Λμ0βj(τ)πj1(τ)dτ+0ηj(τ)πj1(τ)dτ0ξj(θ)πj2(θ)dθ=Λμ0cjVj(τ)eμτeμj0τVj(s)dsdτ+0bjVj(τ)eμτeμj0τVj(s)dsdτ0ξj(θ)πj2(θ)dθ=Λμcjμj0(μμμjVj(τ))eμτeμj0τVj(s)dsdτbjμj0(μμμjVj(τ))eμτeμj0τVj(s)dsdτ0ξj(θ)e0θδj(s)dsdθ, we note that 0(μ+μjVj(τ))eμτeμj0τVj(s))dsdτ=e0τ(μ+μjVj(s))ds|0=1. Thus we have Rj=Λμcjμjμ0e0τ(μ+μjVj(s))dsdτ1bjμjμ0e0τ(μ+μjVj(s))dsdτ10ξj(θ)e0θδj(s)dsdθ=Λμ1μ0e0τ(μ+μjVj(s))dsdτcjμj+bjμj0ξj(θ)e0θδj(s)dsdθ. It is evident that the epidemic reproduction number Rj increases with the increase of the viral load and decreases with the decrease of the viral load. It is also easy to see that the epidemic reproduction number Rj increases with the decrease of the clearance rate of δj(θ) of age θ from the environment and decreases with the increase of δj(θ).

Next, we need to take the derivative of Ij with respect to the viral load. Before doing that, we need to transform Ij to make it more convenient for differentiation. Ij is given by Ij=0ij(τ)dτ=ij(0)0eμτeμj0τVj(s)dsdτ=Λ11Rj0eμτeμj0τVj(s)dsdτ. Denoting ρ=0eμτeμj0τVj(s)dsdτ, we can obtain Ij=Λρ11Rj,Rj=Λμ(1μρ)cjμj+bjμj0ξj(θ)e0θδj(s)dsdθ. Taking the ρ derivative of Ij dIjdρ=Λ11Rj+ρΛ2(Rj)2cjμjbjμj0ξj(θ)e0θδj(s)dsdθ=Λ(Rj)2ΛRj+ρΛ2cjμjbjμj0ξj(θ)e0θδj(s)dsdθ(Rj)2 Define Rj as follows: Rj=1+1+4ρΛ△2>1, where =cjμj+bjμj0ξj(θ)e0θδj(s)dsdθ If Rj>Rj>1, we have dIj/dρ<0. So we obtain that increasing ρ decreases the disease prevalence Ij, while ρ decreases with the increase of the viral load Vj(τ). As a result, the disease prevalence Ij will increase with the increase of the viral load Vj(τ). If Rj>Rj, we have dIj/dρ>0. So we obtain that increasing ρ increases the disease prevalence Ij, while ρ decreases with the increase of the viral load Vj(τ). We draw a conclusion that the disease prevalence Ij will decrease with the increase of the viral load Vj(τ). It is an interesting trade-off scenario.

The competitive exclusion result [Citation5, Citation22], i.e. the strain with the maximal reproduction number eliminates all the rest, has been successfully extended to a multi-strain immuno-epidemiological flu model with environmental transmission. The results show that the basic reproduction number R0 determines the transmission dynamics of HPAI diseases: the disease-free equilibrium is globally asymptotically stable if R01. If R0=Rj>1 is the maximal reproduction number, the dominant single-strain equilibrium Ej is globally asymptotically stable, and the other strains die out. The results suggest that an effective strategy to contain HPAI disease is decreasing the reproduction number R0 below one.

From the perspective of public health, public health efforts will work best if directed to monitoring the single strain j whose basic reproduction number is maximal. We can largely decrease the infected individuals by culling as culling is one of the main control measures used to decrease avian influenza in the poultry population. Then the reproduction number Rj will decrease and the decrease in the AI disease prevalence Ij can be obtained if Rj>Rj>1. But under the condition of Rj<Rj, although Rj decreases with the decrease of the viral load Vj(τ), the disease prevalence Ij increases. This may explain why AI infection is still around despite implementation of integrated control measures.

8. Discussion

In this paper, we study a multi-scale immuno-epidemiological model of influenza viruses including direct and environmental transmission. This work extends prior results in [Citation22] through the linking to within-host model and the integration of arbitrarily distributed clearance rates of the virus in the environment. Mathematically this extends the model in [Citation22] to a model with two time-since-infection structural variables in contrast to most models which only have one structural variable.

We assume that the high pathogenic avian influenza viruses of the infected poultry are obtained from the wild birds. The within-host and the between-host models are linked through the age-since-infection structure of the epidemiological variables. In addition, rates of between-host transmission, shedding by individuals infected with strain j, and disease-induced death are dependent on the within-host viral load. The immunological and epidemiological reproduction number (Rj,Rj), respectively, are then computed. We show that if all epidemiological reproduction numbers are lesser or equal to one, the disease-free equilibrium in the system is locally and globally asymptotically stable and unstable if at least one of them is greater than one. Furthermore, the dominance equilibrium Ej of HPAI virus strain exists and is unique when the reproduction number Rj is greater than one. Without loss of generality, we assume that R1>1 is the maximal reproduction number and all of the reproduction numbers are distinct, and then we prove that the dominance equilibrium E1 is globally asymptotically stable. In brief, under the assumption of the absence of super-infection and co-infection, on the within-host scale, the n strains eliminate each other with the strain with the largest immunological reproduction persisting. However, on the population scale, we extend the competitive exclusion principle to a multi-strain model of SI-type for the dynamics of highly pathogenic flu in poultry that incorporates both the infection age of infectious individuals and biological age of pathogen in the environment. We note that in the presence of super infection the complete competitive exclusion result that we obtain here may not hold.

In this study, we also address the question: How does the viral load of the infected within-host individuals affect the epidemic reproduction number and the disease prevalence? These are important questions in terms of providing insight on disease control and understanding characteristic of the disease. Calculations suggest that increasing the viral load Vj(τ) increases the transmission coefficient βj(τ) and the reproduction number Rj. If Rj>Rj>1, the disease prevalence Ij will increase with the increase of the viral load Vj(τ). But if Rj>Rj, the disease prevalence Ij will decrease with the increase of the viral load Vj(τ). The increase of prevalence with decreasing viral load can be observed in the use of HIV medications in practice. In addition, the reproduction number Rj decreases with the clearance rate δj(θ) of the virus strain j of age θ from the environment.

Under the assumptions that have been considered by us, our results suggest that the AI infection can be controlled well at the beginning of the disease if the infected poultry with HPAI virus who has already begun to show symptoms are all slaughtered in a timely fashion and also their carcasses are handled appropriately. It is important to keep the stall clean all the time and that helps in preventing the disease from spreading to some degree.

The primary risk factor for human infection appears to be a direct or an indirect exposure to the infected live or dead poultry or contaminated environments. The control of circulation of the H5N1 and H7N9 viruses in poultry is essential to reduce the overall risk to human infection.

Acknowledgments

We are very grateful to two anonymous referees for their careful reading, valuable comments and helpful suggestions, which have helped us to improve the presentation of this work significantly.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Supported partially by the NNSF of China [11271314] and Plan for Scientific Innovation Talent of Henan Province [144200510021] and the NSF of Henan Province [142300410350]. M. Martcheva is supported partially through grants [NSF DMS-1220342] and [DMS-1515661].

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