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Articles

Global dynamics of a tuberculosis model with fast and slow progression and age-dependent latency and infection

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Pages 675-705 | Received 12 Jan 2018, Accepted 14 Oct 2019, Published online: 01 Nov 2019

ABSTRACT

In this paper, a mathematical model describing tuberculosis transmission with fast and slow progression and age-dependent latency and infection is investigated. It is assumed in the model that infected individuals can develop tuberculosis by either of two pathogenic mechanisms: direct progression or endogenous reactivation. It is shown that the transmission dynamics of the disease is fully determined by the basic reproduction number. By analyzing corresponding characteristic equations, the local stability of a disease-free steady state and an endemic steady state of the model is established. By using the persistence theory for infinite dimensional system, it is proved that the system is uniformly persistent when the basic reproduction number is greater than unity. By constructing suitable Lyapunov functionals and using LaSalle's invariance principle, it is verified that the global dynamics of the system is completely determined by the basic reproduction number.

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1. Introduction

Tuberculosis (TB) is a bacterial disease caused by Mycobacterium tuberculosis, and is usually acquired through airborne infection from active TB cases [Citation4,Citation9]. According to the World Health Organization, one third of the world's population is infected with tuberculosis either latently or actively. Despite effective antimicrobial chemotherapy, tuberculosis infection remains a leading cause of death from an infectious disease [Citation34].

Mathematical modelling has been proven to be important in better understanding the transmission dynamics of TB and evaluating the effectiveness of various control and prevention strategies (see, for example, [Citation1–3,Citation5–7,Citation10–12,Citation18–21,Citation25,Citation29,Citation30,Citation32]). Tuberculosis is most commonly transmitted from a person suffering from infectious (active) tuberculosis to other persons by infected droplets created when the person with active TB coughs or sneezes. Among generally healthy persons, infection with TB is highly likely to be asymptomatic [Citation10]. In [Citation6], Blower et al. formulated and analysed mathematical models describing the transmission dynamics of untreated tuberculosis epidemics. It was assumed in [Citation6] that infected individuals remain noninfectious until they develop disease by one of two pathogenic mechanisms: direct progression or endogenous reactivation. Two types of tuberculosis contribute to the incidence rate of tuberculosis disease: one type of tuberculosis develops through direct progression soon after infection (fast progression) and the other type of tuberculosis develops through endogenous reactivation in the latently infected individuals (slow progression). In order to assess the intrinsic transmission dynamics of tuberculosis, Blower et al. considered the following mathematical model [Citation6]: (1) S˙(t)=AμS(t)βS(t)I(t),L˙(t)=(1p)βS(t)I(t)νL(t)μL(t),I˙(t)=pβS(t)I(t)+νL(t)(μ+μT)I(t).(1) In (Equation1), S(t) represents the number of individuals who are not previously exposed to tuberculosis at time t, L(t) represents the number of latently infected individuals who have been infected with Mycobacterium tuberculosis at time t, but have no clinical illness and remain noninfectious, I(t) represents the number of active infectious tuberculosis cases at time t. The assumptions of model (Equation1) were made as follows [Citation6].

  1. Recruitment to the susceptible population occurs at a constant rate A. The incidence rate of infection is formulated as the product of the number of susceptible population at time t and the per capita force of infection at time t, where the per capita infection force is defined as the per-susceptible risk of becoming infected with Mycobacterium tuberculosis, and is calculated as the product of the number of infectious cases at time t and the transmission coefficient (β) of the pathogen. Where β represents the likelihood that an infectious case will successfully transmit the infection to a susceptible individual.

  2. The two pathogenous mechanisms are assumed by allowing a proportion (p) of the newly infected to develop tuberculosis directly and a proportion (1−p) of the newly infected to enter the latent class. Latently infected individuals either develop tuberculosis slowly at an average rate ν or die at a natural death rate μ before developing tuberculosis.

  3. Active infectious tuberculosis die, either because of tuberculosis at average rate μT, or because of natural death at an average rate μ.

The qualitative dynamics of model (Equation1) are governed by the basic reproduction number: (2) R0=pAβμ(μ+μT)+(1p)Aβνμ(μ+μT)(μ+ν).(2) Where the quantity R0 represents the average number of secondary infectious cases produced by a typical infectious individual in a susceptible population at a demographic steady state. The first term in (Equation2) gives the new cases resulting from fast progression while the second term represents the cases resulting from slow progression.

We note that system (Equation1) was formulated as ordinary differential equations with distinct variables describing the population size of compartments such as susceptible, latently infected and active infectious. It is assumed that all individuals within a compartment behave identically, regardless of how much time they have spent in the compartment. However, an infected individuals may have latent tuberculosis for months, years or even decades before the disease becomes infectious. The risk per unit time of activation appears to be higher in the early stages of latency than in later stages [Citation6]. On the other hand, laboratory studies suggest that the infectivity of infectious individuals be different at the differential age of infection [Citation39]. For tuberculosis infection, the TB bacteria need to develop in the lung to be transmissible through coughing, and their transmissibility depends on their progression in the lung as well as the strength of a host's immune system. Active TB has the highest possibility of developing within the first 2–5 years of infection, while most TB infections remain latent for a long period of time until immune compromise occurs due to aging or co-infection with other illnesses such as HIV (see, for example, [Citation14,Citation25]). In [Citation28], by including the duration that an individual has spent in the exposed compartment and in the infectious compartment as variables, McCluskey presented and studied an SEI epidemiological model with continuous age-structure in the exposed and infectious classes. A Lyapunov functional was used in [Citation28] to show that the endemic steady state is globally stable. Recently, much attention has been paid to the modelling and analysis on infectious disease dynamics with age dependence (see, for example, [Citation8,Citation13,Citation15–17,Citation27,Citation36]).

Motivated by the works of Blower et al. [Citation6] and McCluskey [Citation28], in the present paper, we are concerned with the effect of age structure for both latently infected and active infectious individuals on the transmission dynamics of untreated tuberculosis epidemics with fast and slow progression. To this end, we consider the following differential equation system: (3) S˙(t)=AμSS(t)S(t)0β(a)i(a,t)da,e(θ,t)t+e(θ,t)θ=(μ(θ)+ε(θ))e(θ,t),θ>0,i(a,t)t+i(a,t)a=ν(a)i(a,t),a>0(3) with boundary conditions (4) e(0,t)=(1p)S(t)0β(a)i(a,t)da,i(0,t)=pS(t)0β(a)i(a,t)da+0ε(θ)e(θ,t)dθ,(4) and initial condition (5) X0:=(S(0),e(,0),i(,0))=(S0,e0(),i0())X,(5) where X=R+×L+1(0,)×L+1(0,), L+1(0,) is the set of all integrable functions from (0,) into R+=[0,).

In system (Equation3), S(t) represents the number of individuals who are susceptible to tuberculosis at time t; e(θ,t) represents the density of individuals who have been in the latently infected class for duration θ at time t, but have no clinical illness and remain noninfectious; i(a,t) represents the density of individuals who have been in the active infectious class for duration a at time t. The definitions of all parameters and symbols in system (Equation3) are listed in Table .

Table 1. Definitions of the parameters and symbols in the system (Equation3).

In the sequel, we further make the following assumptions.

  1. β and ϵ are Lipschitz continuous on R+ with Lipschitz coefficients Lβ and Lε, respectively.

  2. β,ε,μ,νL+(0,) with essential upper bounds β¯,ε¯,μ¯ and ν¯, respectively.

  3. There exists μ0(0,μS] such that μ(a)μ0 for a0 and ν(θ)μ0 for θ0.

  4. There exist positive constants aβ and aε such that β(a) is positive in a neighbourhood of aβ and ε(a) is positive in a neighbourhood of aε.

Using the theory of age-structured dynamical systems developed in [Citation24,Citation35], we can show that system (Equation3) has a unique solution (S(t),e(,t),i(,t)) satisfying the boundary conditions (Equation4) and the initial condition (Equation5). Moreover, it is easy to show that all solutions of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5) are defined on [0,+) and remain positive for all t0. Furthermore, X is positively invariant and system (Equation3) exhibits a continuous semi-flow Φ:R+×XX, namely, (6) Φt(X0)=Φ(t,X0):=(S(t),e(,t),i(,t)),t0,X0X.(6)

Given a point (x,φ,ψ)X, we have the norm (x,φ,ψ)X:=x+0φ(a)da+0ψ(θ)dθ.

The primary goal of the present work is to carry out a complete mathematical analysis of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5), and establish its global dynamics. The organization of this paper is as follows. In the next section, we show the asymptotic smoothness of the semi-flow generated by system (Equation3). In Section 3, we calculate the basic reproduction number and discuss the existence of feasible steady states of system (Equation3). In Section 4, by analyzing corresponding characteristic equations, we establish the local asymptotic stability of a disease-free steady state and an endemic steady state of system (Equation3). In Section 5, by means of the persistence theory for infinite dimensional system, we show that system (Equation3) is uniformly persistent when the basic reproduction number is greater than unity. In Section 6, by constructing suitable Lyapunov functionals and using LaSalle's invariance principle, we study the global stability of each of feasible steady states of system (Equation3) with the boundary conditions (Equation4). In Section 7, numerical simulations are carried out to illustrate the feasibility of theoretical results. A brief discussion is given in Section 8 to conclude this work.

2. Asymptotic smoothness

In order to address the global dynamics of system (Equation3), in this section, we are concerned with the asymptotic smoothness of the semi-flow {Φ(t)}t0 generated by system (Equation3).

2.1. Boundedness of solutions

In this subsection, we study the boundedness of solutions of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5).

Proposition 2.1

Let Φt be defined as in (Equation6). Then the following statements hold true.

  1. (d/dt)Φt(X0)XAμ0Φt(X0)X for all t0;

  2. Φt(X0)Xmax{A/μ0,X0X} for all t0;

  3. lim supt+Φt(X0)XA/μ0;

  4. Φt is point dissipative: there is a bounded set that attracts all points in X.

Proof.

Let Φt(X0)=Φ(t,X0):=(S(t),e(,t),i(,t)) be any nonnegative solution of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5). Denote X0X=S0+0e0(θ)dθ+0i0(a)da and Φ(t,X0)X=S(t)+0e(θ,t)dθ+0i(a,t)da. We derive from system (Equation3) that (7) ddtΦ(t,X0)X=AμSS(t)S(t)0β(a)i(a,t)da+0e(θ,t)tdθ+0i(a,t)tda.(7) On substituting e(θ,t)/t+e(θ,t)/θ=(μ(θ)+ε(θ))e(θ,t),i(a,t)/t+i(a,t)/a=ν(a)i(a,t) into (Equation7), it follows that (8) ddtΦ(t,X0)X=AμSS(t)S(t)0β(a)i(a,t)da0e(θ,t)θdθ0(μ(θ)+ε(θ))e(θ,t)dθ0i(a,t)ada0ν(a)i(a,t)da=AμSS(t)S(t)0β(a)i(a,t)dae(θ,t)|00(μ(θ)+ε(θ))e(θ,t)dθi(a,t)|00ν(a)i(a,t)da.(8) We have from (Equation4) and (Equation8) that ddtΦ(t,X0)XAμSS(t)0μ(θ)e(θ,t)dθ0ν(a)i(a,t)daAμ0Φ(t,X0)X. The variation of constants formula implies Φ(t,X0)XAμ0eμ0t{Aμ0X0X}, which yields Φ(t,X0)Xmax{A/μ0,X0X} for all t0. This completes the proof.

The following results are direct consequences of Proposition 2.1.

Proposition 2.2

If X0X and X0XK for some KA/μ0, then S(t)K,0e(θ,t)dθK,0i(a,t)daK, for all t0.

Proposition 2.3

Let CX be bounded. Then

  1. Φt(C) is bounded for all tR+;

  2. Φt is eventually bounded on C.

2.2. Asymptotic smoothness

In order to investigate the global dynamics of system (Equation3), in this subsection, we show the asymptotic smoothness of the semi-flow {Φ(t)}t0.

Let (S(t),e(,t),i(,t)) be a solution of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5). Integrating the second and the third equations of system (Equation3) along the characteristic line ta=const., respectively, one has (9) e(θ,t)={L1(tθ)ϕ1(θ),0θ<t,e0(θt)ϕ1(θ)ϕ1(θt),0tθ,(9) and (10) i(a,t)={L2(ta)ϕ2(a),0a<t,i0(at)ϕ2(a)ϕ2(at),0ta,(10) where (11) ϕ1(θ)=e0θ(μ(s)+ε(s))ds,ϕ2(a)=e0aν(s)ds,(11) and (12) L1(t)=(1p)S(t)A1(t),L2(t)=pS(t)A1(t)+A2(t),(12) in which A1(t)=0β(a)i(a,t)da,A2(t)=0ε(θ)e(θ,t)dθ.

Proposition 2.4

The functions A1(t) and A2(t) are Lipschitz continuous on R+.

Proof.

Let Kmax{A/μ0,X0X}. By Proposition 2.1 we have ΦtXK for all t0.

Fix t0 and h>0. Then (13) A1(t+h)A1(t)=0β(a)i(a,t+h)da0β(a)i(a,t)da=0hβ(a)i(a,t+h)da+hβ(a)i(a,t+h)da0β(a)i(a,t)da.(13) On substituting (Equation10) into (Equation13), it follows that (14) A1(t+h)A1(t)=0hβ(a)L2(t+ha)ϕ2(a)da+hβ(a)i(a,t+h)da0β(a)i(a,t)da.(14) By Proposition 2.2, we have that L2(t)(pβ¯K+ε¯)K. Noting that ϕ2(a)1, it follows from (Equation14) that (15) |A1(t+h)A1(t)|(pβ¯K+ε¯)Kh+|hβ(a)i(a,t+h)da0β(a)i(a,t)da|=(pβ¯K+ε¯)Kh+|0β(σ+h)i(σ+h,t+h)dσ0β(a)i(a,t)da|.(15) From (Equation10) we have (16) i(a+h,t+h)=i(a,t)ϕ2(a+h)ϕ2(a)=i(a,t)eaa+hν(s)ds,(16) for all a0,t0,h0. Hence, (Equation15) can be rewritten as (17) |A1(t+h)A1(t)|(pβ¯K+ε¯)Kh+|0β(a+h)i(a,t)eaa+hν(s)dsda0β(a)i(a,t)da|(pβ¯K+ε¯)Kh+0β(a+h)(1eaa+hν(s)ds)i(a,t)da+0|β(a+h)β(a)|i(a,t)da.(17) Noting that 1exx for x0, it follows from (Equation17) that |A1(t+h)A1(t)|K(pβ¯K+ε¯+β¯ν¯+Lβ)h, here the fact that β(a) is Lipschitz continuous on R+ was used.

In a similar way, we have |A2(t+h)A2(t)|K[(1p)β¯K+β¯(μ¯+ε¯)+Lε]h. This completes the proof.

Proposition 2.5

The functions L1(t) and L2(t) are Lipschitz continuous on R+.

Proof.

Let Kmax{A/μ0,X0X}. By Proposition 2.1 we have ΦtXK for all t0. Fix t0 and h>0. Then |L1(t+h)L1(t)|(1p)|S(t+h)A1(t+h)S(t)A1(t)|(1p)[A1(t+h)|S(t+h)S(t)|+S(t)|A1(t+h)A1(t)|]ML1h, where ML1=(1p)β¯K(A+μSK+β¯K2)+(1p)K2(pβ¯K+ε¯+β¯ν¯+Lβ). In a similar way, one has |L2(t+h)L2(t)|ML2h, where ML2=pβ¯K(A+μSK+β¯K2)+pK2(pβ¯K+ε¯+β¯ν¯+Lβ)+K[(1p)β¯K+β¯(μ¯+ε¯)+Lε]. This completes the proof.

In order to prove the asymptotic smoothness of the semi-flow Φ generated by system (Equation3), we introduce the following theorems (Theorems 2.46 and B.2 in [Citation31]).

Theorem 2.1

The semi-flow Φ:R+×X+X+ is asymptotically smooth if there are maps Θ,Ψ:R+×X+X+ such that Φ(t,x)=Θ(t,x)+Ψ(t,x) and the following hold for any bounded closed set CX+ that is forward invariant under Φ:

  1. limt+diamΘ(t,C)=0;

  2. there exists tC0 such that Ψ(t,C) has compact closure for each ttC.

Theorem 2.2

Let C be a subset of L1(R+). Then C has compact closure if and only if the following assumptions hold:

  1. supfC0|f(a)|da<;

  2. limrr|f(a)|da=0 uniformly in fC;

  3. limh0+0|f(a+h)f(a)|da=0 uniformly in fC;

  4. limh0+0h|f(a)|da=0 uniformly in fC.

We are now in a position to state and prove a result on the asymptotic smoothness of the semi-flow {Φ(t)}t0 generated by system (Equation3).

Theorem 2.3

The semi-flow Φ generated by system (Equation3) is asymptotically smooth.

Proof.

To verify the conditions (1) and (2) in Theorem 2.1, we first decompose the semi-flow Φ into two parts: for t0, let Ψ(t,X0):=(S(t),e~(,t),i~(,t)),Θ(t,X0):=(0,ϕ~e(,t),ϕ~i(,t)), where e~(θ,t)={L1(tθ)ϕ1(θ),0θt,0,0t<θ,ϕ~e(θ,t)={0,0θt,e0(θt)ϕ1(θ)ϕ1(θt),0t<θ,i~(a,t)={L2(ta)ϕ2(a),0at,0,0t<a,ϕ~i(a,t)={0,0at,i0(at)ϕ2(a)ϕ2(at),0t<a. Clearly, we have Φ=Θ+Ψ for t0.

Let C be a bounded subset of X and K>A/μ0 the bound for C. Let Φ(t,X0)=(S(t),e(,t),i(,t)), where X0=(S0,e0(),i0())C. Then (18) ϕ~e(,t)L1=0|ϕ~e(θ,t)|dθ=te0(θt)ϕ1(θ)ϕ1(θt)dθ.(18) Letting θt=σ, it follows from (Equation18) that ϕ~e(,t)L1=0e0(σ)ϕ1(σ+t)ϕ1(σ)dσ=0e0(σ)eσσ+t(μ(s)+ε(s))dsdσKeμ0t, which yields limt+ϕ~e(,t)L1=0. In a similar way, we can show that ϕ~i(,t)L1Keμ0t and hence limt+ϕ~i(,t)L1=0. Accordingly, Θ(t,X0) approaches 0X with exponential decay and hence, limt+diam Θ(t,C)=0 and the assumption (1) in Theorem 2.1 holds true.

In the following we show that Ψ(t,C) has compact closure for each ttC by verifying the assumptions (i)–(iv) of Theorem 2.2.

From Proposition 2.2 we see that S(t) remains in the compact set [0,K]. Next, we show that e~(θ,t) and i~(a,t) remain in a pre-compact subset of L+1 independent of X0.

It is easy to show that e~(θ,t)L¯1eμ0θ,i~(a,t)L¯2eμ0a, where (19) L¯1=(1p)β¯K2,L¯2=pβ¯K2+ε¯K.(19) Therefore, the assumptions (i),(ii) and (iv) of Theorem 2.2 follow directly. We need only to verify that (iii) of Theorem 2.2 holds. Since we are concerned with the limit as h0, we assume that h(0,t). In this case, we have (20) 0|e~(θ+h,t)e~(θ,t)|dθ=0th|L1(tθh)ϕ1(θ+h)L1(tθ)ϕ1(θ)|dθ+thtL1(tθ)ϕ1(θ)dθ0thL1(tθh)|ϕ1(θ+h)ϕ1(θ)|dθ+0th|L1(θth)L1(θt)|ϕ1(θ)dθ+thtL1(tθ)ϕ1(θ)dθ.(20) It follows from (Equation19) and (Equation20) that 0|e~(θ+h,t)e~(θ,t)|dθL¯10thϕ1(θ)(1eθθ+h(μ(s)+ε(s))ds)dθ+ML1h0thϕ1(θ)dθ+L¯1thtϕ1(θ)dθL¯10thϕ1(θ)θθ+h(μ(s)+ε(s))dsdθ+ML1h+L¯1h[(μ¯+ε¯)L¯1+ML1+L¯1]h. In a similar way, we have 0|i~(a+h,t)i~(a,t)|da(ν¯L¯2+ML2+L¯2)h. Hence, the condition (iii) of Theorem 2.2 holds. By Theorem 2.1, the asymptotic smoothness of the semi-flow Φ generated by system (Equation3) follows. This completes the proof.

The following result is immediate from Theorem 2.33 in [Citation31] and Theorem 2.3.

Theorem 2.4

There exists a global attractor A of bounded sets in X.

3. Steady states and basic reproduction number

In this section, we are concerned with the existence of feasible steady states of system (Equation3) with the boundary conditions (Equation4).

Clearly, system (Equation3) always has a disease-free steady state E1(A/μS,0,0). If system (Equation3) with the boundary conditions (Equation4) has an endemic steady state (S,e(θ), i(a)), then it must satisfy the following equations: (21) AμSSS0β(a)i(a)da=0,de(θ)dθ=(μ(θ)+ε(θ))e(θ),di(a)da=ν(a)i(a),e(0)=(1p)S0β(a)i(a)da,i(0)=pS0β(a)i(a)da+0ε(θ)e(θ)dθ.(21) We obtain from the second and the third equations of (Equation21) and (Equation11) that (22) e(θ)=e(0)ϕ1(θ),(22) and (23) i(a)=i(0)ϕ2(a).(23) It follows from the fourth and the fifth equations of (Equation21), (Equation22) and (Equation23) that (24) S=10β(a)ϕ2(a)da[p+(1p)0ε(θ)ϕ1(θ)dθ].(24) On substituting (Equation24) into the first equation of (Equation21), we have (25) i(0)=μS0β(a)ϕ2(a)da(R01),(25) where R0:=AμS0β(a)ϕ2(a)da[p+(1p)0ε(θ)ϕ1(θ)dθ].R0 is called the basic reproduction number representing the average number of new infections generated by a single newly infectious individual during the full infectious period [Citation33].

It follows from the fourth equation of (Equation21), (Equation23) and (Equation25) that e(0)=(1p)i(0)p+(1p)0ε(θ)ϕ1(θ)dθ. In conclusion, if R0>1, in addition to the disease-free steady state E1(A/μS,0,0), system (Equation3) with the boundary conditions (Equation4) has a unique endemic steady state E(S,e(θ),i(a)), where S=10β(a)ϕ2(a)da[p+(1p)0ε(θ)ϕ1(θ)dθ],e(θ)=μS(1p)ϕ1(θ)0β(a)ϕ2(a)da[p+(1p)0ε(θ)ϕ1(θ)dθ](R01),i(a)=μSϕ2(a)0β(a)ϕ2(a)da(R01).

4. Local stability

In this section, we study the local stability of the disease-free and endemic steady states of system (Equation3) wih the boundary conditions (Equation4).

We first consider the local stability of the disease-free steady state E1(A/μS,0,0) of system (Equation3). Let S(t)=x1(t)+A/μS,e(θ,t)=y1(θ,t),i(a,t)=z1(a,t). Linearizing system (Equation3) at the steady state E1, we obtain from (Equation3) and (Equation4) that (26) x˙1(t)=μSx1(t)AμS0β(a)z1(a,t)da,y1(θ,t)t+y1(θ,t)θ=(μ(θ)+ε(θ))y1(θ,t),z1(a,t)t+z1(a,t)a=ν(a)z1(a,t),y1(0,t)=A(1p)μS0β(a)z1(a,t)da,z1(0,t)=ApμS0β(a)z1(a,t)da+0ε(θ)y1(θ,t)dθ.(26) Looking for solutions of system (Equation26) of the form x1(t)=x11eλt,y1(θ,t)=y11(θ)eλt,z1(a,t)=z11(a)eλt, where x11,y11(θ) and z11(a) will be determined later, one obtains the following linear eigenvalue problem: (27) (λ+μS)x11=AμS0β(a)z11(a)da,y11(θ)=(λ+μ(θ)+ε(θ))y11(θ),z11(a)=(λ+ν(a))z11(a),y11(0)=A(1p)μS0β(a)z11(a)da,z11(0)=ApμS0β(a)z11(a)da+0ε(θ)y11(θ)dθ.(27) Solving the second and the third equations of system (Equation27) yields (28) y11(θ)=y11(0)e0θ(λ+μ(s)+ε(s))ds,(28) and (29) z11(a)=z11(0)e0a(λ+ν(s))ds.(29) On substituting (Equation28) into the fifth equation of system (Equation27), we have that (30) z11(0)=ApμS0β(a)z11(a)da+y11(0)0ε(θ)e0θ(λ+μ(s)+ε(s))dsdθ.(30) It follows from (Equation30) and the fourth equation of system (Equation27) that (31) z11(0)=AμS[p+(1p)0ε(θ)e0θ(λ+μ(s)+ε(s))dsdθ]0β(a)z11(a)da.(31) On substituting (Equation29) into (Equation31), we obtain the characteristic equation of system (Equation3) at the steady state E1 of the form: (32) f(λ)=1,(32) where f(λ):=AμS[p+(1p)0ε(θ)e0θ(λ+μ(s)+ε(s))dsdθ]0β(a)e0a(λ+ν(s))dsda. Clearly, f(0)=R0. It is easy to show that f(λ)<0 and limλ+f(λ)=0. Hence, f(λ) is a decreasing function. Therefore, if R0>1, Equation (Equation32) has a unique positive root. Accordingly, if R0>1, the steady state E1 is unstable.

In the following, we claim that if R0<1, the steady state E1 is locally asymptotically stable. Otherwise, Equation (Equation32) has at least one root λ1=a1+ib1 satisfying a10. In this case, we have that |f(λ1)|AμS[p+(1p)0ε(θ)e0θ(μ(s)+ε(s))dsdθ]0β(a)e0aν(s)dsda=R0<1, a contradiction. Therefore, if R0<1, all roots of Equation (Equation32) have negative real parts. Accordingly, the steady state E1 is locally asymptotically stable if R0<1.

We are now in a position to study the local stability of the endemic steady state E(S,e(θ), i(a)) of system (Equation3) with the boundary conditions (Equation4) in the case that R0>1.

Letting S(t)=x(t)+S,e(θ,t)=y(θ)+e(θ),i(a,t)=z(a,t)+i(a), and linearizing system (Equation3) at the steady state E, we have that (33) x˙(t)=(μS+0β(a)i(a)da)x(t)S0β(a)z(a,t)da,y(θ,t)t+y(θ,t)θ=(μ(θ)+ε(θ))y(θ,t),z(a,t)t+z(a,t)a=ν(a)z(a,t),y(0,t)=(1p)x(t)0β(a)i(a)da+(1p)S0β(a)z(a,t)da,z(0,t)=px(t)0β(a)i(a)da+pS0β(a)z(a,t)da+0ε(θ)y(θ,t)dθ.(33) Looking for solutions of system (Equation33) of the form x(t)=x2eλt,y(θ,t)=y2(θ)eλt,z(a,t)=z2(a)eλt, where x2,y2(θ) and z2(a) will be determined later, we obtain the following linear eigenvalue problem: (34) (λ+μS+0β(a)i(a)da)x2=S0β(a)z2(a)da,y2(θ)=(λ+μ(θ)+ε(θ))y2(θ),z2(a)=(λ+ν(a))z2(a),y2(0)=(1p)x20β(a)i(a)da+(1p)S0β(a)z2(a)da,z2(0)=px20β(a)i(a)da+pS0β(a)z2(a)da+0ε(θ)y2(θ)dθ.(34) Solving the second and the third equations of system (Equation34) yields (35) y2(θ)=y2(0)e0θ(λ+μ(s)+ε(s))ds,(35) and (36) z2(a)=z2(0)e0a(λ+ν(s))ds.(36) It follows from the first equation of system (Equation34) that (37) x2=S0β(a)z2(a)daλ+μS+0β(a)i(a)da.(37) On substituting (Equation37) into the fourth and the fifth equations of system (Equation34), we have that (38) y2(0)=(1p)S(λ+μS)λ+μS+0β(a)i(a)da0β(a)z2(a)da,(38) and (39) z2(0)=pS(λ+μS)λ+μS+0β(a)i(a)da0β(a)z2(a)da+y2(0)0ε(θ)ϕ1(θ)dθ.(39) It follows from (Equation38) and (Equation39) that (40) z2(0)=S[p+(1p)0ε(θ)ϕ1(θ)dθ](λ+μS)λ+μS+0β(a)i(a)da0β(a)z2(a)da.(40) On substituting (Equation36) into (Equation40), one obtains the characteristic equation of system (Equation3) at the steady state E of the form (41) f1(λ)=1,(41) where f1(λ)=S[p+(1p)0ε(θ)ϕ1(θ)dθ](λ+μS)λ+μS+0β(a)i(a)da0β(a)e0a(λ+ν(s))dsda. In the following, we verify that if R0>1, all roots of Equation (Equation41) have real negative parts. Otherwise, Equation (Equation41) has at least one root λ2=a2+b2i satisfying a20. In this case, we have (42) |f1(λ2)|=S[p+(1p)0ε(θ)ϕ1(θ)dθ]|λ2+μS||λ2+μS+0β(a)i(a)da||0β(a)e0a(λ2+ν(s))dsda|<S[p+(1p)0ε(θ)ϕ1(θ)dθ]0β(a)e0aν(s)dsda=1,(42) a contradiction. Hence, if R0>1, the steady state E of system (Equation3) is locally asymptotically stable.

From what has been discussed above, we have the following result.

Theorem 4.1

For system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5), if R0<1, the disease-free steady state E1(A/μS,0,0) is locally asymptotically stable; if R0>1, E1 is unstable and the endemic steady state E(S,e(θ),i(a)) exists and is locally asymptotically stable.

5. Uniform persistence

In this section, we establish the uniform persistence of the semi-flow {Φ(t)}t0 generated by system (Equation3) when the basic reproduction number R0>1.

Define a¯=inf{a:aβ(u)du=0},θ¯=inf{θ:θε(u)du=0}. With the Assumption (H4), we have a¯>0,θ¯>0.

Denote X=L+1(0,+)×L+1(0,+),Y~={(e(,t),i(,t))X:0θ¯e(θ,t)dθ>0or0a¯i(a,t)da>0}, and Y=R+×Y~,Y=XY,Y~=XY~. Following [Citation26], we have the following result.

Proposition 5.1

The subsets Y and Y are both positively invariant under the semi-flow {Φ(t)}t0, namely, Φ(t,Y)Y and Φ(t,Y)Y for t0.

The following result is useful in proving the uniform persistence of the semi-flow {Φ(t)}t0 generated by system (Equation3).

Theorem 5.1

The disease-free steady state E1(A/μS,0,0) is globally asymptotically stable for the semi-flow {Φ(t)}t0 restricted to Y.

Proof.

Let (S0,e0(),i0())Y. Then (e0(),i0())Y~. We consider the following system (43) e(θ,t)t+e(θ,t)θ=(μ(θ)+ε(θ))e(θ,t),i(a,t)t+i(a,t)a=ν(a)i(a,t),e(0,t)=(1p)S(t)0β(a)i(a,t)da,i(0,t)=pS(t)0β(a)i(a,t)da+0ε(θ)e(θ,t)dθ,e(θ,0)=e0(θ),i(a,0)=i0(a).(43) Since lim supt+S(t)A/μS, by comparison principle, we have (44) e(θ,t)e^(θ,t),i(a,t)i^(a,t),(44) where e^(a,t) and i^(a,t) satisfy (45) e^(θ,t)t+e^(θ,t)θ=(μ(θ)+ε(θ))e^(θ,t),i^(a,t)t+i^(a,t)a=ν(a)i^(a,t),e^(0,t)=(1p)AμS0β(a)i^(a,t)da,i^(0,t)=pAμS0β(a)i^(a,t)da+0ε(θ)e^(θ,t)dθ,e^(θ,0)=e0(θ),i^(a,0)=i0(a).(45) Solving the first and the second equations of system (Equation45), we have (46) e^(θ,t)={L^1(tθ)ϕ1(θ),0θ<t,e0(θt)ϕ1(θ)ϕ1(θt),0tθ,(46) and (47) i^(a,t)={L^2(ta)ϕ2(a),0a<t,i0(at)ϕ2(a)ϕ2(at),0ta,(47) where (48) L^1(t)=e^(0,t)=(1p)AμS0β(a)i^(a,t)da,(48) and (49) L^2(t)=i^(0,t)=pAμS0β(a)i^(a,t)da+0ε(θ)e^(θ,t)dθ.(49) On substituting (Equation46) and (Equation47) into the third and the fourth equations of system (Equation45), we obtain that (50) L^1(t)=(1p)AμS0tβ(a)L^2(ta)ϕ2(a)da+G1(t),L^2(t)=pAμS0tβ(a)L^2(ta)ϕ2(a)da+0tε(θ)L^1(tθ)ϕ1(θ)dθ+G2(t),G1(t)=(1p)AμStβ(a)i0(at)ϕ2(a)ϕ2(at)da,G2(t)=pAμStβ(a)i0(at)ϕ2(a)ϕ2(at)da+tε(θ)e0(θt)ϕ1(θ)ϕ1(θt)dθ.(50) Since (e0(),i0())Y, we have G1(t)0 and G2(t)0 for all t0. It therefore follows from (Equation50) that (51) L^1(t)=(1p)AμS0tβ(a)L^2(ta)ϕ2(a)da,L^2(t)=pAμS0tβ(a)L^2(ta)ϕ2(a)da+0tε(θ)L^1(tθ)ϕ1(θ)dθ.(51) It is easy to show that system (Equation51) has a unique solution L^1(t)=0,L^2(t)=0.

We obtain from (Equation46) that e^(θ,t)=0 for 0θ<t. For θt, we have e^(θ,t)L1=e0(θt)ϕ1(θ)ϕ1(θt)L1eμ0te0L1, which yields limt+e^(θ,t)=0. Similarly, one has limt+i^(a,t)=0. By comparison principle, it follows that limt+e(θ,t)=0,limt+i(a,t)=0. We obtain from the first equation of system (Equation3) that limt+S(t)=A/μS. This completes the proof.

Theorem 5.2

If R0>1, then the semi-flow {Φ(t)}t0 generated by system (Equation3) is uniformly persistent with respect to the pair (Y,Y); that is, there exists an ε>0 such that the solution of system (Equation3) with initial value in Y satisfies lim inft+S(t)ε,lim inft+e(,t)Xε,lim inft+i(,t)Xε.

Proof.

Since the disease-free steady state E1(A/μS,0,0) is globally asymptotically stable in Y, applying Theorem 4.2 in [Citation23], we need only to show that Ws(E1)Y=, where Ws(E1)={xY:limt+Φ(t,x)=E1}. Otherwise, there exists a solution yY such that Φ(t,y)E1 as t. In this case, one can find a sequence {yn}Y such that Φ(t,yn)E1X<1n,t0. Denote Φ(t,yn)=(Sn(t),en(,t),in(,t)) and yn=(Sn(0),en(,0),in(,0)). Since R0>1, one can choose n sufficiently large satisfying S01/n>0 and (52) (S01n)0β(a)ϕ2(a)da[p+(1p)0ε(θ)ϕ1(θ)dθ]>1,(52) where S0=A/μS. For such an n>0, there exists a T>0 such that for t>T, (53) S01n<Sn(t)<S0+1n.(53) Consider the following auxiliary system (54) e~(θ,t)t+e~(θ,t)θ=(μ(θ)+ε(θ))e~(θ,t),i~(a,t)t+i~(a,t)a=ν(a)i~(a,t),e~(0,t)=(1p)(S01n)0β(a)i~(a,t)da,i~(0,t)=p(S01n)0β(a)i~(a,t)da+0ε(θ)e~(θ,t)dθ.(54) By direct calculation, we obtain the characteristic equation of system (Equation54) at the steady state E0(0,0) of the form (55) f0(λ)=1,(55) where f0(λ)=(S01n)0β(a)e0a(λ+ν(s))dsda×[p+(1p)0ε(θ)e0θ(λ+μ(s)+ε(s))dsdθ]. Clearly, we have limλ+f0(λ)=0 and f0(0)=(S01n)0β(a)ϕ2(a)da[p+(1p)0ε(θ)ϕ1(θ)dθ]>1. Hence, if R0>1, then Equation (Equation55) has at least one positive root λ0. This implies that the solution (e~(,t),i~(,t)) of system (Equation54) is unbounded. By comparison principle, the corresponding solution Φ(t,yn) of system (Equation3) is unbounded, which contradicts Proposition 2.2. Therefore, the semi-flow {Φ(t)}t0 generated by system (Equation3) is uniformly persistent. This completes the proof.

Furthermore, we have the following result.

Proposition 5.2

The semi-flow {Φ(t)}t0 has a compact global attractor A0Y, which attracts any bounded sets of Y+.

6. Global stability

In this section, we are concerned with the global asymptotic stability of each of feasible steady states of system (Equation3) with the boundary conditions (Equation4). The strategy of proofs is to use LaSalle's invariance principle.

We first give a result on the global stability of the disease-free steady state E1(A/μS,0,0) of system (Equation3).

Theorem 6.1

If R0<1, the disease-free steady state E1(A/μS,0,0) of system (Equation3) is globally asymptotically stable.

Proof.

Let (S(t),e(θ,t),i(a,t)) be any positive solution of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5). Denote S0=A/μS.

Define (56) V1(t)=S(t)S0S0lnS(t)S0+k10F1(θ)e(θ,t)dθ+k20F2(a)i(a,t)da,(56) where the positive constants k1 and k2 and the nonnegative kernel functions F1(θ) and F2(a) will be determined later.

Calculating the derivative of V1(t) along positive solutions of system (Equation3) with the boundary conditions (Equation4), we obtain that (57) ddtV1(t)=(1S0S(t))[AμSS(t)S(t)0β(a)i(a,t)da]+k10F1(θ)e(θ,t)tdθ+k20F2(a)i(a,t)tda.(57) On substituting A=μSS0, e(θ,t)/t=(μ(θ)+ε(θ))e(θ,t)e(θ,t)/θ and i(a,t)/t=ν(a)i(a,t)i(a,t)/a into Equation (Equation57), it follows that (58) ddtV1(t)=(1S0S(t))[μS(S(t)S0)]S(t)0β(a)i(a,t)da+S00β(a)i(a,t)dak10F1(θ)[(μ(θ)+ε(θ))e(θ,t)+e(θ,t)θ]dθk20F2(a)[ν(a)i(a,t)+i(a,t)a]da.(58) Using integration by parts, we have from (Equation58) that (59) ddtV1(t)=(1S0S(t))[μS(S(t)S0)]S(t)0β(a)i(a,t)da+S00β(a)i(a,t)dak1F1(θ)e(θ,t)|0+k10[F1(θ)(μ(θ)+ε(θ))F1(θ)]e(θ,t)dθk2F2(a)i(a,t)|0+k20[F2(a)ν(a)F2(a)]i(a,t)da.(59) We choose (60) F1(θ)=θε(s)eθs(μ(u)+ε(u))duds,F2(a)=S0aβ(s)easν(u)duds.(60) By direct calculations, one obtains that (61) F1(0)=0ε(θ)ϕ1(θ)dθ,F2(0)=S00β(a)ϕ2(a)da,F1(θ)=(μ(θ)+ε(θ))F1(θ)ε(θ),F2(a)=ν(a)F2(a)S0β(a).(61) It follows from (Equation59)–(Equation61) that (62) ddtV1(t)=(1S0S(t))[μS(S(t)S0)]S(t)0β(a)i(a,t)da+S00β(a)i(a,t)da+k1e(0,t)0ε(θ)ϕ1(θ)dθk1F1(θ)e(θ,t)|θ=k10ε(θ)e(θ,t)dθ+k2S0i(0,t)0β(a)ϕ2(a)dak2F2(a)i(a,t)|a=k2S00β(a)i(a,t)da.(62) On substituting (Equation4) into Equation (Equation62), we have that (63) ddtV1(t)=(1S0S(t))[μS(S(t)S0)]S(t)0β(a)i(a,t)da+S00β(a)i(a,t)da+k1(1p)0ε(θ)ϕ1(θ)dθS(t)0β(a)i(a,t)dak1F1(θ)e(θ,t)|θ=k10ε(θ)e(θ,t)dθ+k2S00β(a)ϕ2(a)da×[pS(t)0β(a)i(a,t)da+0ε(θ)e(θ,t)dθ]k2F2(a)i(a,t)|a=k2S00β(a)i(a,t)da.(63) Choosing (64) k1=1p+(1p)0ε(θ)ϕ1(θ)dθ,k2=1,(64) it follows from (Equation63) that (65) ddtV1(t)=μS(S(t)S0)2S(t)k1F1(θ)e(θ,t)|θ=k2F2(a)i(a,t)|a=+S00β(a)ϕ2(a)da(11R0)×(pS(t)0β(a)i(a,t)da+0ε(θ)e(θ,t)dθ).(65) Clearly, if R0<1, then V1(t)0 holds and V1(t)=0 implies that S(t)=S0,e(θ,t)=0,i(a,t)=0. Hence, the largest invariant subset of {V1(t)=0} is the singleton E10(S0,0,0). By Theorem 4.1, we see that if R0<1, the steady state E1 is locally asymptotically stable. Therefore, the global asymptotic stability of E1 follows from LaSalle's invariance principle. This completes the proof.

We are now in a position to study the global asymptotic stability of the endemic steady state E(S,e(θ),i(a)) of system (Equation3) with the boundary conditions (Equation4).

Denote D0={(S0,e0,i0)X|S00β(a)i0(a)da>0}. In order to guarantee the Lyapunov functional in proving the global stability of E to be well-defined, we make the following assumption:

  1. 0e0aν(s)dslni0(a)da<+,0e0θ(μ(s)+ε(s))dslne0(θ)dθ<+.

Lemma 6.1

If (H5) holds, then we have 0i(a)lni(a,t)i(a)da<+,0e(θ)lne(θ,t)e(θ)dθ<+.

Proof.

Let (S(t),e(θ,t),i(a,t)) be any positive solution of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5).

It follows from (Equation10) that (66) 0i(a)lni(a,t)i(a)da=i(0)0te0aν(s)ds[lnL2(ta)lni(0)]da+i(0)te0aν(s)ds×[lni0(at)+0atν(s)dslni(0)]da,(66) where L2(t) is defined in (Equation12). From Proposition 2.2 we see that L2(t) is bounded.

Letting at = u, if (H5) holds, one has te0aν(s)dslni0(at)da=0e0u+tν(s)dslni0(u)du<+. Hence, it follows from (Equation66) that 0i(a)ln(i(a,t)/i(a))da<+. In a similar way, one can show that if (H5) holds, then 0e(θ)ln(e(θ,t)/e(θ))dθ<+. This completes the proof.

Theorem 6.2

If R0>1 and (H5) hold, then the endemic steady state E(S,e(θ),i(a)) of system (Equation3) with the boundary conditions (Equation4) is globally asymptotically stable for all X0D0.

Proof.

Let (S(t),e(θ,t),i(a,t)) be any positive solution of system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5).

Define (67) V2(t)=SG(S(t)S)+c10K1(θ)e(θ)G(e(θ,t)e(θ))dθ+c20K2(a)i(a)G(i(a,t)i(a))da,(67) where the function G(x)=x1lnx for x>0, (68) K1(θ)=θε(s)eθs(μ(u)+ε(u))duds,K2(a)=Saβ(s)easν(u)duds,(68) and the positive constants c1 and c2 will be determined later. If (H5) holds, From Lemma 6.1 we see that all integrals involved in V2(t) are finite.

Calculating the derivative of V2(t) along positive solutions of system (Equation3) with the boundary conditions (Equation4), we have (69) ddtV2(t)=(1SS(t))[AμSS(t)S(t)0β(a)i(a,t)da]+c10K1(θ)(1e(θ)e(θ,t))e(θ,t)tdθ+c20K2(a)(1i(a)i(a,t))i(a,t)tda.(69) On substituting A=μSS+S0β(a)i(a)da, e(θ,t)/t=(μ(θ)+ε(θ))e(θ,t)e(θ,t)/θ and i(a,t)/t=ν(a)i(a,t)i(a,t)/a into Equation (Equation69), it follows that (70) ddtV2(t)=(1SS(t))[μS(S(t)S)+S0β(a)i(a)da]S(t)0β(a)i(a,t)da+S0β(a)i(a,t)dac10K1(θ)(1e(θ)e(θ,t))[(μ(θ)+ε(θ))e(θ,t)+e(θ,t)θ]dθc20K2(a)(1i(a)i(a,t))[ν(a)i(a,t)+i(a,t)a]da.(70) Noting that (71) de(θ)dθ=(μ(θ)+ε(θ))e(θ),(71) and (72) di(a)da=ν(a)i(a),(72) we have (73) θG(e(θ,t)e(θ))=1e(θ)(1e(θ)e(θ,t))[e(θ,t)θ+(μ(θ)+ε(θ))e(θ,t)],(73) and (74) aG(i(a,t)i(a))=1i(a)(1i(a)i(a,t))[i(a,t)a+ν(a)i(a,t)].(74) We obtain from (Equation70), (Equation73) and (Equation74) that (75) ddtV2(t)=(1SS(t))[μS(S(t)S)+S0β(a)i(a)da]S(t)0β(a)i(a,t)da+S0β(a)i(a,t)dac10K1(θ)e(θ)θG(e(θ,t)e(θ))dθc20K2(a)i(a)aG(i(a,t)i(a))da.(75) Using integration by parts, it follows from (Equation75) that (76) ddtV2(t)=(1SS(t))[μS(S(t)S)+S0β(a)i(a)da]S(t)0β(a)i(a,t)da+S0β(a)i(a,t)dac1K1(θ)e(θ)G(e(θ,t)e(θ))|0+c10G(e(θ,t)e(θ))[K1(θ)e(θ)+K1(θ)e(θ)]dθc2K2(a)i(a)G(i(a,t)i(a))|0+c20G(i(a,t)i(a))[K2(a)i(a)+K2(a)i(a)]da.(76) We derive from (Equation71), (Equation72) and (Equation76) that (77) ddtV2(t)=(1SS(t))[μS(S(t)S)+S0β(a)i(a)da]S(t)0β(a)i(a,t)da+S0β(a)i(a,t)dac1K1(θ)e(θ)G(e(θ,t)e(θ))|0+c10G(e(θ,t)e(θ))[K1(θ)(μ(θ)+ε(θ))K1(θ)]e(θ)dθc2K2(a)i(a)G(i(a,t)i(a))|0+c20G(i(a,t)i(a))[K2(a)ν(a)K2(a)]i(a)da.(77) It is easy to show that (78) K1(0)=0ε(θ)ϕ1(θ)dθ,K2(0)=S0β(a)ϕ2(a)da,K1(θ)=(μ(θ)+ε(θ))K1(θ)ε(θ),K2(a)=ν(a)K2(a)Sβ(a).(78) We obtain from (Equation77) and (Equation78) that (79) ddtV2(t)=(1SS(t))[μS(S(t)S)+S0β(a)i(a)da]S(t)0β(a)i(a,t)da+S0β(a)i(a,t)da+c10ε(θ)ϕ1(θ)dθ[e(0,t)e(0)e(0)lne(0,t)e(0)]c1K1(θ)e(θ)G(e(θ,t)e(θ))|θ=c10ε(θ)[e(θ,t)e(θ)e(θ)lne(θ,t)e(θ)]dθ+c2S0β(a)ϕ2(a)da[i(0,t)i(0)i(0)lni(0,t)i(0)]c2K2(a)i(a)G(i(a,t)i(a))|a=c2S0β(a)[i(a,t)i(a)i(a)lni(a,t)i(a)]da.(79) It follows from (Equation4) and (Equation79) that (80) ddtV2(t)(1SS(t))[μS(S(t)S)+S0β(a)i(a)da]S(t)0β(a)i(a,t)da+S0β(a)i(a,t)dac10ε(θ)ϕ1(θ)dθ[e(0)+e(0)lne(0,t)e(0)]+c1(1p)0ε(θ)ϕ1(θ)dθS(t)0β(a)i(a,t)dac10ε(θ)[e(θ,t)e(θ)e(θ)lne(θ,t)e(θ)]dθc2S0β(a)ϕ2(a)da[i(0)+i(0)lni(0,t)i(0)]+c2S0β(a)ϕ2(a)da[pS(t)0β(a)i(a,t)da+0ε(θ)e(θ,t)dθ]c2S0β(a)[i(a,t)i(a)i(a)lni(a,t)i(a)]da.(80) Choosing c1=S0β(a)ϕ2(a)da,c2=1, we have from (Equation24) and (Equation80) that (81) ddtV2(t)μS(S(t)S)2S(t)S0β(a)i(a)da(SS(t)1lnSS(t))pS20β(a)ϕ2(a)da0β(a)i(a)G(S(t)i(a,t)i(0)Si(a)i(0,t))daS0β(a)ϕ2(a)da0ε(θ)e(θ)G(e(θ,t)i(0)e(θ)i(0,t))dθD0β(a)i(a)G(S(t)i(a,t)e(0)Si(a)e(0,t))da+pS20β(a)ϕ2(a)da0β(a)i(a)(S(t)i(a,t)i(0)Si(a)i(0,t)1)da+S0β(a)ϕ2(a)da0ε(θ)e(θ)(e(θ,t)i(0)e(θ)i(0,t)1)dθ+D0β(a)i(a)(S(t)i(a,t)e(0)Si(a)e(0,t)1)da,(81) where (82) D=(1p)S20β(a)ϕ2(a)da0ε(θ)ϕ1(θ)dθ.(82) Noting that (83) i(0)=pS0β(a)i(a)da+0ε(θ)e(θ)dθ,(83) one has (84) pS0β(a)i(a)(S(t)i(a,t)i(0)Si(a)i(0,t)1)da+0ε(θ)e(θ)(e(θ,t)i(0)e(θ)i(0,t)1)dθ=0.(84) Noting that e(0)=(1p)S0β(a)i(a)da, we have that (85) 0β(a)i(a)(S(t)i(a,t)e(0)Si(a)e(0,t)1)da=0.(85) It therefore follows from (Equation81), (Equation84) and (Equation85) that (86) ddtV2(t)μS(S(t)S)2S(t)S0β(a)i(a)daG(SS(t))pS20β(a)ϕ2(a)da0β(a)i(a)G(S(t)i(a,t)i(0)Si(a)i(0,t))daS0β(a)ϕ2(a)da0ε(θ)e(θ)G(e(θ,t)i(0)e(θ)i(0,t))dθ(1p)S20β(a)ϕ2(a)da0ε(θ)ϕ1(θ)dθ×0β(a)i(a)G(S(t)i(a,t)e(0)Si(a)e(0,t))da.(86) Since the function G(x)=x1lnx0 for all x>0 and G(x)=0 holds iff x = 1. Hence, V2(t)0 holds if R0>1. It is readily seen from (Equation86) that V2(t)=0 if and only if (87) S(t)=S,S(t)i(a,t)i(0)Si(a)i(0,t)=1,e(θ,t)i(0)e(θ)i(0,t)=1,S(t)i(a,t)e(0)Si(a)e(0,t)=1,(87) for all θ0,a0. It can be verified that the largest invariant subset of {V2(t)=0} is the singleton E. By Theorem 4.1, if R0>1, E is locally asymptotically stable. Therefore, using LaSalle's invariance principle, we see that if R0>1, the global asymptotic stability of E follows. This completes the proof.

7. Numerical simulation

In this section, we give some numerical simulations to illustrate the theoretical results in Sections 2 and 3. The backward Euler and linearized finite difference method will be used to discretize the ODEs and PDE in system (Equation3), and the integral will be numerically calculated using Simpson's rule.

The transmission coefficient of active infectious individuals at age of infection a is chosen as: (88) β(a)={3×107person1year1,a<1,1.5×107person1year1,a1.(88) The other parameters in system (Equation3) are chosen as follows: (89) A=2×105peopleyear1,μS=1/70year1,ν(a)=0.1612year1,p=0.05,μ(θ)=1/70year1,ε(θ)=0.00256year1.(89) The initial condition is chosen as S(0)=1.4×107,e0(θ)=eθ,i0(a)=ea.

By calculation, we obtain the basic reproduction number R0=2.4827. It is easy to see that in addition to the infection-free steady state E1(1.4×107,0,0), system (Equation3) has an endemic steady state E(5.6389×106,2.0014×104ϕ2(a),1.1347×105ϕ1(θ)) which is locally asymptotically stable. Numerical simulation illustrates this fact (see Figure ) (we plotted the i(a,t) component only, denote I(t)=075i(a,t)da).

Figure 1. The temporal solution found by numerical integration of system (Equation3) with the boundary conditions (Equation4) and the initial condition S(0)=1.4×107,e0(θ)=eθ,i0(a)=ea, and the parameters given in (Equation88) and (Equation89).

Figure 1. The temporal solution found by numerical integration of system (Equation3(3) S˙(t)=A−μSS(t)−S(t)∫0∞β(a)i(a,t)da,∂e(θ,t)∂t+∂e(θ,t)∂θ=−(μ(θ)+ε(θ))e(θ,t),θ>0,∂i(a,t)∂t+∂i(a,t)∂a=−ν(a)i(a,t),a>0(3) ) with the boundary conditions (Equation4(4) e(0,t)=(1−p)S(t)∫0∞β(a)i(a,t)da,i(0,t)=pS(t)∫0∞β(a)i(a,t)da+∫0∞ε(θ)e(θ,t)dθ,(4) ) and the initial condition S(0)=1.4×107,e0(θ)=e−θ,i0(a)=e−a, and the parameters given in (Equation88(88) β(a)={3×10−7person−1year−1,a<1,1.5×10−7person−1year−1,a≥1.(88) ) and (Equation89(89) A=2×105peopleyear−1,μS=1/70year−1,ν(a)=0.1612year−1,p=0.05,μ(θ)=1/70year−1,ε(θ)=0.00256year−1.(89) ).

8. Discussion

In this work, a mathematical model describing tuberculosis transmission with fast and slow progression and age-dependent latency and infection was investigated. By calculations, the basic reproduction number was obtained. It has been shown that the global dynamics of system (Equation3) with boundary conditions (Equation4) and initial condition (Equation5) is completely determined by the basic reproduction number. By constructing suitable Lyapunov functionals and using LaSalle's invariance principle, it has been verified that if the basic reproduction number is less than unity, the disease-free steady state is globally asymptotically stable and the disease dies out; and if the basic reproduction number is greater than unity, the endemic steady state is globally asymptotically stable and the disease persists. The global stability of the endemic steady state rules out any possibility for the existence of Hopf bifurcations and sustained oscillations in system (Equation3) with boundary conditions (Equation4). A similar dynamic behavior has been observed in [Citation36,Citation37].

In the following, we present some special cases of system (Equation3) with the boundary conditions (Equation4).

Example 8.1

When p = 0, then the boundary conditions (Equation4) reduce to the following: (90) e(0,t)=S(t)0β(a)i(a,t)da,i(0,t)=0ε(θ)e(θ,t)dθ.(90) The global dynamics of system (Equation3) with the boundary conditions (Equation90) has been established in [Citation28] by constructing suitable Lyapunov functionals and using LaSalle's invariance principle. It has been shown from Theorems 7.1 and 9.5 in [Citation28] that system (Equation3) with the boundary conditions (Equation90) admits the same global dynamics as system (Equation3) with the boundary conditions (Equation4).

Example 8.2

In system (Equation3), we suppose (91) μS=μ,β(a)β,μ(θ)μ,ε(θ)ν,ν(a)μ+μT,(91) where β,μ,ν and μT are positive constants. Let L(t)=0e(θ,t)dθ,I(t)=0i(a,t)da.

Solving the second equation of system (Equation3) with the boundary condition (Equation4) and the initial condition (Equation5) yields (92) e(θ,t)={e(0,tθ)e0θ(μ+ν)ds,0θt,e0(θt)eθtθ(μ+ν)ds,0tθ.(92) When t is sufficiently large (being greater than all possible infection ages), we have (93) e(θ,t)=e(0,tθ)e0θ(μ+ν)ds=e(0,tθ)e(μ+ν)θ.(93) It therefore follows from (Equation3), (Equation90) and (Equation93) that (94) ddtL(t)=0e(θ,t)tdθ=0e(θ,t)θdθ0(μ+ν)e(θ,t)dθ=e(θ,t)|0(μ+ν)0e(θ,t)dθ=(1p)S(t)0βi(a,t)da(μ+ν)0e(θ,t)dθ=(1p)βS(t)I(t)(μ+ν)L(t).(94) Similarly, we have (95) ddtI(t)=pβS(t)I(t)+νL(t)(μ+μT)I(t).(95) Hence, system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5) reduces to system (Equation1). The global dynamics of system (Equation1) was investigated in [Citation22] by means of Lyapunov functions and LaSalle's invariance principle. It has been verified in [Citation22] that system (Equation1) has the same global dynamics as system (Equation3) with the boundary conditions (Equation4) and the initial condition (Equation5).

From the expression of the basic reproduction number R0 of system (Equation3), we see that the introduction of the age structure has significant influence on the value of the basic reproduction number of tuberculosis transmission. In fact, a direct calculation shows that with the assumption (Equation91), R0 reduces to R0, the basic reproduction number of epidemiological model (Equation1). We note that it is assumed in system (Equation1) that all parameters are positive constants and all individuals within a compartment behave identically, regardless of how much time they have spent in the compartment. For instance, infectious individuals are assumed to be equally infectious during their periodic infectivity and the waiting times in each compartment are assumed to be exponentially distributed. In system (Equation3), we included the duration that an individual has spent in the latently infected compartment and in the active infectious compartment as continuous variables. Hence, R0 can be used to describe the number of new infections generated by a single newly infectious individual during the full infectious period more accurately than R0. This indicates that although ODE models without age structure are usually easier to use than PDE models with age structure, it could lead to the over-valuation or underestimation of the basic reproduction number of tuberculosis transmission.

By choosing appropriate kernel functions, system (Equation3) also contains epidemiological models with time delay, and the global stability results in this work provide the global dynamics for these delayed epidemic models.

Acknowledgments

The authors wish to thank the reviewers and the Editor for their careful reading, valuable comments and suggestions that greatly improved the presentation of this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://doi.org/10.1080/17513758.2020.1761137)

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 11871316, 11801340, 61573016, 11371368] and the Natural Science Foundation of Shanxi Province [grant numbers 201801D121006, 201801D221007].

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