This paper is about the existence of traveling wave solutions for a delayed nonlocal dispersal SIR epidemic model with the critical wave speed. Because of the introduction of nonlocal dispersal and the generality of incidence function, it is difficult to investigate the existence of critical traveling waves. To this end, we construct an auxiliary system and show the existence of traveling waves for the auxiliary system. Employing the results for the auxiliary system, we obtain the existence of traveling waves for the delayed nonlocal dispersal SIR epidemic model with the critical wave speed under mild conditions.
Citation: Shiqiang Feng, Dapeng Gao. Existence of traveling wave solutions for a delayed nonlocal dispersal SIR epidemic model with the critical wave speed[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9357-9380. doi: 10.3934/mbe.2021460
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This paper is about the existence of traveling wave solutions for a delayed nonlocal dispersal SIR epidemic model with the critical wave speed. Because of the introduction of nonlocal dispersal and the generality of incidence function, it is difficult to investigate the existence of critical traveling waves. To this end, we construct an auxiliary system and show the existence of traveling waves for the auxiliary system. Employing the results for the auxiliary system, we obtain the existence of traveling waves for the delayed nonlocal dispersal SIR epidemic model with the critical wave speed under mild conditions.
In the biological context, to better understand the spatial spread of infectious diseases, epidemic waves in all kinds of epidemic models are attracting more and more attention, for instance, in Wu et al. [1], Wang et al. [2] and Zhang et al. [3,4,5]. Biologically speaking, the existence of an epidemic wave suggests that the disease can spread in the population. The traveling wave describes the epidemic wave moving out from an initial disease-free equilibrium to the endemic equilibrium with a constant speed. Various theoretical results, numerical algorithms and applications have been studied extensively for traveling waves about epidemic models in the literature; for instance, we refer the reader to [6,7,8,9]. More precisely, Hosono and Ilyas [10] studied the existence of traveling wave solutions for a reaction-diffusion model. In view of the fact that individuals can move freely and randomly and can be exposed to the infection from contact with infected individuals in different spatial location, Wang and Wu [11] investigated the existence and nonexistence of non-trivial traveling wave solutions of a general class of diffusive Kermack-Mckendrick SIR models with nonlocal and delayed transmission, see also [12]. Incorporating random diffusion into epidemic model, then the dynamics of disease transmission between species in a heterogeneous habitat can be described by a variety of reaction-diffusion models (see, for example, [13,14,15] and the references therein). Random diffusion is essentially a local behavior, which depicts the individuals at the location x can only be influenced by the individuals in the neighborhood of the location x. In real life, individuals can move freely. One way to solve such problems is to introduce nonlocal dispersal, which is the standard convolution with space variable. Recently, Yang et al. [16] studied a nonlocal dispersal Kermack-McKendrick epidemic model. Cheng and Yuan [17] investigated the traveling waves of a nonlocal dispersal Kermack-McKendrick epidemic model with delayed transmission, Zhang et al. [18] discussed the traveling waves for a delayed SIR model with nonlocal dispersal and nonlinear incidence, and Zhou et al. [19] proved the existence and non-existence of traveling wave solutions for a nonlocal dispersal SIR epidemic model with nonlinear incidence rate. As we know, there are many existence of traveling wave solutions for reaction-diffusion models when the wave speed is greater than the minimum wave speed (see, e.g.[20,21,22]). However, there are few discussions on the existence of traveling wave solutions when the wave speed is equal to the minimum wave speed (the critical wave speed), see[23,24,25,26].
In this paper, we focus on the delayed SIR model with the nonlocal dispersal and nonlinear incidence which proposed by Zhang et al.[18] as follows:
{∂S(x,t)∂t=d1(J∗S(x,t)−S(x,t))−f(S(x,t))g(I(x,t−τ)),∂I(x,t)∂t=d2(J∗I(x,t)−I(x,t))+f(S(x,t))g(I(x,t−τ))−γI(x,t),∂R(x,t)∂t=d3(J∗R(x,t)−R(x,t))+γI(x,t), | (1.1) |
where S(x,t), I(x,t) and R(x,t) denote the densities of susceptible, infective and removal individuals at time t and location x, respectively. The parameters di>0(i=1,2,3) are diffusion rates for susceptible, infected and removal individuals, respectively. The removal rate γ is positive number and τ>0 is a given constant. Moreover, J∗S(x,t),J∗I(x,t) and J∗R(x,t) represent the standard convolution with space variable x, namely,
J∗u(x,t)=∫RJ(x−y)u(y,t)dy=∫RJ(y)u(x−y,t)dy, |
where u can be either S,I or R. Throughout this paper, assume that the nonlinear functions f and g, and the dispersal kernel J satisfy the following assumptions:
(A1) f(S) is positive and continuous for all S>0 with f(0)=0 and f′(S) is positive and bounded for all S≥0 with L:=maxS∈[0,∞)f′(S);
(A2) g(I) is positive and continuous for all I>0 with g(0)=0,g′(I)>0 and g″(I)≤0 for all I≥0;
(A3) J∈C1(R),J(y)=J(−y)≥0,∫RJ(y)dy=1 and J is compactly supported.
Since the third equation in (1.1) is decoupled with the first two equations, it is enough to consider the following subsystem of (1.1):
{∂S(x,t)∂t=d1(J∗S(x,t)−S(x,t))−f(S(x,t))g(I(x,t−τ)),∂I(x,t)∂t=d2(J∗I(x,t)−I(x,t))+f(S(x,t))g(I(x,t−τ))−γI(x,t). | (1.2) |
We recall that, a traveling wave solution of system (1.2) is a solution of form (S(ξ),I(ξ)) for system (1.2), where ξ=x+ct. Substituting (S(ξ),I(ξ)) with ξ=x+ct into system (1.2) yields the following system:
{cS′(ξ)=d1(J∗S(ξ)−S(ξ))−f(S(ξ))g(I(ξ−cτ)),cI′(ξ)=d2(J∗I(ξ)−I(ξ))+f(S(ξ))g(I(ξ−cτ))−γI(ξ). | (1.3) |
Clearly, if τ=0, then system (1.2) becomes
{∂S(x,t)∂t=d1(J∗S(x,t)−S(x,t))−f(S(x,t))g(I(x,t)),∂I(x,t)∂t=d2(J∗I(x,t)−I(x,t))+f(S(x,t))g(I(x,t))−γI(x,t), | (1.4) |
which was considered by Zhou et al. [19]. Combining the method of auxiliary system, Schauder's fixed point theorem and three limiting arguments, they proved the following result.
Theorem 1.1. ([19,Theorem 2.3]) Assume that (A1)-(A3) hold. If R0>1 and c≥c∗, where c∗>0 is the minimal wave speed and R0=f(S0)g′(0)γ is the reproduction number of (1.4), then system (1.4) admits a nontrivial and nonnegative traveling wave solution (S(x+ct),I(x+ct)) satisfying the following asymptotic boundary conditions:
S(−∞)=S0, S(+∞)=S∞<S0, I(±∞)=0, | (1.5) |
where S0>0 is a constant representing the size of the susceptible individuals before being infected.
For (1.3) satisfying (1.5), Zhang et al.[18] obtained the following result.
Theorem 1.2. ([18,Theorem 2.7]) Assume that (A1)-(A3) hold. In addition, suppose that
(H) there exists I0>0 such that f(S0)g(I0)−γI0≤0.
If R0>1 and c>c∗, where c∗>0 is the minimal wave speed and R0=f(S0)g′(0)γ is the reproduction number of (1.3), then system (1.3) admits a traveling wave solution (S(ξ),I(ξ)) satisfying (1.5).
We note that the assumption (H) plays a key role in the proof of Theorem 1.2 ([18,Theorem 2.7]). However, we should pointed out here that (H) cannot be applied for some incidence, such as bilinear incidence, see[27]. Therefore, one natural question is: can we obtain the existence of traveling wave solutions for system (1.2) without assumption (H)? This constitutes our first motivation of the present paper. In addition, as was pointed out in [28] that, epidemic waves with the minimal/critical speed play a significant role in the study of epidemic spread. However, it is very challenging to investigate traveling waves with the critical wave speed. Herein, we should point out that Zhang et al.[29] defined a minimal wave speed c∗:=infλ>0d2∫RJ(y)e−λydy−d2+f(S0)g′(0)e−λcτ−γλ and then studied the existence of critical traveling waves for system (1.1). They took a bit lengthy analysis to derive the boundedness of the density of infective individual I. Unlike [29], we will apply the auxiliary system to obtain the existence of critical traveling waves, since the method is first applied in nonlocal dispersal epidemic model in 2018, see [19] for more details. Our second motivation is to make an attempt in this direction.
The rest of this paper is organized as follows. In Section 2, we propose an auxiliary system and establish the existence of traveling wave solutions for the auxiliary system. In Section 3, we prove the existence of traveling waves under the critical wave speed. The paper ends with an application for our general results and a brief conclusion in Section 4.
In this section, we will derive the existence of traveling wave solutions for the following auxiliary system on R:
{cS′(ξ)=d1(J∗S(ξ)−S(ξ))−f(S(ξ))g(I(ξ−cτ)),cI′(ξ)=d2(J∗I(ξ)−I(ξ))+f(S(ξ))g(I(ξ−cτ))−γI(ξ)−εI2(ξ), | (2.1) |
where ε>0 is a constant.
Clearly, (A1) and (A2) imply that f(0)=g(0)=0. Thus, linearizing the second equation in (2.1) at the initial disease free point (S0,0) yields
d2∫RJ(y)(I(ξ−y)−I(ξ))dy−cI′(ξ)+f(S0)g′(0)I(ξ−cτ)−γI(ξ)=0. | (2.2) |
Substituting I(ξ)=eλξ into (2.2) leads to the corresponding characteristic equation:
Δ(λ,c):=d2∫RJ(y)(e−λy−1)dy−cλ+f(S0)g′(0)e−λcτ−γ=0. | (2.3) |
Lemma 2.1. ([18]) Suppose that R0:=f(S0)g′(0)γ>1. Then there exist c∗>0 and λ∗>0 such that
Δ(λ∗,c∗)=0and∂Δ(λ,c)∂λ|(λ∗,c∗)=0. |
Obviously, Δ(λ,c)=0 also has the following properties:
(i) If c>c∗, then Δ(λ,c)=0 has two different positive roots λ1:=λ1(c)<λ2:=λ2(c) with
Δ(⋅,c){>0,λ∈[0,λ1(c))∪(λ2(c),+∞),<0,λ∈(λ1(c),λ2(c)). |
(ii) If 0<c<c∗, then Δ(λ,c)>0 for all λ≥0.
We now present some lemmas for our main results. Throughout this section, we always assume that R0>1 and c>c∗.
For our purpose, we define the following functions on R:
¯S(ξ):=S0, S_(ξ):=max{S0−ρeαξ,0}, ¯I(ξ):=min{eλ1ξ,Kε}, I_(ξ):=max{eλ1ξ(1−Meηξ),0}, |
where Kε=f(S0)g′(0)−γε, λ1 is the smallest positive real root of Eq.(2.3), and α,ρ,η, M are four positive constants to be determined in the following lemmas.
Lemma 2.2. The function ¯I(ξ) satisfies the following inequality:
c¯I′(ξ)≥d2J∗¯I(ξ)−d2¯I(ξ)+f(¯S(ξ))g(¯I(ξ−cτ))−γ¯I(ξ)−ε¯I2(ξ). | (2.4) |
Proof. The concavity of g(I) with respect to I implies that g(I)≤g′(0)I and so
g(I(ξ−cτ))≤g′(0)I(ξ−cτ). | (2.5) |
When ¯I(ξ)=eλ1ξ, it follows from (2.5) that
d2J∗¯I(ξ)−d2¯I(ξ)−c¯I′(ξ)+f(¯S(ξ))g(¯I(ξ−cτ))−γ¯I(ξ)−ε¯I2(ξ)≤d2J∗¯I(ξ)−d2¯I(ξ)−c¯I′(ξ)+f(S0)g′(0)¯I(ξ−cτ)−γ¯I(ξ)−ε¯I2(ξ)=(d2∫RJ(y)(e−λ1y−1)dy−cλ1+f(S0)g′(0)e−λ1cτ−γ)eλ1ξ−εe2λ1ξ=eλ1ξΔ(λ1,c)−εe2λ1ξ=−εe2λ1ξ≤0. |
When ¯I(ξ)=Kε=f(S0)g′(0)−γε, we derive from (A3) and (2.5) that
d2J∗¯I(ξ)−d2¯I(ξ)−c¯I′(ξ)+f(¯S(ξ))g(¯I(ξ−cτ))−γ¯I(ξ)−ε¯I2(ξ)≤f(S0)g′(0)¯I(ξ−cτ)−γKε−εK2ε=f(S0)g′(0)Kε−γKε−εK2ε=0, |
and the lemma follows.
Lemma 2.3. Suppose that α∈(0,λ1) is sufficiently small. Then the function S_(ξ) satisfies
cS_′(ξ)≤d1J∗S_(ξ)−d1S_(ξ)−f(S_(ξ))g(¯I(ξ−cτ)) | (2.6) |
for any ξ≠ξ1:=1αln(S0ρ) and ρ>S0 large enough.
Proof. If ξ>ξ1, then S_(ξ)=0 and (2.6) holds. If ξ<ξ1, then S_(ξ)=S0−ρeαξ and ¯I(ξ)=eλ1ξ. According to the assumptions (A1), (A2) and (2.5), one has
d1J∗S_(ξ)−d1S_(ξ)−cS_′(ξ)−f(S_(ξ))g(¯I(ξ−cτ))≥d1J∗S_(ξ)−d1S_(ξ)−cS_′(ξ)−f(S0)g′(0)¯I(ξ−cτ)=eαξ(−d1ρ∫RJ(y)(e−αy−1)dy+cρα−f(S0)g′(0)e−λ1cτe(λ1−α)ξ). | (2.7) |
Since 0<α<λ1 and e(λ1−α)ξ<(S0ρ)λ1−αα for ξ<ξ1, it follows from (2.7) that
d1J∗S_(ξ)−d1S_(ξ)−cS_′(ξ)−f(S_(ξ))g(¯I(ξ−cτ))≥eαξ(−d1ρ∫RJ(y)(e−αy−1)dy+cρα−f(S0)g′(0)e−λ1cτ(S0ρ)λ1−αα). | (2.8) |
Taking ρ=1α in (2.8) and noting that
limα→0+(αS0)λ1−αα=0,limα→0+1α∫RJ(y)(e−αy−1)dy=0, |
for sufficiently small α>0, we have
−d1α∫RJ(y)(e−αy−1)dy+c−f(S0)g′(0)e−λ1cτ(αS0)λ1−αα>0. | (2.9) |
Owing to (2.8) and (2.9), we find
d1J∗S_(ξ)−d1S_(ξ)−cS_′(ξ)−f(S_(ξ))g(¯I(ξ−cτ))≥eαξ(−d1α∫RJ(y)(e−αy−1)dy+c−f(S0)g′(0)e−λ1cτ(αS0)λ1−αα)>0. |
This completes the proof.
Lemma 2.4. Assume that 0<η<min{λ2−λ1,λ1}. Then for sufficiently large M>1, the function I_(ξ) satisfies
cI_′(ξ)≤d2(J∗I_(ξ)−I_(ξ))+f(S_(ξ))g(I_(ξ−cτ))−γI_(ξ)−εI_2(ξ) | (2.10) |
for any ξ≠ξ2:=1ηln1M.
Proof. If ξ>ξ2, then I_(ξ)=0,J∗I_(ξ)≥0 and g(I_(ξ−cτ))≥0 (by (A2)), Thus (2.10) holds. If ξ<ξ2, then we can take M1>1 such that 1ηln1M1+1=ξ1 and choose large enough M≥M1 with I_(ξ)=eλ1ξ(1−Meηξ) and S_(ξ)=S0−ρeαξ. Thus, (2.10) is equivalent to
f(S0)g′(0)I_(ξ−cτ)−f(S_(ξ))g(I_(ξ−cτ))+εI_2(ξ)≤d2(J∗I_(ξ)−I_(ξ))−cI_′(ξ)+f(S0)g′(0)I_(ξ−cτ)−γI_(ξ) |
and so
f(S0)g′(0)I_(ξ−cτ)−f(S_(ξ))g(I_(ξ−cτ))+εI_2(ξ)≤d2∫RJ(y)eλ1(ξ−y)(1−Meη(ξ−y))dy−d2eλ1ξ(1−Meηξ)−c(λ1eλ1ξ−M(λ1+η)e(λ1+η)ξ)+f(S0)g′(0)(eλ1(ξ−cτ)−Me(λ1+η)(ξ−cτ))−γ(eλ1ξ−Me(λ1+η)ξ)=d2(∫RJ(y)e−λ1ydy−d2−cλ1+f(S0)g′(0)e−λ1cτ−γ)eλ1ξ−Me(λ1+η)ξ(d2∫RJ(y)e−(λ1+η)ydy−d2−c(λ1+η)+f(S0)g′(0)e−(λ1+η)cτ−γ)=−MΔ(λ1+η,c)e(λ1+η)ξ. | (2.11) |
For any ˆε∈(0,g′(0)), noting that limI→0+g(I)I=g′(0), there exists a small positive number δ0 such that
g(I)I≥g′(0)−ˆε, 0<I<δ0. | (2.12) |
Choose M large enough such that 0<I_(ξ)<δ0. Then, it follows from (2.12) that
f(S0)g′(0)I_(ξ−cτ)−f(S_(ξ))g(I_(ξ−cτ))=(f(S0)g′(0)−f(S_(ξ))g(I_(ξ−cτ))I_(ξ−cτ))I_(ξ−cτ)≤(f(S0)g′(0)−f(S_(ξ))g(I_(ξ−cτ))I_(ξ−cτ)+I_(ξ−cτ)2)2≤(f(S0)g′(0)−f(S_(ξ))(g′(0)−ˆε)+I_(ξ−cτ)2)2. | (2.13) |
Since (2.13) holds for arbitrary sufficiently small ˆε∈(0,g′(0)) and S_(ξ)→S0 for sufficiently large M, one can conclude from (2.13) that
f(S0)g′(0)I_(ξ−cτ)−f(S_(ξ))g(I_(ξ−cτ))≤I_2(ξ−cτ). |
Then, to prove (2.11), we only need to show that
I_2(ξ−cτ)+εI_2(ξ)≤−MΔ(λ1+η,c)e(λ1+η)ξ. |
Noting I_2(ξ−cτ)≤e2λ1ξ and I_2(ξ)≤e2λ1ξ, it suffices to show that
(1+ε)e(λ1−η)ξ≤−MΔ(λ1+η,c). | (2.14) |
Due to 0<η<λ2−λ1, we have Δ(λ1+η,c)<0 (by Lemma 2.1). Then (2.14) leads to
M≥(1+ε)e(λ1−η)ξ−Δ(λ1+η,c). |
The facts that ξ<ξ2<0 and 0<η<λ1 imply that e(λ1−η)ξ<1. To end the proof, we only need to take
M≥max{1+ε−Δ(λ1+η,c)+1,M1}. |
This completes the proof.
Next we define a bounded set as follows:
ΓX,τ={(ϕ(⋅),φ(⋅))∈C([−X−cτ,X],R2)|ϕ(ξ)=S_(−X),φ(ξ)=I_(−X),for anyξ∈[−X−cτ,−X],S_(ξ)≤ϕ(ξ)≤S0,I_(ξ)≤φ(ξ)≤¯I(ξ),for anyξ∈[−X,X],} |
where
X>max{1ηlnM,1αlnρS0}. |
For any (ϕ(⋅),φ(⋅))∈C([−X−cτ,X],R2), we define
ˆϕ(ξ)={ϕ(X),ξ>X,ϕ(ξ),−X−cτ≤ξ≤X,S_(ξ+cτ),ξ<−X−cτ | (2.15) |
and
ˆφ(ξ)={φ(X),ξ>X,φ(ξ),−X−cτ≤ξ≤X,I_(ξ+cτ),ξ<−X−cτ. | (2.16) |
We consider the following initial value problems:
cS′(ξ)=d1∫RJ(y)ˆϕ(ξ−y)dy−d1S(ξ)−f(S(ξ))g(φ(ξ−cτ)) | (2.17) |
and
cI′(ξ)=d2∫RJ(y)ˆφ(ξ−y)dy+f(ϕ(ξ))g(φ(ξ−cτ))−(d2+γ)I(ξ)−εI2(ξ) | (2.18) |
with
S(−X)=S_(−X), I(−X)=I_(−X). | (2.19) |
By the existence theorem of ordinary differential equations, problems (2.17)-(2.19) admit a unique solution (SX(⋅),IX(⋅)) satisfying SX(⋅)∈C1([−X,X]) and IX(⋅)∈C1([−X,X]). Thus, we can define an operator F=(F1,F2):ΓX,τ→C([−X−cτ,X]) by
F1[ϕ,φ](ξ)=SX(ξ), F2[ϕ,φ](ξ)=IX(ξ) for ξ∈[−X,X] |
and
F1[ϕ,φ](ξ)=SX(−X), F2[ϕ,φ](ξ)=IX(−X) for ξ∈[−X−cτ,−X]. |
Proposition 2.1. The operator F=(F1,F2) maps ΓX,τ into ΓX,τ.
Proof. For any (ϕ(⋅),φ(⋅))∈ΓX,τ, we should show that
S_(ξ)≤F1[ϕ,φ](ξ)≤S0,I_(ξ)≤F2[ϕ,φ](ξ)≤¯I(ξ),∀ξ∈[−X,X] |
and
F1[ϕ,φ](ξ)=S_(−X),F2[ϕ,φ](ξ)=I_(−X),∀ξ∈[−X−cτ,−X]. |
By the definition of the operator F, it is easy to see that the last two equalities hold.
For ξ∈[−X,X], we first consider F1[ϕ,φ](ξ). By the definition of the operator F, it is sufficient to prove S_(ξ)≤SX(ξ)≤S0. Note that f(0)=0 (see (A1)). Then it is obvious that 0 is a sub-solution of (2.17). It follows from the maximum principle that SX(ξ)≥0 for ξ∈[−X,X]. From the definition of ˆϕ(ξ), (A1) and (A2), we obtain
d1∫RJ(y)ˆϕ(ξ−y)dy−d1¯S(ξ)−f(¯S(ξ))g(φ(ξ−cτ))−c¯S′(ξ)≤d1J∗¯S(ξ)−d1¯S(ξ)−f(¯S(ξ))g(φ(ξ−cτ))−c¯S′(ξ)≤0, |
which implies that ¯S(ξ)=S0 is a super-solution of (2.17). Thus, we have SX(ξ)≤S0 for ξ∈[−X,X]. Clearly, S_(ξ)=S0−ρeαξ for ξ∈[−X,ξ1). Thus, utilizing Lemma 2.3 and (A2),
cS_′(ξ)−d1∫RJ(y)ˆϕ(ξ−y)dy+d1S_(ξ)+f(S_(ξ))g(φ(ξ−cτ))≤cS_′(ξ)−d1[J∗S_(ξ)−S_(ξ)]+f(S_(ξ))g(¯I(ξ−cτ))≤0, |
for any ξ∈(−X,ξ1). Since SX(−X)=S_(−X), applying the comparison principle, we have S_(ξ)≤SX(ξ) for ξ∈[−X,ξ1) and so S_(ξ)≤SX(ξ)≤S0 for all ξ∈[−X,X].
Next, we consider F2[ϕ,φ](ξ). Similarly, we only need to show that I_(ξ)≤IX(ξ)≤¯I(ξ). First, from the maximum principle, we have IX(ξ)≥0 for ξ∈[−X,X]. Thus, it follows from Lemma 2.4, S_(ξ)≤ϕ(ξ), I_(ξ)≤ˆφ(ξ), (A1), (A2) and I_(ξ)=eλ1ξ(1−Meηξ) for ξ∈[−X,ξ2) that
cI_′(ξ)−d2∫RJ(y)ˆφ(ξ−y)dy−f(ϕ(ξ))g(φ(ξ−cτ))+(d2+γ)I_(ξ)+εI_2(ξ)≤cI_′(ξ)−d2[J∗I_(ξ)−I_(ξ)]−f(S_(ξ))g(I_(ξ−cτ))+γI_(ξ)+εI_2(ξ)≤0 |
for all ξ∈[−X,ξ2). Since IX(−X)=I_(−X), the comparison principle implies that I_(ξ) is a sub-solution of (2.18) on [−X,ξ2). Recalling the fact that I_(ξ)=0 for ξ∈[ξ2,X], it is easy to see that
I_(ξ)≤IX(ξ), ∀ξ∈[−X,X]. | (2.20) |
Since ϕ(ξ)≤S0 and ^φ(ξ)≤¯I(ξ) for all ξ∈[−X,X], from (A1), (A2) and Lemma 2.2, we deduce that
c¯I′(ξ)−d2∫RJ(y)ˆφ(ξ−y)dy−f(ϕ(ξ))g(φ(ξ−cτ))+(d2+γ)¯I(ξ)+ε¯I2(ξ)≥c¯I′(ξ)−d2[J∗¯I(ξ)−¯I(ξ)]−f(S0)g(¯I(ξ−cτ))+γ¯I(ξ)+ε¯I2(ξ)≥0, |
which ensures that ¯I(ξ) is a super-solution of (2.18) on [−X,X] by the comparison principle. Combining with (2.20), we know that I_(ξ)≤IX(ξ)≤¯I(ξ) for ξ∈[−X,X]. The proof is finished.
Proposition 2.2. The operator F:ΓX,τ→ΓX,τ is completely continuous.
Proof. We first show the compactness of F=(F1,F2). We need to prove that, for any bounded subset B⊂ΓX,τ, the set F(B) is precompact. In view of the definition of the operator F, for any (SX,IX)∈F(B), there exists (ϕ,φ)∈B such that F[ϕ,φ](ξ)=(SX,IX)(ξ) for ξ∈[−X,X] and F[ϕ,φ](ξ)=(SX,IX)(−X) for ξ∈[−X−cτ,−X].
Since (ϕ,φ)∈B, there exists a constant M1>0 such that
|SX(ξ)|≤M1,|IX(ξ)|≤M1,∀ξ∈[−X−cτ,X]. |
Moreover, since (ϕ,φ)∈B, from (2.17), (2.18) and the above inequalities, we know that there exists some constant M2>0 such that
|S′X(ξ)|≤M2,|I′X(ξ)|≤M2,∀ξ∈[−X−cτ,X]. |
It follows that F(B) is a family of the uniformly bounded and equicontinuous functions. The compactness of F(B) then follows from the Arzelà-Ascoli theorem and the definition of ΓX,τ.
Next we prove the continuity of F=(F1,F2). By the definition of the operator F, we assume that (ϕi(ξ),φi(ξ))∈ΓX,τ(i=1,2) and SX,i(ξ)=F1[ϕi,φi](ξ),IX,i(ξ)=F2[ϕi,φi](ξ) for ξ∈[−X,X]. We will prove the continuity of F by the following two steps.
Step 1. The continuity of F1.
It follows from (2.17) that
c(S′X,1(ξ)−S′X,2(ξ))+d1(SX,1(ξ)−SX,2(ξ))=d1∫RJ(ξ−y)(ˆϕ1(y)−ˆϕ2(y))dy+[f(SX,2(ξ))g(φ2(ξ−cτ))−f(SX,1(ξ))g(φ1(ξ−cτ))], | (2.21) |
where \widehat{\phi}_i(\xi)(i = 1, 2) is defined analogously as the \widehat{\phi}(\xi) in (2.15).
In view of
\begin{eqnarray} &&\left|d_1\int_\mathbb{R}J(\xi-y)(\widehat{\phi}_1(y)-\widehat{\phi}_2(y))dy\right|\\ & = &d_1\bigg|\int_{-\infty}^{-X-c\tau}J(\xi-y)(\underline{S}(y+c\tau)-\underline{S}(y+c\tau))dy+\int_{-X-c\tau}^{X}J(\xi-y)(\phi_1(y)-\phi_2(y))dy\\ &&\mbox{}+\int_{X}^{+\infty}J(\xi-y)(\phi_1(X)-\phi_2(X))dy\bigg|\\ &\leq&d_1\int_{-X-c\tau}^{X}J(\xi-y)|\phi_1(y)-\phi_2(y)|dy+d_1\int_{X}^{+\infty}J(\xi-y)|\phi_1(X)-\phi_2(X)|dy\\ & = &d_1\int_{-X-c\tau}^{-X}J(\xi-y)|\underline{S}(-X)-\underline{S}(-X)|dy+d_1\int_{-X}^{X}J(\xi-y)|\phi_1(y)-\phi_2(y)|dy\\ &&+d_1\int_{X}^{+\infty}J(\xi-y)|\phi_1(X)-\phi_2(X)|dy\\ &\leq&2d_1\max\limits_{y\in[-X, X]}|\phi_1(y)-\phi_2(y)|, \end{eqnarray} | (2.22) |
it follows from (2.5), the mean-value theorem, (A1), (A2) and the definition of \Gamma_{X, \tau} that
\begin{eqnarray} && |f(S_{X, 2}(\xi))g(\varphi_2(\xi-c\tau))-f(S_{X, 1}(\xi))g(\varphi_1(\xi-c\tau))|\\ & = &|f(S_{X, 2}(\xi))g(\varphi_2(\xi-c\tau))-f(S_{X, 2}(\xi))g(\varphi_1(\xi-c\tau))+f(S_{X, 2}(\xi))g(\varphi_1(\xi-c\tau))\\ &&\mbox{}-f(S_{X, 1}(\xi))g(\varphi_1(\xi-c\tau))|\\ &\leq&f(S_0)|g(\varphi_2(\xi-c\tau))-g(\varphi_1(\xi-c\tau))|+|g(\varphi_1(\xi-c\tau))||f(S_{X, 2}(\xi))-f(S_{X, 1}(\xi))|\\ &\leq&f(S_0)g'(0)|\varphi_2(\xi-c\tau)-\varphi_1(\xi-c\tau)|+g'(0)\varphi_1(\xi-c\tau)L|S_{X, 2}(\xi)-S_{X, 1}(\xi)|\\ &\leq&f(S_0)g'(0)\max\limits_{\xi\in[-X, X]}|\varphi_1(\xi)-\varphi_2(\xi)|+L g'(0)K_{\varepsilon}|S_{X, 2}(\xi)-S_{X, 1}(\xi)|. \end{eqnarray} | (2.23) |
If S_{X, 1}(\xi)-S_{X, 2}(\xi) > 0 , then we deduce from (2.21)-(2.23) that
\begin{eqnarray} && c(S'_{X, 1}(\xi)-S'_{X, 2}(\xi))+(d_1-L g'(0)K_{\varepsilon})(S_{X, 1}(\xi)-S_{X, 2}(\xi))\\ &\leq&f(S_0)g'(0)\max\limits_{\xi\in[-X, X]}|\varphi_1(\xi)-\varphi_2(\xi)|+2d_1\max\limits_{y\in[-X, X]}|\phi_1(y)-\phi_2(y)|. \end{eqnarray} | (2.24) |
Applying the Gronwall inequality (see p. 90 of [31]) to (2.24), we obtain that \mathcal{F}_1 is continuous on \Gamma_{X, \tau} . If S_{X, 1}(\xi)-S_{X, 2}(\xi) < 0 , then one can prove the same result. Thus, we show that \mathcal{F}_1 is continuous on \Gamma_{X, \tau} .
Step 2. The continuity of \mathcal{F}_2 .
From (2.18), we have
\begin{eqnarray} &&c(I'_{X, 1}(\xi)-I'_{X, 2}(\xi))+(d_2+\gamma)(I_{X, 1}(\xi)-I_{X, 2}(\xi))+\varepsilon(I_{X, 1}(\xi)+I_{X, 2}(\xi))(I_{X, 1}(\xi)-I_{X, 2}(\xi))\\ & = &d_2\int_{\mathbb{R}}J(\xi-y)(\widehat{\varphi}_1(y)-\widehat{\varphi}_2(y))dy+[f(\phi_1(\xi))g(\varphi_1(\xi-c\tau))-f(\phi_2(\xi))g(\varphi_2(\xi-c\tau))], \end{eqnarray} | (2.25) |
where \widehat{\varphi}_i(\xi)(i = 1, 2) is defined analogously as the \widehat{\varphi}(\xi) in (2.16).
In view of 0\leq I_{X, 1}(\xi)+I_{X, 2}(\xi)\leq 2K_{\varepsilon} , we then deduce from (2.25) that there exists a nonnegative constant \widetilde{c} such that
\begin{eqnarray} &&c(I'_{X, 1}(\xi)-I'_{X, 2}(\xi))+(d_2+\gamma+\widetilde{c}\varepsilon)(I_{X, 1}(\xi)-I_{X, 2}(\xi))\\ &\leq&d_2\int_{\mathbb{R}}J(\xi-y)(\widehat{\varphi}_1(y)-\widehat{\varphi}_2(y))dy+[f(\phi_1(\xi))g(\varphi_1(\xi-c\tau))-f(\phi_2(\xi))g(\varphi_2(\xi-c\tau))]. \end{eqnarray} | (2.26) |
Arguing as in the proof of (2.22) and (2.23), we obtain
\begin{eqnarray} \bigg|d_2\int_{\mathbb{R}}J(\xi-y)(\widehat{\varphi}_1(y)-\widehat{\varphi}_2(y))dy\bigg|\leq2d_2\max\limits_{\xi\in[-X, X]}|\varphi_1(\xi)-\varphi_2(\xi)| \end{eqnarray} | (2.27) |
and
\begin{eqnarray} &&|f(\phi_1(\xi))g(\varphi_1(\xi-c\tau))-f(\phi_2(\xi))g(\varphi_2(\xi-c\tau))|\\ &\leq&f(S_0)g'(0)\max\limits_{\xi\in[-X, X]}|\varphi_1(\xi)-\varphi_2(\xi)|+L g'(0)K_{\varepsilon}\max\limits_{\xi\in[-X, X]}|\phi_1(\xi)-\phi_2(\xi)|. \end{eqnarray} | (2.28) |
Thus, it follows from (2.26)-(2.28) that
\begin{eqnarray*} &&c(I'_{X, 1}(\xi)-I'_{X, 2}(\xi))+(d_2+\gamma+\widetilde{c}\varepsilon)(I_{X, 1}(\xi)-I_{X, 2}(\xi))\\ &\leq&(f(S_0)g'(0)+2d_2)\max\limits_{\xi\in[-X, X]}|\varphi_1(\xi)-\varphi_2(\xi)|+L g'(0)K_{\varepsilon}\max\limits_{\xi\in[-X, X]}|\phi_1(\xi)-\phi_2(\xi)|, \end{eqnarray*} |
which together with the Gronwall inequality implies that \mathcal{F}_2 is continuous on \Gamma_{X, \tau} . This completes the proof.
From the definition of \Gamma_{X, \tau} , it is easy to see that \Gamma_{X, \tau} is closed and convex. Thus, employing Propositions 2.1, 2.2 and Schauder's fixed point theorem, we can obtain the following result.
Proposition 2.3. There exists (S_X(\xi), I_X(\xi))\in\Gamma_{X, \tau} such that
(S_X(\xi), I_X(\xi)) = \mathcal{F}(S_X, I_X)(\xi) |
and
\begin{eqnarray*} \underline{S}(\xi)\leq S_{X}(\xi)\leq S_0, \ \underline{I}(\xi)\leq I_{X}(\xi)\leq\overline{I}(\xi), \ \xi\in(-{X}, {X}). \end{eqnarray*} |
Next, we wish to obtain the existence of traveling wave solutions of (2.1) on \mathbb{R} . Before doing this, we need to give some estimates for S_X(\xi) and I_X(\xi) in the following space:
C^{1, 1}([-X, X]) = \left\{u\in C^1[-X, X]|u\ \text{and}\ u' \ \text{are Lipschitz continuous}\right\} |
with the norm
\|u(x)\|_{C^{1, 1}([-X, X])}: = \max\limits_{x\in[-X, X]}|u(x)|+\max\limits_{x\in[-X, X]}|u'(x)|+\sup\limits_{x, y\in[-X, X], x\neq y}\frac{|u'(x)-u'(y)|}{|x-y|}. |
Proposition 2.4. Let (S_X(\xi), I_X(\xi)) be the fixed point of the operator \mathcal{F} which is guaranteed by Proposition 2.3. Then there exists a positive constant C_1 independent of X such that
\|S_X(\xi)\|_{C^{1, 1}([-X, X])} < C_1, \quad \|I_X(\xi)\|_{C^{1, 1}([-X, X])} < C_1 |
for all
X > \max\left\{\frac{1}{\eta}\ln M, \frac{1}{\alpha}\ln\frac{\rho}{S_0}\right\}. |
Proof. First, we know that (S_X(\xi), I_X(\xi)) satisfies
\begin{eqnarray} \left\{ \begin{array}{l} c S'_X(\xi) = d_1\int_{\mathbb{R}}J(y)\widehat{S_X}(\xi-y)dy-d_1S_X(\xi)-f(S_X(\xi))g(I_X(\xi-c\tau)), \ \xi\in[-X, X], \\ S_X(\xi) = \underline{S}(-X), \ \xi\in[-X-c\tau, -X] \end{array} \right. \end{eqnarray} | (2.29) |
and
\begin{eqnarray} \left\{ \begin{array}{l} c I'_X(\xi) = d_2\int_{\mathbb{R}}J(y)\widehat{I_X}(\xi-y)dy+f(S_X(\xi))g(I_X(\xi-c\tau))-(d_2+\gamma)I_X(\xi)-\varepsilon I^2_X(\xi), \ \xi\in[-X, X], \\ I_X(\xi) = \underline{I}(-X), \ \xi\in[-X-c\tau, -X], \end{array} \right. \end{eqnarray} | (2.30) |
where
\widehat{S_X}(\xi) = \left \{ \begin{aligned} &S_X(X), \quad\xi > X, \\ &S_X(\xi), \quad-X-c\tau\leq\xi\leq X, \\ &\underline{S}(\xi+c\tau), \quad\xi < -X-c\tau \end{aligned} \right. |
and
\widehat{I_X}(\xi) = \left \{ \begin{aligned} &I_X(X), \quad\xi > X, \\ &I_X(\xi), \quad-X-c\tau\leq\xi\leq X, \\ &\underline{I}(\xi+c\tau), \quad\xi < -X-c\tau. \end{aligned} \right. |
By the facts that S_X(\xi)\leq S_0, \ 0\leq \widehat{S_X}(\xi)\leq S_0, \ 0\leq \widehat{I_X}(\xi)\leq K_{\varepsilon} and I_X(\xi-c\tau)\leq K_{\varepsilon} for \xi\in[-X, X] , it follows from (A1), (A3), (2.5) and (2.29) that
\begin{eqnarray*} |S'_X(\xi)|&\leq&\frac{d_1}{c}\left|\int_\mathbb{R}J(y)\widehat{S_X}(\xi-y)dy\right|+\frac{d_1}{c}\left|S_X(\xi)\right|+\frac{1}{c}\left|f(S_X(\xi))g(I_X(\xi-c\tau))\right|\\ &\leq&\frac{1}{c}(2d_1S_0+f(S_0)g'(0)K_{\varepsilon}). \end{eqnarray*} |
Thus, there exists a positive constant C_2 independent of X such that
\begin{eqnarray} \|S_X(\xi)\|_{C^1([-X, X])} < C_2. \end{eqnarray} | (2.31) |
Similar arguments apply to the case I'_X(\xi) , we have
\begin{eqnarray} \|I_X(\xi)\|_{C^1([-X, X])} < C_2. \end{eqnarray} | (2.32) |
Next, we intend to show that S_X(\xi), \ I_X(\xi), \ S'_X(\xi) and I'_X(\xi) are Lipschitz continuous. For any \xi, \eta\in[-X, X] , it follows from (2.31) and (2.32) that
\begin{eqnarray} |S_X(\xi)-S_X(\eta)| < C_2|\xi-\eta|, \quad |I_X(\xi)-I_X(\eta)| < C_2|\xi-\eta|, \end{eqnarray} | (2.33) |
and so S_X(\xi) and I_X(\xi) are Lipschitz continuous.
In view of (2.29), we have
\begin{eqnarray} && c|S'_X(\xi)-S'_X(\eta)|\\ &\leq &d_1\left|\int_\mathbb{R}J(y)(\widehat{S_X}(\xi-y)-\widehat{S_X}(\eta-y))dy\right|+d_1|S_X(\xi)-S_X(\eta)|\\ &&\mbox{}+\left|f(S_X(\xi))g(I_X(\xi-c\tau))-f(S_X(\eta))g(I_X(\eta-c\tau))\right|\\ &: = &B_1+B_2+B_3. \end{eqnarray} | (2.34) |
From (A3), we know that the kernel function J is Lipschitz continuous and compactly supported. Let L_J be the Lipschitz constant of J and R be the radius of supp J . Then,
\begin{eqnarray*} B_1 & = &d_1\left|\int_\mathbb{R}J(y)\widehat{S_X}(\xi-y)dy-\int_\mathbb{R}J(y)\widehat{S_X}(\eta-y)dy\right|\\ & = &d_1\left|\int_{-R}^{R}J(y)\widehat{S_X}(\xi-y)dy-\int_{-R}^{R}J(y)\widehat{S_X}(\eta-y)dy\right|\\ & = &d_1\left|\int_{\xi-R}^{\xi+R}J(\xi-y)\widehat{S_X}(y)dy-\int_{\eta-R}^{\eta+R}J(\eta-y)\widehat{S_X}(y)dy\right|\\ & = &d_1\left|\left(\int_{\eta+R}^{\xi+R}+\int_{\eta-R}^{\eta+R}+\int_{\xi-R}^{\eta-R}\right)J(\xi-y)\widehat{S_X}(y)dy-\int_{\eta-R}^{\eta+R}J(\eta-y)\widehat{S_X}(y)dy\right|\\ & = &d_1\Bigg(\left|\int_{\eta+R}^{\xi+R}J(\xi-y)\widehat{S_X}(y)dy\right|+\left|\int_{\xi-R}^{\eta-R}J(\xi-y)\widehat{S_X}(y)dy\right|\nonumber\\ &&\mbox{}+\left|\int_{\eta-R}^{\eta+R}[J(\xi-y)-J(\eta-y)]\widehat{S_X}(y)dy\right|\Bigg)\nonumber\\ &\leq&d_1(2S_0\|J\|_{L^\infty}+2RL_JS_0)|\xi-\eta| \end{eqnarray*} |
and
\begin{eqnarray} B_3 & = &|f(S_X(\xi))g(I_X(\xi-c\tau))-f(S_X(\eta))g(I_X(\eta-c\tau))|\\ &\leq&|f(S_X(\xi))||g(I_X(\xi-c\tau))-g(I_X(\eta-c\tau))|+|g(I_X(\eta-c\tau))||f(S_X(\xi))-f(S_X(\eta))|\\ &\leq&f(S_0)g'(0)|I_X(\xi)-I_X(\eta)|+L g'(0)K_{\varepsilon}|S_X(\xi)-S_X(\eta)|, \end{eqnarray} | (2.35) |
in which we have used the mean-value theorem, the assumptions (A1), (A2) and inequality (2.5). Combining (2.33), (2.34) and (2.35), there exists some positive constant L_1 independent of X such that
|S'_X(\xi)-S'_X(\eta)|\leq L_1|\xi-\eta| |
and so S'_X is Lipschitz continuous. It follows from (2.30) that
\begin{eqnarray*} &&c|I'_X(\xi)-I'_X(\eta)|\nonumber\\ &\leq&d_2\left|\int_{\mathbb{R}}J(y)[\widehat{I_X}(\xi-y)-\widehat{I_X}(\eta-y)]dy\right|+(d_2+\gamma)|I_X(\xi)-I_X(\eta)|\nonumber\\ &&\mbox{}+\varepsilon|I_X^2(\xi)-I_X^2(\eta)|+|f(S_X(\xi))g(I_X(\xi-c\tau))-f(S_X(\eta))g(I_X(\eta-c\tau))|.\nonumber \end{eqnarray*} |
Analogously, we have
|I'_X(\xi)-I'_X(\eta)|\leq L_1|\xi-\eta| |
and so I'_X is Lipschitz continuous. Thus, there is a constant C_1 independent of X such that
\|S_X(\xi)\|_{C^{1, 1}([-X, X])} < C_1, \quad \|I_X(\xi)\|_{C^{1, 1}([-X, X])} < C_1. |
This ends the proof.
Now, we are in a position to derive the existence of solutions for (2.1) on \mathbb{R} by a limiting argument.
Theorem 2.1. Let \mathcal{R}_0 = \frac{f(S_0)g'(0)}{\gamma} > 1. Then, for any c > c^* , (2.1) admits a solution (S(\xi), I(\xi)) such that
\begin{eqnarray} \underline{S}(\xi)\leq S(\xi)\leq S_0, \ \underline{I}(\xi)\leq I(\xi)\leq\overline{I}(\xi). \end{eqnarray} | (2.36) |
Proof. Choose a sequence \{X_n\}_{n = 1}^\infty satisfying
X_n > \max\left\{\frac{1}{\eta}\ln M, \frac{1}{\alpha}\ln\frac{\rho}{S_0}\right\} |
and \lim_{n\rightarrow +\infty}X_n = +\infty. Then, for each n\in\mathbb{N} , the solution (S_{X_n}(\xi), I_{X_n}(\xi))\in\Gamma_{X_n, \tau} satisfies Propositions 2.3 and 2.4, Eqs.(2.29) and (2.30) in \xi\in[-X_n-c\tau, X_n] for every c > c^* .
According to the estimates in Proposition 2.4, for the sequence \{(S_{X_n}(\xi), I_{X_n}(\xi))\} , we can extract a subsequence by a standard diagonal argument, denoted by \{(S_{X_{n_k}}(\xi), I_{X_{n_k}}(\xi))\}_{k\in\mathbb{N}} , such that
\begin{eqnarray} S_{X_{n_k}}(\xi)\rightarrow S(\xi), \ I_{X_{n_k}}(\xi)\rightarrow I(\xi) \ \text{in} \ C_{loc}^1(\mathbb{R})\ \text{as}\ k\rightarrow \infty \end{eqnarray} | (2.37) |
and
\begin{eqnarray} \left\{ \begin{array}{l} c S'_{X_{n_k}}(\xi) = d_1\int_{\mathbb{R}}J(y)\widehat{S_{X_{n_k}}}(\xi-y)dy-d_1S_{X_{n_k}}(\xi)-f(S_{X_{n_k}}(\xi))g(I_{X_{n_k}}(\xi-c\tau)), \xi\in[-{X_{n_k}}, {X_{n_k}}], \\ S_{X_{n_k}}(\xi) = \underline{S}(-{X_{n_k}}), \ \xi\in[-{X_{n_k}}-c\tau, -{X_{n_k}}] \end{array} \right. \end{eqnarray} | (2.38) |
with
\begin{eqnarray} \left\{ \begin{array}{l} c I'_{X_{n_k}}(\xi) = d_2\int_{\mathbb{R}}J(y)\widehat{I_{X_{n_k}}}(\xi-y)dy+f(S_{X_{n_k}}(\xi))g(I_{X_{n_k}}(\xi-c\tau))\\ \mbox{}\quad\quad\quad\quad\quad-(d_2+\gamma)I_{X_{n_k}}(\xi)-\varepsilon I^2_{X_{n_k}}(\xi), \ \xi\in[-{X_{n_k}}, {X_{n_k}}], \\ I_{X_{n_k}}(\xi) = \underline{I}(-{X_{n_k}}), \ \xi\in[-{X_{n_k}}-c\tau, -{X_{n_k}}] \end{array} \right. \end{eqnarray} | (2.39) |
and
\begin{eqnarray} \underline{S}(\xi)\leq S_{X_{n_k}}(\xi)\leq S_0, \ \underline{I}(\xi)\leq I_{X_{n_k}}(\xi)\leq\overline{I}(\xi), \ \xi\in(-{X_{n_k}}, {X_{n_k}}), \end{eqnarray} | (2.40) |
where \widehat{S_{X_{n_k}}}(\xi) and \widehat{I_{X_{n_k}}}(\xi) are defined analogously as the \widehat{\phi}(\xi) and \widehat{\varphi}(\xi) in (2.15) and (2.16), respectively. Sine J is compactly supported (see(A3)), by the Lebesgue dominated convergence theorem, one has
\begin{eqnarray} \lim\limits_{k\rightarrow +\infty}\int_{\mathbb{R}}J(y)\widehat{S_{X_{n_k}}}(\xi-y)dy & = &\lim\limits_{k\rightarrow +\infty}\int_{-R}^{R}J(y)\widehat{S_{X_{n_k}}}(\xi-y)dy\\ & = &\lim\limits_{k\rightarrow +\infty}\int_{\xi-R}^{\xi+R}J(\xi-y)\widehat{S_{X_{n_k}}}(y)dy\\ & = &\int_{\xi-R}^{\xi+R}J(\xi-y)S(y)dy\\ & = &\int_{\mathbb{R}}J(y)S(\xi-y)dy\\ & = &J*S(\xi), \quad \forall \xi\in\mathbb{R}. \end{eqnarray} | (2.41) |
Similarly, we can show that
\begin{eqnarray} \lim\limits_{k\rightarrow +\infty}\int_{\mathbb{R}}J(y)\widehat{I_{X_{n_k}}}(\xi-y)dy = \int_{\mathbb{R}}J(y)I(\xi-y)dy = J*I(\xi), \quad \forall \xi\in\mathbb{R}. \end{eqnarray} | (2.42) |
Furthermore, in light of the continuity of f and g , we obtain
\begin{eqnarray} \lim\limits_{k\rightarrow +\infty}f(S_{X_{n_k}}(\xi))g(I_{X_{n_k}}(\xi-c\tau)) = f(S(\xi))g(I(\xi-c\tau)), \quad \forall \xi\in\mathbb{R}. \end{eqnarray} | (2.43) |
Thus, passing to limits in (2.38), (2.39) and (2.40) as k\rightarrow +\infty , we derive from (2.37), (2.41)-(2.43) that (S(\xi), I(\xi)) satisfies (2.1) and (2.36). The proof of this theorem is finished.
In this section, we will prove the existence of traveling wave solutions of (1.3) satisfying (1.5).
Theorem 3.1. Let \mathcal{R}_0 = \frac{f(S_0)g'(0)}{\gamma} > 1. Then for any c\geq c^* , (1.3) admits a pair of functions (S(\xi), I(\xi)) such that
\begin{eqnarray} \underline{S}(\xi)\leq S(\xi)\leq S_0, \ \underline{I}(\xi)\leq I(\xi)\leq\overline{I}(\xi). \end{eqnarray} | (3.1) |
Proof. For c > c^* , let \{\varepsilon_n\} be a sequence such that 0 < \varepsilon_{n+1} < \varepsilon_n < 1 (n = 1, 2, 3, \cdots) and \lim_{n\rightarrow +\infty}\varepsilon_n = 0 . By Theorem 2.1 and Proposition 2.4, for each n\in\mathbb{N} , there exists a solution \Phi_n(\xi) = (S_n(\xi), I_n(\xi)) for \varepsilon = \varepsilon_n , such that
\begin{equation} \left \{ \begin{aligned} cS'_n(\xi)& = d_1(J*S_n(\xi)-S_n(\xi))-f(S_n(\xi))g(I_n(\xi-c\tau)), \\ cI'_n(\xi)& = d_2(J*I_n(\xi)-I_n(\xi))+f(S_n(\xi))g(I_n(\xi-c\tau))-\gamma I_n(\xi)-\varepsilon_n I_n^2(\xi) \end{aligned} \right. \end{equation} | (3.2) |
and
\begin{eqnarray} \underline{S}(\xi)\leq S_n(\xi)\leq S_0, \ \underline{I}(\xi)\leq I_n(\xi)\leq\overline{I}(\xi) \end{eqnarray} | (3.3) |
for all \xi\in\mathbb{R} .
Furthermore, we know that
\begin{eqnarray} \|S_n(\xi)\|_{C^{1, 1}(\mathbb{R})}+\|I_n(\xi)\|_{C^{1, 1}(\mathbb{R})} < C_3, \end{eqnarray} | (3.4) |
where C_3 is a positive constant independent of \xi . Then we can assert that \{\Phi_n(\xi)\} and \{\Phi'_n(\xi)\} are equi-continuous and uniformly bounded on \mathbb{R} . By the Arzelà-Ascoli theorem, there exists a subsequence of \{\varepsilon_n\} , still denoted by \{\varepsilon_n\} , such that \lim_{n\rightarrow \infty}\varepsilon_n = 0 and
\Phi_n(\xi)\rightarrow \Phi(\xi), \ \Phi'_n(\xi)\rightarrow \Phi'(\xi) |
uniformly on every closed bounded interval as n\rightarrow \infty , and hence pointwise on \mathbb{R} , where \Phi(\xi) = (S(\xi), I(\xi)) and \Phi'(\xi) = (S'(\xi), I'(\xi)) are bounded. Passing to the limits in (3.2) and (3.3) as n\rightarrow \infty and employing the dominated convergence theorem and the continuity of f and g (see (A1) and (A2)), we obtain that (S(\xi), I(\xi)) satisfies (1.3) and (3.1).
For c = c^* , one can choose a decreasing sequence \{c_n\}\in(c^*, c^*+1) such that \lim_{n\rightarrow \infty}c_n = c^* and the same reasoning applies to the above case c > c^* and \varepsilon_n\rightarrow0 . For simplicity, we omit the details. This ends the proof.
Next we aim at the asymptotic behavior of solution (S(\xi), I(\xi)) of (1.3), whose existence is guaranteed by Theorem 3.1. For \xi\in\mathbb{R} , invoking the Squeeze theorem to (3.1), we deduce the asymptotic behavior of solution (S(\xi), I(\xi)) at -\infty .
Proposition 3.1. Suppose that \mathcal{R}_0 = \frac{f(S_0)g'(0)}{\gamma} > 1 and c\geq c^* . Then the solution (S(\xi), I(\xi)) of (1.3) satisfies
\begin{eqnarray} S(-\infty) = S_0, \ I(-\infty) = 0 \end{eqnarray} | (3.5) |
and
\begin{eqnarray*} \lim\limits_{\xi\rightarrow -\infty}e^{-\lambda_1\xi}I(\xi) = 1. \end{eqnarray*} |
The following proposition shows the asymptotic behavior of I(\xi) at \infty .
Proposition 3.2. Assume that \mathcal{R}_0 = \frac{f(S_0)g'(0)}{\gamma} > 1 and c\geq c^* . Then the solution (S(\xi), I(\xi)) of (1.3) satisfies
\begin{eqnarray} 0 < \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi < \infty \end{eqnarray} | (3.6) |
with \int_{\mathbb{R}}I(\xi)d\xi < \infty and I(\infty) = 0.
Proof. Using (3.1), (A1), (A2) and the definitions of \underline{S}(\xi) and \underline{I}(\xi) , one has
\begin{eqnarray*} \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi\geq\int_{\mathbb{R}}f(\underline{S}(\xi))g(\underline{I}(\xi-c\tau))d\xi > 0. \end{eqnarray*} |
Note that
\begin{eqnarray*} &&\int_z^x(J*S(\xi)-S(\xi))d\xi\nonumber\\ & = &\int_z^x\int_{\mathbb{R}}J(y)(S(\xi-y)-S(\xi))dyd\xi\nonumber\\ & = &-\int_z^x\int_{\mathbb{R}}J(y)y\int_0^1S'(\xi-ty)dtdyd\xi\nonumber\\ & = &\int_{\mathbb{R}}J(y)y\int_0^1(S(z-ty)-S(x-ty))dtdy. \end{eqnarray*} |
Then, by(3.5) and (A3), we get
\begin{eqnarray*} &&\lim\limits_{z\rightarrow -\infty}\int_z^x(J*S(\xi)-S(\xi))d\xi\nonumber\\ & = &\int_{\mathbb{R}}J(y)y\int_0^1(S_0-S(x-ty))dtdy\nonumber\\ & = &-\int_{\mathbb{R}}J(y)y\int_0^1S(x-ty)dtdy, \end{eqnarray*} |
which implies that, for x\in\mathbb{R} ,
\begin{eqnarray} \left|\int_{-\infty}^x(J*S(\xi)-S(\xi))d\xi\right|\leq S_0\int_{\mathbb{R}}J(y)|y|dy: = \sigma_0, \end{eqnarray} | (3.7) |
where we used the fact that J is compactly supported (see(A3)). Taking an integration of the first equation in (1.3) over (-\infty, x) and using (3.5) and (3.7), we get
\begin{eqnarray*} &&\int_{-\infty}^{x}f(S(\xi))g(I(\xi-c\tau))d\xi\nonumber\\ & = &d_1\int_{-\infty}^{x}(J*S(\xi)-S(\xi))d\xi+cS_0-cS(x)\nonumber\\ &\leq&d_1\sigma_0+cS_0, \end{eqnarray*} |
which implies
\begin{eqnarray} \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi < \infty. \end{eqnarray} | (3.8) |
Similar to the proof of (3.7), we have
\begin{eqnarray} \left|\int_{\mathbb{R}}(J*I(\xi)-I(\xi))d\xi\right|\leq K_{\varepsilon}\int_{\mathbb{R}}J(y)|y|dy: = \sigma_1. \end{eqnarray} | (3.9) |
Taking an integration of the second equation in (1.3) over \mathbb{R} gives
\begin{eqnarray} &&cI(+\infty)+\gamma\int_{\mathbb{R}}I(\xi)d\xi\\ & = &d_2\int_{\mathbb{R}}(J*I(\xi)-I(\xi))d\xi+\int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi\\ & < &\infty, \end{eqnarray} | (3.10) |
where we have used (3.8) and (3.9).
Consequently, it follows from (3.10) that
\begin{eqnarray*} \int_{\mathbb{R}}I(\xi)d\xi < \infty. \end{eqnarray*} |
Upon combining with the fact that I'(\xi) is bounded on \mathbb{R} (see (3.4)), we have
\begin{eqnarray} I(+\infty) = 0. \end{eqnarray} | (3.11) |
This completes the proof.
The following proposition deals with the asymptotic behavior of S(\xi) at \infty .
Proposition 3.3. Assume that \mathcal{R}_0 = \frac{f(S_0)g'(0)}{\gamma} > 1 and c\geq c^* . Then (1.3) has a solution (S(\xi), I(\xi)) such that \lim_{\xi\rightarrow +\infty}S(\xi) exists and
\begin{eqnarray*} \lim\limits_{\xi\rightarrow +\infty}S(\xi): = S_\infty < S_0. \end{eqnarray*} |
Moreover, there holds
\begin{eqnarray*} \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi = \gamma\int_{\mathbb{R}}I(\xi)d\xi = c(S_0-S_\infty). \end{eqnarray*} |
Proof. We prove the existence of \lim_{\xi\rightarrow +\infty}S(\xi) by a contradiction argument. Suppose
\begin{eqnarray*} \lim\limits_{\xi\rightarrow +\infty}\sup S(\xi) > \lim\limits_{\xi\rightarrow +\infty}\inf S(\xi) \end{eqnarray*} |
for a contrary. Then from Fluctuation Lemma (see Lemma 2.2 in [1]), we infer that there exists a sequence \{\xi_n\} satisfying \xi_n\rightarrow \infty as n\rightarrow \infty such that
\begin{eqnarray} \lim\limits_{n\rightarrow \infty}S(\xi_n) = \lim\limits_{\xi\rightarrow +\infty}\sup S(\xi): = \sigma_2\ \text{and}\ S'(\xi_n) = 0. \end{eqnarray} | (3.12) |
Meanwhile, there exists another sequence \{\eta_n\} satisfying \eta_n\rightarrow \infty as n\rightarrow \infty such that
\begin{eqnarray} \lim\limits_{n\rightarrow \infty}S(\eta_n) = \lim\limits_{\xi\rightarrow +\infty}\inf S(\xi): = \sigma_3 < \sigma_2 \ \text{and}\ S'(\eta_n) = 0. \end{eqnarray} | (3.13) |
Following from the first equation in (1.3), we have
\begin{eqnarray} cS'(\xi_n) = d_1(J*S(\xi_n)-S(\xi_n))-f(S(\xi_n))g(I(\xi_n-c\tau)). \end{eqnarray} | (3.14) |
Passing to the limits in (3.14) as n\rightarrow \infty , and using (3.11), (3.12) and (A2), we obtain
\begin{eqnarray} \lim\limits_{n\rightarrow \infty}J*S(\xi_n) = \lim\limits_{n\rightarrow \infty}S(\xi_n) = \sigma_2. \end{eqnarray} | (3.15) |
Set
\begin{eqnarray} S_n(y) = S(\xi_n-y). \end{eqnarray} | (3.16) |
We will show that \lim_{n\rightarrow \infty}S_n(y)\rightarrow \sigma_2 for y\in supp J: = \Omega . Take sufficiently small \varepsilon_1 > 0 and let
\begin{eqnarray} \Omega_{\varepsilon_1} = \Omega\bigcap\Big\{y\in\Omega|\lim\limits_{n\rightarrow \infty}S_n(y) < \sigma_2-\varepsilon_1\Big\}. \end{eqnarray} | (3.17) |
Then from (3.12), (3.15)-(3.17) and (A3) we get
\begin{eqnarray*} \sigma_2 & = &\lim\limits_{n\rightarrow \infty}J*S(\xi_n)\nonumber\\ & = &\lim\limits_{n\rightarrow \infty}\int_{\Omega}J(y)S(\xi_n-y)dy\nonumber\\ & = &\lim\limits_{n\rightarrow \infty}\int_{\Omega}J(y)S_n(y)dy\nonumber\\ &\leq&\lim\limits_{n\rightarrow \infty}\sup\int_{\Omega\backslash{\Omega_{\varepsilon_1}}}J(y)S_n(y)dy+\lim\limits_{n\rightarrow \infty}\sup\int_{\Omega_{\varepsilon_1}}J(y)S_n(y)dy\nonumber\\ &\leq&\sigma_2\int_{\Omega\backslash{\Omega_{\varepsilon_1}}}J(y)dy+(\sigma_2-\varepsilon_1)\int_{\Omega_{\varepsilon_1}}J(y)dy\nonumber\\ & = &\sigma_2-\varepsilon_1\int_{\Omega_{\varepsilon_1}}J(y)dy, \end{eqnarray*} |
which shows that m(\Omega_{\varepsilon_1}) = 0 , where m(\cdot) denotes the measure. Therefore, we have \lim_{n\rightarrow \infty}S_n(y) = \sigma_2 almost everywhere in \Omega .
However, since \{S_n\} is an equi-continuous family, the convergence is everywhere in \Omega , that is,
\begin{eqnarray} \lim\limits_{n\rightarrow \infty}S_n(y) = \lim\limits_{n\rightarrow \infty}S(\xi_n-y) = \sigma_2, \ y\in\Omega. \end{eqnarray} | (3.18) |
Using the similar arguments, we can prove that
\begin{eqnarray} \lim\limits_{n\rightarrow \infty}S(\eta_n-y) = \sigma_3 < \sigma_2, \ y\in\Omega. \end{eqnarray} | (3.19) |
Integrating two sides of the first equation in (1.3) from \eta_n to \xi_n , using (3.12), (3.13), (3.18), (3.19) and the fact that
\lim\limits_{n\rightarrow \infty}\int_{\eta_n}^{\xi_n}f(S(\xi))g(I(\xi-c\tau))d\xi = 0, |
we get
\begin{eqnarray*} 0 < c(\sigma_2-\sigma_3) & = &c\lim\limits_{n\rightarrow \infty}(S(\xi_n)-S(\eta_n))\nonumber\\ & = &d_1\lim\limits_{n\rightarrow \infty}\int_{\eta_n}^{\xi_n}(J*S(\xi)-S(\xi))d\xi-\lim\limits_{n\rightarrow \infty}\int_{\eta_n}^{\xi_n}f(S(\xi))g(I(\xi-c\tau))d\xi\nonumber\\ & = &d_1\lim\limits_{n\rightarrow \infty}\int_{\eta_n}^{\xi_n}\int_{\mathbb{R}}J(y)(S(\xi-y)-S(\xi))dyd\xi\nonumber\\ & = &d_1\lim\limits_{n\rightarrow \infty}\int_{\eta_n}^{\xi_n}\int_{\mathbb{R}}J(y)(-y)\int_{0}^{1}S'(\xi-ty)dtdyd\xi\nonumber\\ & = &d_1\lim\limits_{n\rightarrow \infty}\int_{\mathbb{R}}J(y)y\int_{0}^{1}(S(\eta_n-ty)-S(\xi_n-ty))dtdy\nonumber\\ & = &0, \end{eqnarray*} |
which leads to a contradiction. Thus, \lim_{\xi\rightarrow \infty}\sup S(\xi) = \lim_{\xi\rightarrow \infty}\inf S(\xi) and so \lim_{\xi\rightarrow \infty}S(\xi): = S_\infty exists.
Next, we will prove that S_\infty < S_0 . Since S(\xi)\leq S_0 , we have S_\infty\leq S_0 . Assume that S_\infty = S_0 . Then it follows from (3.5) that
\begin{eqnarray} S(-\infty) = S_\infty = S_0. \end{eqnarray} | (3.20) |
Taking an integration of the first equation in (1.3) over \mathbb{R} yields
\begin{eqnarray} &&c(S_\infty-S(-\infty))\\ & = &d_1\int_{\mathbb{R}}(J*S(\xi)-S(\xi))d\xi-\int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi\\ & = &d_1\left(\int_{\mathbb{R}}\int_{\mathbb{R}}J(y)S(\xi-y)dyd\xi-\int_{\mathbb{R}}S(\xi)d\xi\right)-\int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi. \end{eqnarray} | (3.21) |
By Fubini's theorem and (A3), one has
\begin{eqnarray} &&\int_{\mathbb{R}}\int_{\mathbb{R}}J(y)S(\xi-y)dyd\xi-\int_{\mathbb{R}}S(\xi)d\xi\\ & = &\int_{\mathbb{R}}J(y)\left(\int_{\mathbb{R}}S(\xi-y)d\xi\right)dy-\int_{\mathbb{R}}S(\xi)d\xi\\ & = &\int_{\mathbb{R}}J(y)\left(\int_{\mathbb{R}}S(\xi)d\xi\right)dy-\int_{\mathbb{R}}S(\xi)d\xi\\ & = &\int_{\mathbb{R}}S(\xi)d\xi-\int_{\mathbb{R}}S(\xi)d\xi\\ & = &0. \end{eqnarray} | (3.22) |
From (3.20)-(3.22), we obtain \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi = 0, which contradicts (3.6). Thus, we have
S_\infty < S_0 |
and
\begin{eqnarray} \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi = c(S_0-S_\infty). \end{eqnarray} | (3.23) |
Moreover, integrating two sides of the second equation in (1.3) on \mathbb{R} and recalling that I(\pm\infty) = 0 , one has
\begin{eqnarray} 0 = d_2\int_{\mathbb{R}}[J*I(\xi)-I(\xi)]d\xi+\int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi-\int_{\mathbb{R}}\gamma I(\xi)d\xi. \end{eqnarray} | (3.24) |
Using the Fubini theorem again and repeating the above procedures, we can obtain \int_{\mathbb{R}}[J*I(\xi)-I(\xi)]d\xi = 0 . It follows from (3.23) and (3.24) that
\gamma\int_{\mathbb{R}} I(\xi)d\xi = \int_{\mathbb{R}}f(S(\xi))g(I(\xi-c\tau))d\xi = c(S_0-S_\infty). |
The proof of this proposition is completed.
Finally, combining Theorem 3.1 and Propositions 3.1-3.3, we obtain the existence of traveling wave solutions for system (1.2) satisfying (1.5).
Theorem 3.2. Assume that \mathcal{R}_0 = \frac{f(S_0)g'(0)}{\gamma} > 1 and c\geq c^* . Then system (1.2) admits a nontrivial and nonnegative traveling wave solution (S(x+ct), I(x+ct)) satisfying (1.5).
Remark 3.1
(i) It is worth to point out that, in this paper, we have derived the existence of traveling wave solutions in the absence of assumption (H) and further obtained Theorem 3.2 to confirm that c^* is the minimal wave speed of the existence of traveling wave solutions for (1.2), which improves Theorem 1.1 ([18,Theorem 2.7]).
(ii) Theorem 3.2 states that Theorem 1.2 ([19,Theorem 2.3]) still holds if we take the influences of delays into consideration.
(iii) In (1.1), the choice of f(S) = \beta S and g(I) = I leads to the model investigated by Cheng and Yuan[17]. Thus, Theorem 3.2 includes Theorem 3.2 of [17] as a special case.
In this section, we will give a typical example to demonstrate the abstract results presented in Section 3. The choice of f(S) = S and g(I) = \frac{\beta I}{1+\alpha I} (\alpha, \beta > 0 are two coefficients) in (1.2) leads to
\begin{equation} \left \{ \begin{aligned} \frac{\partial S(x, t)}{\partial t}& = d_1(J*S(x, t)-S(x, t))-\beta S(x, t)\frac{I(x, t-\tau)}{1+\alpha I(x, t-\tau)}, \\ \frac{\partial I(x, t)}{\partial t}& = d_2(J*I(x, t)-I(x, t))+\beta S(x, t)\frac{I(x, t-\tau)}{1+\alpha I(x, t-\tau)}-\gamma I(x, t).\\ \end{aligned} \right. \end{equation} | (4.1) |
Obviously, it is easy to verify that f(S) and g(I) satisfy assumptions (A1)-(A2). Applying Theorem 3.2, we obtain the following result.
Theorem 4.1. There exists a positive constant c^* such that if \mathcal{R}_0 = \frac{\beta S_0}{\gamma} > 1 and c\geq c^* . Then system (4.1) admits a nontrivial and nonnegative traveling wave solution (S(x+ct), I(x+ct)) satisfying
\begin{equation} S(-\infty) = S_0, \ S(+\infty) = S_\infty < S_0, \ I(\pm\infty) = 0. \end{equation} | (4.2) |
We further show that the minimal wave speed c^* is determined by the following system:
\Delta(\lambda, c) = 0 \quad\text{and}\quad \frac{\partial\Delta(\lambda, c)}{\partial\lambda} = 0, \quad\text{for} \lambda > 0, c > 0, |
where
\Delta(\lambda, c): = d_2\int_{\mathbb{R}}J(y)(e^{-\lambda y}-1)dy-c\lambda+\beta S_0e^{-\lambda c\tau}-\gamma. |
It is noticed that the minimal wave speed c^* is relevant to the dispersal rate d_2 and the delay \tau . Due to \Delta(\lambda^*, c^*) = 0 , by the implicit function theorem, a direct calculation gives
\frac{dc^*}{dd_2} = \frac{\int_{\mathbb{R}}J(y)e^{-\lambda^* y}dy-1}{\lambda^*+\lambda^*\tau \beta S_0e^{-\lambda^* c^*\tau}} > 0, |
which implies that the geographical movement of infected individuals can increase the speed of the spread of disease. Similarly, we have
\frac{dc^*}{d\tau} = \frac{-c^*\beta S_0e^{-\lambda^* c^*\tau}}{1+\beta\tau S_0e^{-\lambda^* c^*\tau}} < 0. |
That is, the longer the delay \tau , the slower the spreading speed.
It is known that the existence and non-existence of the traveling wave solution to nonlinear partial equations have been investigated extensively since they can predict whether or not the disease spread in the individuals and how fast a disease invades geographically. In the present paper, we have studied the traveling wave solutions for a delayed nonlocal dispersal SIR epidemic model with the critical wave speed. It has been found that the existence of traveling wave solutions are totally determined by the basic reproduction number and the minimal wave speed c^* . More precisely, if \mathcal{R}_0 > 1 and c\geq c^* , then system (1.2) admits a nontrivial and nonnegative traveling wave solution (S(x+ct), I(x+ct)) satisfying (1.5). Results on this topic may help one predict how fast a disease invades geographically, and accordingly, take measures in advance to prevent the disease, or at least decrease possible negative consequences. The approaches applied in this paper have prospects for the study of the existence and non-existence of traveling wave solutions for nonlocal dispersal epidemic models with more general nonlinear incidences. Finally, we remark that there are quite a few spaces to deserve further investigations. For example, we can study the asymptotic speed of propagation, the uniqueness and stability of traveling wave solutions. Moreover, the exact boundary behavior of susceptible S(\xi) at +\infty is not obtained although the existence of S(+\infty) is established. We leave these problems for future work.
This work was supported by the National Natural Science Foundation of China (11701460), the Natural Science Foundation of Sichuan Provincial Education Department (Grant No. 18ZB0581), the Meritocracy Research Funds of China West Normal University (17YC373), the research startup foundation of China West Normal University (Grant No. 18Q060) and the Research and Innovation Team of China West Normal University (CXTD2020-5).
The authors declare there is no conflict of interest.
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