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Research article

Synchronization analysis of coupled fractional-order neural networks with time-varying delays

  • In this paper, the complete synchronization and Mittag-Leffler synchronization problems of a kind of coupled fractional-order neural networks with time-varying delays are introduced and studied. First, the sufficient conditions for a controlled system to reach complete synchronization are established by using the Kronecker product technique and Lyapunov direct method under pinning control. Here the pinning controller only needs to control part of the nodes, which can save more resources. To make the system achieve complete synchronization, only the error system is stable. Next, a new adaptive feedback controller is designed, which combines the Razumikhin-type method and Mittag-Leffler stability theory to make the controlled system realize Mittag-Leffler synchronization. The controller has time delays, and the calculation can be simplified by constructing an appropriate auxiliary function. Finally, two numerical examples are given. The simulation process shows that the conditions of the main theorems are not difficult to obtain, and the simulation results confirm the feasibility of the theorems.

    Citation: Biwen Li, Xuan Cheng. Synchronization analysis of coupled fractional-order neural networks with time-varying delays[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14846-14865. doi: 10.3934/mbe.2023665

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  • In this paper, the complete synchronization and Mittag-Leffler synchronization problems of a kind of coupled fractional-order neural networks with time-varying delays are introduced and studied. First, the sufficient conditions for a controlled system to reach complete synchronization are established by using the Kronecker product technique and Lyapunov direct method under pinning control. Here the pinning controller only needs to control part of the nodes, which can save more resources. To make the system achieve complete synchronization, only the error system is stable. Next, a new adaptive feedback controller is designed, which combines the Razumikhin-type method and Mittag-Leffler stability theory to make the controlled system realize Mittag-Leffler synchronization. The controller has time delays, and the calculation can be simplified by constructing an appropriate auxiliary function. Finally, two numerical examples are given. The simulation process shows that the conditions of the main theorems are not difficult to obtain, and the simulation results confirm the feasibility of the theorems.



    Understanding human displacement is very important since it influences the population's dynamic. In light of the recent COVID-19 epidemic outbreak, human travel is critical to understand how a virus spreads at the scale of a city, a country, and the scale of the earth, see [1,2]. Human mobility is also essential to understand and quantify the changes in social behavior.

    The spatial motion of populations is sometimes modeled using Brownian motion and diffusion equations at the population scale. For instance, reaction-diffusion equations are widely used to model the spatial invasion of populations both in ecology and epidemiology. We refer for instance to Cantrell and Cosner [3], Cantrell, Cosner and Ruan [4], Murray [5], Perthame [6], Roques [7] and the references therein. In particular, the spatial propagation for the solutions of reaction-diffusion equations has been observed and studied in the 30s by Fisher [8], and Kolmogorov, Petrovski, and Piskunov [9]. Diffusion is a good representation of the process of invasion or colonization for humans and animals. Nevertheless, once the population is established, the return to home process (i.e., diffusion combined with a return to home) seems to be more adapted to the daily life of humans.

    This article aims at describing short-distance human mobility. We aim at considering the local human movement at the scale of the city with a special focus on how to model the returning home of humans. The return to home behavior is also very important in ecology, agriculture and fisheries. In agriculture, a return to home model for bees has been proposed and studied in [10,11].

    Roughly speaking, we can classify the human movement into 1) short-distance movement: working, shopping, and other activities; 2) long-distance movement: intercity travels, plane, train, cars, etc. These considerations have been developed recently in [12,13,14,15]. A global description of the human movement has been proposed (by extending the idea of the Brownian motion) by considering the Lévy flight process. The long-distance movement can be covered by using patch models (see Cosner et al. [16] for more results about this).

    In Section 2, we present the return to home model. Section 3 explores some properties of the return to home model. Section 4 presents a semi-explicit formula for the solutions of the return to home model. Section 5 studies the equilibrium distributions. Section 6 develops a functional framework to understand the mild solutions for the return to home model. Section 7 is devoted to an extension of the Fisher KPP model with return to home and colonization. By analogy to the Fisher KPP problem, we obtain a monotone semiflow. The last section is devoted to an epidemic model with return to home.

    In this section we describe a model for the movement of individuals within a city. To simplify the presentation, we consider a population moving in the whole plane R2. Our goal is to focus on the most important processes involved in the model. The presentation would be more complicated for a model in a bounded set taking into account boundary conditions.

    Let yu(t,y) be the distribution of population of individual staying at home at time t. This means that the quantity

    ωu(t,y)dy

    represents the number of individuals staying at home in the sub-region ωR2 at time t.

    Let xvy(t,x)=v(t,x,y) be the distribution of a population of individuals out of the house (called travelers) and originated from their home located at yR2. Here originated from yR2 means that their home is located (or possibly centered at y according to the spatial scale) at the position y. Then the quantity

    ωv(t,x,y)dx

    is the number of individuals (originated from the home located at yR2) and traveling in the sub-region ωR2 at time t.

    Model for individuals staying at home: The following equation describes the flux between individuals staying at home at the location yR2 and individuals out of the house

    tu(t,y)=αR2v(t,x,y)dxFlow of individuals returning homeγu(t,y)Flow of individuals leaving home (2.1)

    where 1/α>0 is the average time spent by individuals out of the home, and 1/β>0 is the average time spent by individuals at home.

    Model for travelers (people who are not staying at home): The following equation describes the flux between individuals staying at home at the location y and the travelers

    tv(t,x,y)=ε2xv(t,x,y)Brownian motionαv(t,x,y)Flow of individuals returning home+γρ(xy)u(t,y)Flow of individuals leaving home, (2.2)

    where x denotes the Laplace operator for the the variable x=(x1,x2)R2, that is

    x=2x1+2x2.

    In Eq (2.2), ε>0 is the diffusivity of the travelers. The map xρ(xy) is a Gaussian distribution representing the location of a house centered at the position yR2. Here by a Gaussian distribution centered at 0 we mean that the function ρ is given by

    ρ(x)=12πσ2ex21+x222σ2,x=(x1,x2), (2.3)

    for some σ2>0, so that the covariance matrix is given by the diagonal matrix σ2I2. Note also that for all yR2, the translated map ρ(y) satisfies

    R2ρ(xy)dx=1 and R2xρ(xy)dx=y.

    Initial condition: Systems (2.1) and (2.2) is complemented with some initial distributions

    u(0,y)=u0(y) and v(0,x,y)=v0(x,y). (2.4)

    Remark 2.1. Here in the model we have neglected a possible convection term describing for instance the transport of individuals from their home to their workplace, shopping mall etc. Such a convection term describes the tendency of individuals to start moving from their home location y:

    tv(t,x,y)=ε2xv(t,x,y)+(cy(x)v(t,x,y))convectionαv(t,x,y)+γρ(xy)u(t,y),

    where cy(x) is the travel speed at location x. This speed may depend on the location of the house y to distinguish the individual's origin in the city.

    Remark 2.2. If we formally replace u(t,y) by αγR2v(t,x,y)dx in the v-equation (see (2.2)) of the model, we obtain the following single non local equation

    tv(t,x,y)=ε2xv(t,x,y)+αρ(xy)R2v(t,x,y)dxαv(t,x,y).

    Indeed, replacing u(t,y) by αγR2v(t,x,y)dx corresponds to slow-fast system idea. Under suitable assumptions on the parameters (i.e. α=ˆα/ε and γ=ˆγ/ε with ε>0 small enough), we can formally assume that u(t,y)=αγR2v(t,x,y)dx.

    The total distribution of population originated from a house located at yR2 is

    w(t,x,y)=v(t,x,y)+u(t,y)ρ(xy).

    By integrating the distribution with respect to y (homes locations), we obtain the total distribution of population in space (including both individuals staying at home or travelers)

    ¯w(t,x)=R2w(t,x,y)dy.

    The map yh(t,y)=u(t,y)+R2v(t,x,y)dx is the density of individuals per home at time t. That is to say that

    ωh(t,y)dy

    is the total number of individuals (staying at home or traveling) with their home in the sub-region ωR2 at time t. Data representing this distribution is usually available. This is true for example in USA [17] where such a distribution is known per county (a home is identified to a county).

    If we neglect the newborns and the deaths, and people moving house, we can assume that h(t,y)=h0(y) is independent of time. Let yh0(y) be the spatial distribution of individuals with their home located at the position y. This means that

    ωh0(y)dy

    denotes the number of people having their home in the region ωR2.

    In that case, we obtain

    h0(y)=u(t,y)+R2v(t,x,y)dx, (3.1)

    or equivalently

    R2v(t,x,y)dx=h0(y)u(t,y),t0. (3.2)

    By using Eqs (2.1) and (2.2), we have

    th(t,y)=t[u(t,y)+R2v(t,x,y)dx]=0,

    Therefore the solution (2.1) and (2.2) satisfies (3.1).

    By replacing the formula (3.2) in the u-equation (2.1), we obtain

    tu(t,y)=αh0(y)(α+γ)u(t,y),

    and we deduce an explicit form for the solutions

    u(t,y)=u0(y)e(α+γ)t+αα+γ(1e(α+γ)t)h0(y). (4.1)

    In order to derive an explicit formula for v(t,x,y) we define the two-dimensional heat kernel K:(0,)×R2R be

    K(t,x)=14πε2te|x|24ε2t,

    and we will use the simplifying assumption (2.3).

    Lemma 4.1. Assume that ρ(x) is a Gaussian distribution (2.3). Then there exists t0>0 such that

    ρ(x)=K(t0,x).

    Moreover the convolution of ρ with the kernel K(t,.) satisfies

    R2K(t,xz)ρ(zy)dz=K(t+t0,xy). (4.2)

    Proof. Note that (2.3) rewrites as

    ρ(x)=K(t0,x) with t0=σ22ε2.

    We deduce that

    R2K(t,xz)ρ(zy)dz=R2K(t,xyz)ρ(z)dz=R2K(t,xyz)K(t0,z)dz

    and the result follows by the semigroup property of the diffusion semigroup.

    Define

    Tε2xαI(t)(v(.,y))(x)=eαtR2K(t,xz)v(z,y)dz,

    and

    Tε2x(t)(v(.,y))(x)=R2K(t,xz)v(z,y)dz,

    where

    K(t,x)=14πε2te|x|24ε2t.

    We obtain

    v(t,x,y)=Tε2xαI(t)(v0(.,y))(x)+t0Tε2xαI(tσ)(γρ(.y)u(σ,y))(x)dσ.

    Next, by replacing the explicit formula (4.1) for u(t,y) in the above formula, we obtain

    v(t,x,y)=(v1+v2+v3)(t,x,y), (4.3)

    with

    v1(t,x,y)=eαtR2K(t,xz)v0(z,y)dz, (4.4)
    v2(t,x,y)=t0eα(tσ)Tε2x(tσ)(γρ(.y)[u0(y)αα+γh0(y)]e(α+γ)σ)(x)dσ,

    and

    v3(t,x,y)=t0eα(tσ)Tε2x(tσ)(γρ(.y)αα+γh0(y))(x)dσ.

    By using the above formula for v2, we obtain

    v2(t,x,y)=γeαtt0eγσTε2x(tσ)(ρ(.y))(x)dσ[u0(y)αα+γh0(y)],

    and by using Lemma 4.1 and formula (4.2), we deduce that

    v2(t,x,y)=eαtt0eγσK(t+t0σ,xy)dσγ[u0(y)αα+γh0(y)]. (4.5)

    By using the expression for v3, we obtain

    v3(t,x,y)=t0eα(tσ)K(t+t0σ,xy)dσγαα+γh0(y).

    By using the change of variable ˆσ=tσ we obtain

    v3(t,x,y)=t0αeαˆσK(ˆσ+t0,xy)dˆσγα+γh0(y). (4.6)

    Therefore, combining Eqs (4.3)–(4.6), we obtain an explicit formula for v(t,x,y).

    The equilibrium distribution must satisfy the following coupled system of equations

    0=αR2¯v(x,y)dxγ¯u(y), (5.1)

    and

    0=ε2x¯v(x,y)α¯v(x,y)+γρ(xy)¯u(y). (5.2)

    The first Eq (5.1) provides

    ¯u(y)=αγR2¯v(x,y)dx. (5.3)

    By using in (5.2) we obtain

    ¯v(x,y)=(αIε2x)1(ρ(.y)γ¯u(y))

    and since ¯u(y) is independent of x, we obtain

    ¯v(x,y)=(αIε2x)1(ρ(.y))γ¯u(y).

    and

    (αIε2x)1(ρ(.y))(x)=0eαtR2K(t,xz)ρ(zy)dzdt=0eαtK(t+t0,xy)dt.

    Define now

    χ(z)=γ0eαtK(t+t0,z)dt,

    so that we get

    ¯v(x,y)=χ(xy)¯u(y). (5.4)

    Remark 5.1. We observe that

    R2χ(xy)dx=γα.

    Therefore by using (3.1) we obtain

    h0(y)=[1+γα]¯u(y)¯u(y)=αα+γh0(y).

    Recall that the return to home model reads as follows

    {tu(t,y)=αR2v(t,x,y)dxγu(t,y),tv(t,x,y)=ε2xv(t,x,y)αv(t,x,y)+γρ(xy)u(t,y). (6.1)

    In the above formulation of the return to home model x denotes the position of travelers and their home located at y. So we use an Eulerian system of coordinate for x that is independent of the home location y. Instead, we can consider the spatial location

    z=xy

    which can be regarded as a Lagrangian system of coordinate for z centered at the home location y. By using the change of variables

    w(t,z,y)=v(t,z+y,y)w(t,xy,y)=v(t,x,y)

    we obtain the following system of equations

    {tu(t,y)=γu(t,y)+αR2w(t,z,y)dz,tw(t,z,y)=ε2zw(t,z,y)αw(t,z,y)+γρ(z)u(t,y). (6.2)

    In order to understand our choice of Banach spaces, one may observe that the term in (6.1), which is a typical term,

    γρ(xy)u(t,y)

    becomes in (6.2)

    γρ(z)u(t,y)

    when rewritten with the variable z=xy. In this system of coordinate, the two variables z and y are mostly uncoupled. This explain our choice of the Banach Y2 below.

    We consider BUC(R2) the space of bounded and uniformly continuous maps from R2 to R, which is a Banach space endowed with the supremum norm

    u=supyR2|u(y)|.

    The space Y1: We define

    Y1=BUC(R2,L1(R2)),

    The space Y1 becomes a Banach space when it is endowed with the norm

    vY1=supxR2v(x,.)L1(R2)=supxR2R2|v(x,y)|dy.

    The space Y2: We also define Y2 to be the space of maps (x,y)v(x,y) such that the function w(x,y)=v(x+y,y) satisfies

    xw(x,.)L1(R2,BUC(R2)).

    That is also equivalent to say that Y2 is the space of maps (x,y)w(xy,y) such that

    xw(x,.)L1(R2,BUC(R2)).

    Therefore Y2 is a Banach space endowed with the norm

    vY2=R2supyR2|v(x+y,y)|dx.

    Note that the maps in Y2 enjoy the following property.

    Lemma 6.1. Let vY2 be given. Then the map

    yR2v(x,y)dx

    is bounded and uniformly continuous on R2.

    Proof. By construction, the map

    yR2v(x+y,y)dx

    is bounded and uniformly continuous. But by using a change of variable we deduce that

    R2v(x+y,y)dx=R2v(x,y)dx.

    Hence the map

    yR2v(x,y)dx

    is bounded and uniformly continuous.

    Let (Z,.Z) be a Banach space. Let us consider the semigroup generated by the heat equation on Y=BUC(R2,Z) given by

    T(t)φ(x)=R2K(t,xx)φ(x)dx,φBUC(R2,Z).

    Here recall that the function K is the two-dimensional heat kernel. Now we will prove the following lemma. We refer to [18] for more results on vector valued elliptic operators and related evolution problems.

    Lemma 6.2. The semigroup {T(t)} is strongly continuous in Y=BUC(R2,Z).

    Proof. First observe that

    T(t)φYφY,t>0,φY.

    To prove the strong continuity of T(t) we fix φY. We have

    T(t)φ(x)φ(x)=R2K(t,xx)φ(x)dxφ(x)=R2K(t,z)[φ(xz)φ(x)]dz.

    Let ε>0 be given. By using the fact that xφ(x) is uniformly continuous, we deduce that there exists η>0 such that

    φ(xz)φ(x)ε/2,x,zR2, whenever |z|η,

    and we have

    T(t)(ϕ)(x)ϕ(x)Z|z|ηK(t,z)ϕ(xz)ϕ(x)Zdz+|z|ηK(t,z)ϕ(xz)ϕ(x)Zdz.

    Hence

    T(t)ϕ(x)ϕ(x)Zε/2+2φZ|z|ηK(t,z)dz,

    and since

    limt0|z|ηK(t,z)=0,

    the result follows.

    We consider the heat semigroup on Y=L1(R2,Z) given by

    T(t)φ(x)=R2K(t,xx)φ(x)dx,φL1(R2,Z).

    We have the following lemma.

    Lemma 6.3. The semigroup {T(t)} is strongly continuous in Y=L1(R2,Z).

    Proof. First observe that

    T(t)φYφY,t>0,φY.

    To prove the strong continuity of T(t) we fix φY. Next fix ϵ>0 and note that there exists ϕCc(R2;Z) (compactly supported) such that

    R2φ(x)ϕ(x)Zdxε/4.

    Next one has for all t>0

    T(t)φφ=T(t)(φϕ)(φϕ)+[T(t)ϕϕ],

    and

    T(t)φφYε/2+T(t)ϕϕY,

    and the result follows by using similar argument than in the proof of Lemma 6.2.

    Now assume that vY2. Then v can be written as

    v(x,y)=w(xy,y)

    for some

    xw(x,.)L1(R2,BUC(R2)).

    We obtain that

    T(t)v(x,y)=R2K(t,xx)v(x,y)dx,=R2K(t,xx)w(xy,y)dx,=R2K(t,xzy))w(z,y)dz,

    therefore

    T(t)v(x+y,y)=R2K(t,xz))w(z,y)dz,
    T(t)vY2=R2supyR|T(t)v(x+y,y)|dx=T(t)wL1(R2,BUC(R2)).

    So by using the same arguments as in the proof of Lemma 6.3 combine with the above observations, we deduce the following lemma.

    Lemma 6.4. The semigroup {T(t)} is strongly continuous in Y2.

    Let us consider a non-autonomous perturbation of the return to home model

    {tu(t,y)=γu(t,y),tv(t,x,y)=ε2xv(t,x,y)αv(t,x,y), (6.3)

    with the initial distribution

    {u(0,y)=u0(y)BUC(R2),andv(0,x,y)=v0(x,y)Y1Y2. (6.4)

    We define the state space

    X=BUC(R2)×Y1Y2,

    which becomes a Banach space when it is endowed with the standard product norm X given by

    (u,v)X=u+vY1+vY2.

    Then the following lemma holds.

    Lemma 6.5. The family of linear operators defined for t0 by

    TA(t)(uv)=(eγtueαtR2K(t,xz)v(z,y)dz) (6.5)

    defines a strongly continuous semigroup of bounded linear operators on (X,.X).

    We define the linear operators B1,B2:BUC(R2)×Y1Y2BUC(R2)×Y1Y2 by

    B1(uv)=(ϕ0),B2(uv)=(0ψ) with {ϕ(y)=αR2v(x,y)dxψ(x,y)=γρ(xy)u(y).

    Note that ϕBUC(R2) due to Lemma 6.1. Next the following lemma holds true.

    Lemma 6.6. The linear operators B1 and B2 are bounded on (X,X).

    Proof. Let uBUC(R2) and vY1Y2. As noticed above, from Lemma 6.1 and since vY2, we know that the map yαR2v(x,y)dx is bounded and uniformly continuous. Moreover, we have

    supyR2|R2v(x,y)dx|=supyR2|R2v(x+y,y)dx|R2supyR2|v(x+y,y)|dx=vY2,

    while

    ψY1=supxR2|R2ρ(xy)u(y)dy|=supxR2|R2ρ(z)u(xz)dz|R2ρ(z)dzsupxR2|u(x)|,

    and

    ρ(xy)u(y)Y2=R2supyR|ρ(x)u(y)|dx=supyR|u(y)|.

    The result follows.

    Let us consider the return to home model

    {tu(t,y)=γu(t,y)+αR2v(t,x,y)dx,tv(t,x,y)=ε2xv(t,x,y)αv(t,x,y)+γρ(xy)u(t,y), (6.6)

    with the initial distribution

    {u(0,y)=u0(y)BUC(R2),andv(0,x,y)=v0(x,y)Y1Y2. (6.7)

    Let A:D(A)XX be the infinitesimal generator of the strongly continuous semigroup {TA(t)}t0. By considering W=(uv) and setting B=B1+B2, the problem (6.6) can be rewritten as the following abstract Cauchy problem

    W(t)=(A+B)W(t), for t0, and W(0)=(u0v0)X. (6.8)

    Applying usual bounded perturbation results for strongly continuous semigroup (see for instance [20] and the reference therein), we obtain the following result.

    Theorem 6.7. The linear operator A+B:D(A)XX is the infinitesimal generator of a strongly continuous semigroup {TA+B(t)}t0 of bounded linear operator on X. Moreover tW(t)=TA+B(t)W0 is the unique continuous map satisfying

    W(t)=TA(t)W0+t0TA(tσ)BW(σ)dσ,t0.

    Furthermore, for each t0, the positive cone X+=BUC+(R2)×Y1+Y2+ (where the plus sign stands for the space of non-negative functions) is positive invariant by TA+B(t).

    In this section we incorporate a colonization phenomenon in the return to home model. This new feature turns out to be important especially when dealing with animal dispersal behavior. The following model takes into account a colonization effect that corresponds to the fact that when it is time to return home for the travelers, a fraction p (with 0p1) returns back home like before, and a fraction 1p colonizes the location they have reached at the end of their traveling period. This return to home model with colonization reads as the following system of equations

    {tu(t,y)=γu(t,y)+αpR2v(t,x,y)dx+α(1p)R2v(t,y,y)dyFlow of individuals colonizing the location ytv(t,x,y)=ε2xv(t,x,y)αv(t,x,y)+γρ(xy)u(t,y), (7.1)

    where p[0,1].

    Remark 7.1. Recall that 1/γ is the average time spent at home while 1/α stands for the average time spent to travel. If we assume that p=0 (that is no individuals return to home after traveling), the process described above corresponds to rest during a period 1/γ after a period of travel 1/α. So the case where p=0 is also realistic and this extends the standard diffusion process. The corresponding system could be called for short the "diffuse and rest model".

    In the model the term α(1p)R2v(t,y,y)dy counts the fraction of individuals who colonize the location y at the end of their travel. It is summed over all their previous home locations yR2. Hence 1p is the fraction of the travelers that will change their home location by the end of their travel period.

    Lemma 7.2. Let vY1 be given. Then the map

    xR2v(x,y)dy

    is bounded and uniformly continuous on R2, and one has

    supxR2|R2v(x,y)dy|vY1.

    In other words the linear map vR2v(,y)dy is bounded from Y1 into BUC(R2).

    Proof. Recall that the space Y1 is the set of maps xv(x,.) which belongs to BUC(R2,L1(R2)). The linear form

    G:uG(u)=R2u(y)dy.

    is bounded linear on L1(R2). Therefore the map xG(u(x,.)) must be bounded and uniformly continuous on R2.

    We define the linear bounded operator C:XX by

    C(uv)=(ϕ0) with ϕ(y)=αR2v(y,y)dy.

    By considering W=(uv) the problem (7.1) can be rewritten as the following abstract Cauchy problem in X

    W(t)=(A+pB1+B2+(1p)C)W(t), for t0, and W(0)=W0=(u0v0)X.

    Theorem 7.3. Let p[0,1] be given. Then the linear operator A+pB1+B2+(1p)C:D(A)XX is the infinitesimal generator of a strongly continuous semigroup {TA+pB1+B2+(1p)C(t)}t0 of bounded linear operator on X. Moreover tW(t)=TA+pB1+B2+(1p)C(t)W0 is the unique continuous map satisfying

    W(t)=TA(t)W0+t0TA(tσ)(pB1+B2+(1p)C)W(σ)dσ,t0.

    Furthermore, for each t0, the positive cone X+=BUC+(R2)×(Y1+Y2+) (where the plus sign stands for the space of non-negative functions) is positively invariant by TA+pB1+B2+(1p)C(t).

    Colonization and return to home model: If we add a vital dynamic on the individual staying at home in the model with colonization and return home, we have

    {tu(t,y)=γu(t,y)+α[pR2v(t,x,y)dx+(1p)R2v(t,y,y)dy]+βu(t,y)μu(t,y)κu(t,y)2tv(t,x,y)=ε2xv(t,x,y)αv(t,x,y)+γρ(xy)u(t,y)νv(t,x,y), (7.2)

    where p[0,1].

    To deal with the above system we consider the nonlinear map F:XX given by

    F(uv)=((βμ)uκu20).

    Together with the notation of the previous section and replacing α by α+ν into (6.5), problem (7.2) rewrites as the following abstract Cauchy problem for W=(uv)X:

    W(t)=(A+pB1+B2+(1p)C)W(t)+F(W(t)), for t0, (7.3)

    and

    W(0)=W0=(u0v0)X. (7.4)

    Note that the function f:RR given by f(u)=(βμ)uκu2 satisfies: For each M>0 there exists λ(M)R such that the function uf(u)+λ(M)u is non-negative and increasing on [0,M]. Hence since F is locally Lipschitz continuous on X and the semigroup {TA+pB1+B2+(1p)C(t)} is positive (with respect to the cone X+) we obtain the following theorem.

    The following theorem is a consequence of the results proved in Magal and Ruan [19,20]. The result on monotone semiflows are consequences of Hal Smith [21,22]. We also refer to Magal, Seydi, and Wang [23] for recent extensions.

    Theorem 7.4. The Cauchy problem (7.3)–(7.4) generates a maximal semiflow U:R+×X+X+ on X+. Moreover U is monotone on X+, that is

    W1W00U(t)W1U(t)W00,t0.

    Remark 7.5. In the Lagrangian system of coordinate the above system becomes

    {tu(t,y)=γu(t,y)+α[pR2w(t,z,y)dz+(1p)R2w(t,yy,y)dy]+βu(t,y)μu(t,y)κu(t,y)2tw(t,z,y)=ε2zw(t,z,y)αw(t,z,y)+γρ(z)u(t,y)νw(t,z,y). (7.5)

    Epidemic model for people staying at home: The following equation describes the flux between individuals staying at home at the location yR2 and individuals out of the house

    {ts1(t,y)=αR2s2(t,x,y)dxγs1(t,y)κ1s1(t,y)i1(t,y),ti1(t,y)=αR2i2(t,x,y)dxγi1(t,y)+κ1s1(t,y)i1(t,y)ν1i1(t,y) (8.1)

    Epidemic model for travelers: The following equation describes the flux between individuals staying at home at the location y and the travelers

    {ts2(t,x,y)=ε2xs2(t,x,y)αs2(t,x,y)+γρ(xy)s1(t,y)κ2s2(t,x,y)R2i2(t,x,ˆy)dˆyti2(t,x,y)=ε2xi2(t,x,y)αi2(t,x,y)+γρ(xy)i1(t,y)+κ2s2(t,x,y)R2i2(t,x,ˆy)dˆyν2i2(t,x,y) (8.2)

    In the above epidemic model we assume that the transmission occurs locally in space. At home (see (8.1)) the term κ1s1(t,y)i1(t,y) means that susceptible individuals located at y can only be infected by the infectious at home and located at the same position y. For travelers susceptible individuals originated from y and located at x at time t can be infected by infected travelers located at x at time t whatever the location of their home position. In other words, for the travelers the incidence rate at time t and spatial location x reads as κ2s2(t,x,y)R2i2(t,x,ˆy)dˆy.

    Initial condition: The systems (8.1) and (8.2) is complemented with the initial distributions

    s1(0,y):=s10(y) and i1(0,y):=i10(y), (8.3)

    and

    s2(0,x,y):=s20(x,y) and i2(0,x,y):=i20(x,y). (8.4)

    As above, to handle this problem we consider the Banach space X:=X×X endowed with the product norm. We define the linear operator A:D(A)XX by

    D(A)=D(A)×D(A),

    and

    A((s1s2)(i1i2))=((γs1(y)ε2xs2(x,y)αs2(x,y)+γρ(xy)s1(y))((γ+ν1)i1(y)ε2xi2(x,y)(α+ν2)i2(x,y)+γρ(xy)i1(y)))

    We also define the bounded linear operator L:X×XX×X by

    L((s1s2)(i1i2))=((αR2s2(x,)dx0)(αR2i2(x,)dx0))

    so that A+L:D(A):XX is the infinitesimal generator of a strongly continuous semigroup on X, leaving the positive cone X+=X+×X+ positively invariant. For the contamination terms, we define the nonlinear map F:X×XX×X by

    F((s1s2)(i1i2))=((κ1s1(y)i1(y)κ2s2(x,y)R2i2(x,ˆy)dˆy)(κ1s1(y)i1(y)κ2s2(x,y)R2i2(x,ˆy)dˆy))

    Next setting

    W=((s1s2)(i1i2))X,

    systems (8.1)–(8.4) becomes

    W(t)=(A+L)W(t)+F(W(t)),t>0, (8.5)

    together with

    W(0)=((s10s20)(i10i20))X. (8.6)

    Since F is locally Lipschitz continuous on X, (8.5) generates a strongly continuous maximal semiflow on X. Moreover for each M>0 there exists λ(M) such that for all WX+ with WXM one has

    F(W)+λ(M)WX+.

    As a consequence, since A+L generates a strongly continuous semigroup on X which is positive (with respect to the positive cone X+), the cone X+ is positively invariant with respect to this maximal semiflow. In other words, when the initial data are positive then the maximal solution is positive as well.

    To see that the semiflow is globally defined, fix an initial data W(0)=((s10,s20),(i10,i20))TX+ and denote by W=((s1,s1),(i1,i2))TX+ the maximal solution with initial data w(0) defined on [0,τ) with τ(0,]. Let us show that τ= Adding the Eqs (8.1) and (8.2) integrated with respect to xR2 yields for any t0 and yR2:

    s1(t,y)+i1(t,y)s1(t,y)+i1(t,y)+R2(s2+i2)(t,x,y)dxM:=(s10,s20)TX+(i10,i2,0)TX.

    Now from (8.2) and the positivity of the solution we obtain for t[0,τ), (x,y)R2×R2 that

    0s2(t,x,y)R2eαtK(t,xx)s20(x,y)dx+γMt0eαsR2K(s,xyx)ρ(x)dx,

    This proves that the map ts2(t,)Y1+s2(t,)Y2 is uniformly bounded on [0,τ). From these bounds, the equation for i2 becomes sub-linear so that ti2(t,)Y1Y2 has at most an exponential growth. This prevents from finite time blow-up and ensures that τ= and the semiflow is globally defined on X+.

    The following theorem is a consequence of the results proved in Magal and Ruan [19,20].

    Theorem 8.1. The Cauchy problem (8.5)–(8.6) generates a continuous semiflow U:R+×X+X+. More precisely, for each W0X+, the map tW(t)W is the unique continuous map from [0,) into X satisfying

    W(t)=TA+L(t)W0+t0TA+L(ts)F(W(s))ds,t0.

    The authors are grateful to the anonymous referees for their helpful comments.

    The authors declare there is no conflict of interest.



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