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Research article Special Issues

Modeling repellent-based interventions for control of vector-borne diseases with constraints on extent and duration

  • We study a simple model for a vector-borne disease with control intervention based on clothes and household items treated with mosquito repellents, which has constraints on the extent (population coverage) and on the time duration reflecting technological and physical properties. We compute first, the viability kernel of initial data of the model for which exists an optimal control that maintains the infected host population below a given cap for all future times. Second, we use the viability kernel to compute the set of initial data of the model for which exists an optimal control that brings this population below the cap in a time period not exceeding the intervention's duration. We discuss applications of this framework in predicting and evaluating the performance of control interventions under the given type of constraints.

    Citation: Peter Rashkov. Modeling repellent-based interventions for control of vector-borne diseases with constraints on extent and duration[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 4038-4061. doi: 10.3934/mbe.2022185

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  • We study a simple model for a vector-borne disease with control intervention based on clothes and household items treated with mosquito repellents, which has constraints on the extent (population coverage) and on the time duration reflecting technological and physical properties. We compute first, the viability kernel of initial data of the model for which exists an optimal control that maintains the infected host population below a given cap for all future times. Second, we use the viability kernel to compute the set of initial data of the model for which exists an optimal control that brings this population below the cap in a time period not exceeding the intervention's duration. We discuss applications of this framework in predicting and evaluating the performance of control interventions under the given type of constraints.



    In this paper we study the following time-fractional telegraph equation (TFTE) [1,2,3]

    {DγC,tu(x,t)+aDγ1C,tu(x,t)+bu(x,t)=cuxx(x,t)+f(x,t),(x,t)(0,1)×(0,T],u(x,0)=ϕ1(x),  ut(x,0)=ϕ2(x),x[0,1],u(0,t)=ψ1(t),  u(1,t)=ψ2(t),t[0,T], (1.1)

    where 1<γ<2, a,b,c and T are given positive constants, f(x,t),ϕ1(x),ϕ2(x),ψ1(t) and ψ2(t) are given functions and satisfy the compatibility conditions, DγC,tu(x,t) and Dγ1C,tu(x,t) are the Caputo fractional derivatives of order γ and γ1 respectively. The Caputo fractional derivative DγC,tu(x,t) is defined by [4,Definition 2.2]

    DγC,tu(x,t)=1Γ(2γ)t0(ts)1γuss(x,s)ds.

    The Caputo operators DγC,t and Dγ1C,t have the following relationship

    DγC,tu(x,t)=1Γ(1(γ1))t0(ts)(γ1)sus(x,s)ds=Dγ1C,tut(x,t).

    Based on the above relationship, the TFTE (1.1) can be transformed into the following equivalent integral-differential equation as shown in [4,Lemma 6.2]

    {ut(x,t)+au(x,t)=aϕ1(x)+ϕ2(x)+1Γ(γ1)t0(ts)γ2[cuxx(x,s)bu(x,s)+f(x,s)]ds,(x,t)(0,1)×(0,T],u(x,0)=ϕ1(x),x[0,1],u(0,t)=ψ1(t),  u(1,t)=ψ2(t),t[0,T], (1.2)

    which simplifies the original problem to a certain extent. For the subsequent numerical discretization and error analysis, we assume

    |lf(x,t)tl|C(1+tγl),            l=0,1,2. (1.3)

    The TFTE (1.1) is used to describe some phenomena such as propagation of electric signals [5], acoustic waves in porous media [6], transport of neutron in a nuclear reactor [7], and hyperbolic heat transfer [8].

    There are a few numerical methods to solve the TFTE. Wang [9] developed a method in the reproducing kernel space by piecewise technique to solve the TFTE. Hosseini et al. [10] applied the radial basis functions and finite difference method to solve the TFTE. Kumar et al. [11] described a finite difference scheme for the generalized TFTE. Hashemi and Baleanu [12] utilized a combination of method of line and group preserving scheme to solve the TFTE. Wei et al. [13] discussed a fully discrete local discontinuous Galerkin finite element method for the TFTE. Sweilam [14] introduced the Sinc-Legendre collocation method for solving the TFTE. Mollahasani et al. [15] presented an operational method based on hybrid functions of Legendre polynomials and Block-Pulse-Functions for solving the TFTE. Hafez and Youssri [16] used a shifted Jacobi collocation scheme for a multidimensional TFTE. Bhrawy et al. [17] proposed an accurate and efficient spectral algorithm for the numerical solution of the two-sided space-time TFTE with three types of non-homogeneous boundary conditions. Youssri et al. [18,19] also developed numerical spectral Legendre approaches to solve the TFTEs. Bhrawy and Zaky [20] used a method based on the Jacobi tau approximation for solving multi-term time-space TFTE. In [2,3] B-spline collocation methods were applied to solve the TFTEs. In [21,22] wavelet methods were used to solve the TFTEs. But these papers only study the case that the solutions of the TFTEs are sufficiently smooth. Special treatment techniques, such as graded meshes [23,24,25,26,27] and mapped basis functions [28] reflecting the characteristics of the singularity of the exact solution, need to be used to solve the fractional differential equations.

    The aim of the present study is twofold. The first aim is to construct an integral-difference discretization scheme on a graded mesh to approximate the integral-differential equation with a weakly singular kernel transformed from the TFTE, which is a second-order convergence discretization scheme. The second aim is to take the possible singularity of the exact solution into account in the convergence analysis, where the singularity of the exact solution is reflected as

    |ku(x,t)xk|C,                      k=0,1,,4, (1.4)
    |l+mu(x,t)tlxm|C(1+tγl),      l=0,1,2,3,  and  m=0,1,2, (1.5)

    which can be referred in the literature [25,26,27,29] for details. We show that our discretization scheme on a graded mesh is second-order convergent for both the spatial discretization and the time discretization, although the exact solution of the TFTE may have singularity. Numerical experiments confirm the effectiveness of the theoretical results, and also verify that this scheme is more accurate than the methods given in [2,3].

    Remark 1.1 If b,c are variable coefficients and a is a variable coefficient with respect to x, the TFTE (1.1) can also be transformed into the same integral-differential equation as given in (1.2). If a is a variable coefficient with respect to t, we first approximate a with a piecewise linear interpolation function about t and then transform the approximate TFTE into an integral-differential equation as given in (1.2). If there is a nonlinear term in the TFTE, we first transform it into a linear equation by Newton iterative method and then transform it into an integral-differential equation as given in (1.2).

    Notation. Throughout the paper, C is used to indicate the positive constant independent of the mesh, and C in different places can represent different values. In order to simplify the notation, the representation gji=g(xi,tj) is introduced for any function g at the mesh point (xi,tj)(0,1)×(0,T].

    We developed the discretization scheme on a graded mesh ˉΩN,K{(xi,tj)|xi=ih,h=1/N,0i N,0jK} with time mesh points

    tj=T(jK)2γ1 (2.1)

    and time sizes tj=tjtj1 for 1jK, where the discretization parameters N and K are positive integers. The following integral-difference discretization scheme

    UjiUj1itj+aUj1i+Uji2=aϕ1,i+ϕ2,i+12Γ(γ1)j1k=1tktk1(tj1s)γ2[tkstk(cδ2xUk1ibUk1i+fk1i)   +stk1tk(cδ2xUkibUki+fki)]ds+12Γ(γ1)jk=1tktk1(tjs)γ2   [tkstk(cδ2xUk1ibUk1i+fk1i)+stk1tk(cδ2xUkibUki+fki)]ds

    for 1i<N and 1jK is used to approximate the integral-differential equation in (1.2), where Uji denotes the numerical solution at the mesh point (xi,tj) and δ2xUji=Uji+12Uji+Uji1h2. Thus, our discretization scheme for problem (1.2) is

    {UjiUj1itj+aUj1i+Uji2=aϕ1,i+ϕ2,i+12j1k=1[ξj1,k(cδ2xUk1ibUk1i+fk1i)+ηj1,k(cδ2xUkibUki+fki)]+12jk=1[ξj,k(cδ2xUk1ibUk1i+fk1i)+ηj,k(cδ2xUkibUki+fki)],1i<N,1jK,U0i=ϕ1(xi),0iN,Uj0=ψ1(tj),  UjN=ψ2(tj),1jK, (2.2)

    where

    ξj,k=1tkΓ(γ1)tktk1(tjs)γ2(tks)ds,            k=1,2,j, (2.3)
    ηj,k=1tkΓ(γ1)tktk1(tjs)γ2(stk1)ds,         k=1,2,j. (2.4)

    Let zji=Ujiuji for 0iN and 0jK. Then, from (1.2) and (2.2) we know that the error mesh function z satisfies

    {zjizj1itj+azj1i+zji2=12j1k=1[ξj1,k(cδ2xzk1ibzk1i)+ηj1,k(cδ2xzkibzki)]   +12jk=1[ξj,k(cδ2xzk1ibzk1i)+ηj,k(cδ2xzkibzki)]+Rji,1i<N,1jK,z0i=0,0iN,zj0=zjN=0,1jK, (2.5)

    where

    Rji=12(ut(xi,tj1)+ut(xi,tj))ujiuj1itj+12Γ(γ1)j1k=1tktk1(tj1s)γ2[tkstk(cδ2xuk1ibuk1i+fk1i)+stk1tk(cδ2xukibuki+fki)(c2u(xi,s)x2bu(xi,s)+f(xi,s))]ds+12Γ(γ1)jk=1tktk1(tjs)γ2[tkstk(cδ2xuk1ibuk1i+fk1i)+stk1tk(cδ2xukibuki+fki)(c2u(xi,s)x2bu(xi,s)+f(xi,s))]ds.

    Then, under the assumption (1.3) we can obtain

    |Rji|Ctjtj1|3us3(xi,s)|(stj1)ds+Cj1k=1tktk1(tj1s)γ2{tkstk(stk1)2|10(c4ux2t2b2ut2+2ft2)(xi,tk1+(stk1)y)ydy|+stk1tk(tks)2|10(c4ux2t2b2ut2+2ft2)(xi,s+(tks)y)(1y)dy|}ds+Cjk=1tktk1(tjs)γ2{tkstk(stk1)2|10(c4ux2t2b2ut2+2ft2)(xi,tk1+(stk1)y)ydy|+stk1tk(tks)2|10(c4ux2t2b2ut2+2ft2)(xi,s+(tks)y)(1y)dy|}ds+Cj1k=1tktk1(tj1s)γ2|tkstk(δ2xuk1i2u(xi,tk1)x2)+stk1tk(δ2xuki2u(xi,tk)x2)|ds+Cjk=1tktk1(tjs)γ2|tkstk(δ2xuk1i2u(xi,tk1)x2)+stk1tk(δ2xuki2u(xi,tk)x2)|dsC(tjtj1sγ32ds)2+Cj1k=1tktktk1(tj1s)γ2tktk1tγ2dtds+Cjk=1tktktk1(tjs)γ2tktk1tγ2dtds+Ch2C(N2+K2), (2.6)

    where we have used the linear interpolation remainder formula

    tkttkgk1+ttk1tkgkg(t)=1tk{(tkt)(ttk1)210g[tk1+(ttk1)s]sds+(ttk1)(tkt)210g[t+(tkt)s](1s)ds},

    the regularities (1.4) and (1.5), the assumption (1.3), the graded mesh (2.1), the inequality (see [30])

    tjtj1sα(stj1)ds12{tjtj1sα/2ds}2

    for α<0, and the following estimates

    tjtj1sγ32ds=2γ1(tγ12jtγ12j1)CK1,t1t10(tjs)γ2t10tγ2dtdsC(t1)γt10(tjs)γ2dsC(1K)2γγ1CK2γ,jk=2tktktk1(tjs)γ2tktk1tγ2dtdsCjk=2(tk)3(tjtk1)γ2tγ2k1=Cjk=2[(kK)2γ1(k1K)2γ1]3[tj(k1K)2γ1]γ2(k1K)2(γ2)γ1CK3jk=1(kK)6γ13(tjk1K)γ2(k1K)2(γ2)γ1CK2j1k=1(tjkK)γ2(kK)5γγ11KCK2tj0(tjs)γ2s5γγ1ds=CK2tγ1+5γγ1jB(4γ1,γ1)CK2.

    The following lemma gives a useful result for our convergence analysis, which is given in [31].

    Lemma 2.1 Assume that {ωk}k=0 be a sequence of non-negative real numbers satisfying

    ωk0,   ωk+1ωk,   ωk+12ωk+ωk10.

    Then for any integer K>0 and vector (V1,V2,,VK)TRK, the following inequality

    Kk=1(kp=1ωkpVp)Vk0

    holds true.

    Next we show that the sequence {βj,k}jk=1 satisfies the conditions in Lemma 2.1 by using the technique in [32], where βj,jk=tj(ξj,k+1+ηj,k) with ξj,j+1=0.

    Lemma 2.2 The sequence {βj,k}jk=0 satisfies

    βj,k0,   βj,k+1βj,k,   βj,k+12βj,k+βj,k10.

    Proof. From the definitions of the sequences {ξj,k}jk=1 and {ηj,k}jk=1 we have

    βj,jk=tjtk+1Γ(γ1)tk+1tk(tjs)γ2(tk+1s)ds+tjtkΓ(γ1)tktk1(tjs)γ2(stk1)ds=tjtk+1Γ(γ1)tk+10(tjtky)γ2(tk+1y)dy+tjtkΓ(γ2)0tk(tjtky)γ2(y+tk)dy=tjΓ(γ1)tk+1tk(tjtky)γ2(1|y|tkχ[tk,0]+tk+1χ[0,tk+1])dy (2.7)

    for 1k<j and

    βj,jj=1Γ(γ1)tjtj1(tjs)γ2(stj1)ds, (2.8)

    where

    χ[p,q](y)={1,y[p,q],0,y[p,q].

    Obviously, from (2.7) and (2.8) we have

    βj,jk0,          1kj, (2.9)

    and

    βj,j(j1)βj,jj. (2.10)

    Since

    ddk(tjtky)γ2>0,      k1  and  y(tk,tk+1),d2dk2(tjtky)γ2>0,     k1  and  y(tk,tk+1),

    we can obtain

    βj,jk1βj,jk,    and   βj,jk12βj,jk+βj,jk+10,

    which imply

    βj,k+1βj,k,    and   βj,k+12βj,k+βj,k10. (2.11)

    From (2.9)–(2.11) we know the lemma holds true.

    A modified Grönwall inequality given in [33,Lemma 3.3] also is needed in the convergence analysis of the scheme.

    Lemma 2.3 Suppose that α,C0,T>0 and dk,j=C0tk+1(tjtk)α1(k=0,1,,j1) for 0=t0<t1<<tK=T and j=1,2,,K, where tk+1=tk+1tk. Assume that g0 is positive and the sequence {φj} satisfies

    {φ0g0,φjj1k=0ak,jφk+g0,

    then

    φkCg0,             j=1,2,,K.

    For analyzing the stability and estimating the error for the discrete scheme (2.2) we introduce the discrete inner product and discrete L2-norm as follows

    v,w=hNi=0viwi   and   v=v,v,

    where v and w are two mesh functions. We also introduce the notation δxvi=vivi1h for 1iN.

    By using the technique given in [34,Theorem 3.2] we will derive the following stability result of the discrete scheme (2.2).

    Theorem 2.4 Let {Uji|0iN, 1jK} be the solution of the discrete scheme (2.2). Then the numerical solution U satisfies the following estimates

    UjC(max0kjfk+ϕ1+ϕ2),        1jK,

    where C is a positive constant independent of N and K.

    Proof. First we rewrite (2.2) as the following equation

    UjiUj1i+12atj(Uj1i+Uji)=tj(aϕ1,i+ϕ2,i)+12tjj1k=1[ξj1,k(cδ2xUk1ibUk1i+fk1i)+ηj1,k(cδ2xUkibUki+fki)]+12tjjk=1[ξj,k(cδ2xUk1ibUk1i+fk1i)+ηj,k(cδ2xUkibUki+fki)] (2.12)

    for 1i<N and 1jK. Then, taking the inner produce of (2.12) with Uji, we can get

    Uj2Uj1,Uj+12atjUj1,Uj+12atjUj2=12tjj1k=1[ξj1,k(cδ2xUk1,UjbUk1,Uj+fk1,Uj)   +ηj1,k(cδ2xUk,UjbUk,Uj+fk,Uj)]   +12tjjk=1[ξj,k(cδ2xUk1,UjbUk1,Uj+fk1,Uj)   +ηj,k(cδ2xUk,UjbUk,Uj+fk,Uj)]+tj(aϕ1,Uj+ϕ2,Uj).

    By recursion, we also can get the following equations

    Uj12Uj2,Uj1+12atj1Uj2,Uj1+12atj1Uj12=12tj1j2k=1[ξj2,k(cδ2xUk1,Uj1bUk1,Uj1+fk1,Uj1)   +ηj2,k(cδ2xUk,Uj1bUk,Uj1+fk,Uj1)]   +12tj1j1k=1[ξj1,k(cδ2xUk1,Uj1bUk1,Uj1+fk1,Uj1)   +ηj1,k(cδ2xUk,Uj1bUk,Uj1+fk,Uj1)]   +tj1(aϕ1,Uj1+ϕ2,Uj1),                   U22U1,U2+12at2U1,U2+12at2U22=12t21k=1[ξ1,k(cδ2xUk1,U2bUk1,U2+fk1,U2)   +η1,k(cδ2xUk,U2bUk,U2+fk,U2)]   +12t22k=1[ξ2,k(cδ2xUk1,U2bUk1,U2+fk1,U2)   +η2,k(cδ2xUk,U2bUk,U2+fk,U2)]+t2(aϕ1,U2+ϕ2,U2),U12U0,U1+12at1U0,U1+12at1U12=12t11k=1[ξ1,k(cδ2xUk1,U1bUk1,U1+fk1,U1)   +η1,k(cδ2xUk,U1bUk,U1+fk,U1)]+t1(aϕ1,U1+ϕ2,U1).

    Applying the inequality

    v,wvw12v2+12w2

    and the equality

    δ2xv,w=δxv,δxw,

    we have

    Uj212Uj212Uj12+12atjUj1,Uj+12atjUj212tjj1k=1[ξj1,k(cδxUk1,δxUjbUk1,Uj+fk1,Uj)   +ηj1,k(cδxUk,δxUjbUk,Uj+fk,Uj)]   +12tjjk=1[ξj,k(cδxUk1,δxUjbUk1,Uj+fk1,Uj)   +ηj,k(cδxUk,δxUjbUk,Uj+fk,Uj)]+tj(aϕ1,Uj+ϕ2,Uj),Uj1212Uj1212Uj22+12atj1Uj2,Uj1+12atj1Uj1212tj1j2k=1[ξj2,k(cδxUk1,δxUj1bUk1,Uj1+fk1,Uj1)   +ηj2,k(cδxUk,δxUj1bUk,Uj1+fk,Uj1)]   +12tj1j1k=1[ξj1,k(cδxUk1,δxUj1bUk1,Uj1+fk1,Uj1)   +ηj1,k(cδxUk,δxUj1bUk,Uj1+fk,Uj1)]   +tj1(aϕ1,Uj1+ϕ2,Uj1),                   U2212U2212U12+12at2U1,U2+12at2U2212t21k=1[ξ1,k(cδxUk1,δxU2bUk1,U2+fk1,U2)   +η1,k(cδxUk,δxU2bUk,U2+fk,U2)]   +12t22k=1[ξ2,k(cδxUk1,δxU2bUk1,U2+fk1,U2)   +η2,k(cδxUk,δxU2bUk,U2+fk,U2)]+t2(aϕ1,U2+ϕ2,U2),U1212U1212U02+12at1U0,U1+12at1U12=12t11k=1[ξ1,k(cδxUk1,δxU1bUk1,U1+fk1,U1)   +η1,k(cδxUk,δxU1bUk,U1+fk,U1)]+t1(aϕ1,U1+ϕ2,U1).

    Adding up the above inequalities we can obtain

    Uj22jp=1p1k=1tp[ξp1,k(cδxUk1,δxUpbUk1,Up+fk1,Up)+ηp1,k(cδxUk,δxUpbUk,Up+fk,Up)]+2jp=1pk=1tp[ξp,k(cδxUk1,δxUpbUk1,Up+fk1,Up)+ηp,k(cδxUk,δxUpbUk,Up+fk,Up)]jp=1tpaUp1,Up+2jp=1tp(aϕ1,Up+ϕ2,Up)+U022jp=1p1k=1tpUp[ξp1,k(bUk1+fk1)+ηp1,k(bUk+fk)]+2jp=1pk=1tpUp[ξp,k(bUk1+fk1)+ηp,k(bUk+fk)]+2jp=1tpUp(aUp1+aϕ1+ϕ2)+U02,

    where we have used Lemmas 2.1 and 2.2. From the above inequality we have

    Uj2Cmax0pjUp[j1k=1(ξj1,k+1+ηj1,k+ξj,k+1+ηj,k)(Uk+max0kjfk)+j1k=1tk+1aUk+ϕ1+ϕ2], (2.13)

    where ξj1,j=0. For each j, there exists j(1jj) such that

    Uj=max0pjUp.

    Since j in (2.13) is any integer from 0 to N, we have

    UjC[j1k=1(ξj1,k+1+ηj1,k+ξj,k+1+ηj,k)(Uk+max0kjfk)+j1k=1tk+1aUk+ϕ1+ϕ2].

    Combining the above inequality with Lemma 2.2 we have

    Uj[j1k=1(ξj1,k+1+ηj1,k+ξj,k+1+ηj,k)(Uk+max0kjfk)+j1k=1tk+1aUk+ϕ1+ϕ2] (2.14)

    for 1jjK. Furthermore, we have

    ξj,k+1+ηj,k=1Γ(γ+1){1tk[(tjtk1)γ(tjtk)γ]1tk+1[(tjtk)γ(tjtk+1)γ]}=1Γ(γ)[(tjθk)γ1(tjθk+1)γ1]2Γ(γ)tk+1(tjtk+1)γ223γΓ(γ)tk+1(tjtk)γ2, (2.15)

    where we have used the mean value theorem with θk(tk1,tk) and the inequality n2(n1) for n2. Combining (2.14) and (2.15) we can get

    UjC{j1k=1tk+1[1+(tj1tk)γ2+(tjtk)γ2](Uk+max0kjfk)+ϕ1+ϕ2}C(max0kjfk+ϕ1+ϕ2)

    for 1jK, where Lemma 2.3 has been used. This inequality implies the theorem holds true.

    Next we derive the error estimates for the integral-difference discretization scheme (2.2).

    Theorem 2.5 Under the regularity conditions (1.4) and (1.5) and the assumption (1.3), we have the following error estimates

    UjujC(N2+K2),        1jK,

    where C is a positive constant independent of N and K.

    Proof. Applying Theorem 2.4 to the error equation (2.5) we can derive

    UjCmax0kjRkC(N2+K2),

    where we have used the estimates (2.6). From this we complete the proof.

    In this section we present some numerical results to indicate experimentally the efficiency and accuracy of the integral-difference discretization scheme. Errors and convergence rates for the integral-difference discretization scheme are presented for two examples.

    Example 4.1 We first consider the TFTE (1.1) with a=b=1,c=π,ϕ1(x)=ϕ2(x)=ψ1(t)=0 and ψ2(t)=t3sin2(1), where f(x,t) is chosen such that the exact solution is u(x,t)=t3sin2(x). This equation has been tested in [2,3].

    We measure the accuracy in the discrete L2-norm and L-norm

    eN,KL2=max1jKujUj,      eN,KL=max0iN,0jK|ujiUji|,

    and the convergence rate

    rN,KL2=log2(eN,KL2/e2N,2KL2),        rN,KL=log2(eN,KL/e2N,2KL),

    respectively. In order to further confirm that the convergence rate in the time direction is consistent with the theoretical convergence rate, we also measure the convergence rates by fixing a large N as follows

    ˉrN,KL2=log2(eN,KL2/eN,2KL2),        ˉrN,KL=log2(eN,KL/eN,2KL),

    respectively. The numerical results for Example 4.1 are tabulated in Tables 1 and 2.

    Table 1.  Error estimates eN,KL2,eN,KL and convergence rates rN,KL2,rN,KL for Example 4.1.
    γ norm Number of mesh points (K,N)
    (32,32) (64,64) (128,128) (256,256) (512,512)
    1.2 L2 5.9593e-4 1.6516e-4 4.4234e-5 1.1648e-5 3.0354e-6
    1.851 1.901 1.925 1.940 -
    L 8.5299e-4 2.3639e-4 6.3289e-5 1.6662e-5 4.3412e-6
    1.851 1.901 1.925 1.940 -
    1.4 L2 1.9673e-4 5.1561e-5 1.3295e-5 3.3967e-6 8.6265e-7
    1.932 1.955 1.969 1.977 -
    L 2.8577e-4 7.4813e-5 1.9284e-5 4.9245e-6 1.2503e-6
    1.934 1.956 1.969 1.978 -
    1.6 L2 8.3665e-5 2.1305e-5 5.3828e-6 1.3545e-6 3.4004e-7
    1.973 1.985 1.991 1.994 -
    L 1.2489e-4 3.1809e-5 8.0322e-6 2.0206e-6 5.0714e-7
    1.973 1.986 1.991 1.994 -
    1.8 L2 3.8982e-5 9.7671e-6 2.4448e-6 6.1165e-7 1.5298e-7
    1.997 1.998 1.999 1.999 -
    L 5.6986e-5 1.4245e-5 3.5643e-6 8.9169e-7 2.2303e-7
    2.000 1.999 1.999 1.999 -

     | Show Table
    DownLoad: CSV
    Table 2.  Error estimates eN,KL2,eN,KL and convergence rates ˉrN,KL2,ˉrN,KL for Example 4.1 with N=1024.
    γ norm Number of mesh points K
    32 64 128 256 512 1024
    1.2 L2 6.1879e-4 1.7080e-4 4.5625e-5 1.1979e-5 3.1016e-6 7.8528e-7
    1.857 1.904 1.929 .949 1.982 -
    L 8.8009e-4 2.4294e-4 6.4901e-5 1.7044e-5 4.4176e-6 1.1229e-6
    1.857 1.904 1.929 1.948 1.976 -
    1.4 L2 2.1850e-4 5.6970e-5 1.4630e-5 3.7144e-6 9.2607e-7 2.1819e-7
    1.939 1.961 1.978 2.004 2.086 -
    L 3.1067e-4 8.1008e-5 2.0808e-5 5.2871e-6 1.3225e-6 3.1617e-7
    1.939 1.961 1.977 1.999 2.064 -
    1.6 L2 1.0424e-4 2.6426e-5 6.6476e-6 1.6552e-6 3.9985e-7 8.5239e-8
    1.980 1.991 2.006 2.050 2.230 -
    L 1.4812e-4 3.7555e-5 9.4509e-6 2.3574e-6 5.7372e-7 1.2711e-7
    1.980 1.990 2.003 2.039 2.174 -
    1.8 L2 5.2400e-5 1.3135e-5 3.2750e-6 , 8.0479e-7 1.8676e-7 3.8256e-8
    1.996 2.004 2.025 2.107 2.287 -
    L 7.4474e-5 1.8678e-5 4.6619e-6 1.1498e-6 2.7124e-7 5.5771e-8
    1.995 2.002 2.020 2.084 2.282 -

     | Show Table
    DownLoad: CSV

    Example 4.2 We now consider the TFTE (1.1) with a=b=c=1,ϕ1(x)=ϕ2(x)=0, ψ1(t)=ψ2(t)=tγ, where f(x,t) is chosen such that the exact solution is u(x,t)=tγ(x2x+1). We also measure the accuracy in the discrete L2-norm eN,KL2, L-norm eN,KL and the convergence rates rN,KL2,rN,KL,ˉrN,KL2,ˉrN,KL as previously defined, respectively. The numerical results for Example 4.2 are tabulated in Tables 3 and 4.

    Table 3.  Error estimates eN,KL2,eN,KL and convergence rates rN,KL2,rN,KL for Example 4.2.
    γ norm Number of mesh points (K,N)
    (32,32) (64,64) (128,128) (256,256) (512,512)
    1.2 L2 1.1821e-4 2.9391e-5 7.3519e-6 1.8372e-6 4.5927e-7
    2.008 1.999 2.001 2.000 -
    L 1.6019e-4 3.9819e-5 9.9670e-6 2.4906e-6 6.2257e-7
    2.008 1.998 2.001 2.000 -
    1.4 L2 6.6529e-5 .6604e-5 4.1490e-6 1.0373e-6 2.5931e-7
    2.002 2.001 2.000 2.000 -
    L 9.1346e-5 2.2775e-5 5.6898e-6 1.4225e-6 3.5562e-7
    2.004 2.001 2.000 2.000 -
    1.6 L2 4.2340e-5 1.0580e-5 2.6446e-6 6.6114e-7 1.6528e-7
    2.001 2.000 2.000 2.000 -
    L 6.0023e-5 1.4954e-5 3.7358e-6 9.3402e-7 2.3349e-7
    2.005 2.001 2.000 2.000 -
    1.8 L2 2.1727e-5 5.4316e-6 1.3587e-6 3.3967e-7 8.4917e-8
    2.000 1.999 2.000 2.000 -
    L 3.2980e-5 8.2122e-6 2.0523e-6 5.1287e-7 1.2821e-7
    2.006 2.001 2.001 2.000 -

     | Show Table
    DownLoad: CSV
    Table 4.  Error estimates eN,KL2,eN,KL and convergence rates ˉrN,KL2,ˉrN,KL for Example 4.2 with N=1024.
    γ norm Number of mesh points K
    32 64 128 256 512 1024
    1.2 L2 1.1827e-4 2.9395e-5 7.3521e-6 1.8372e-6 4.5927e-7 1.1482e-7
    2.008 1.999 2.001 2.000 2.000 -
    L 1.6027e-4 3.9823e-5 9.9673e-6 2.4906e-6 6.2257e-7 1.5564e-7
    2.009 1.998 2.001 2.000 2.000 -
    1.4 L2 6.6566e-5 1.6606e-5 4.1491e-6 1.0373e-6 2.5931e-7 6.4828e-8
    2.003 2.001 2.000 2.000 2.000 -
    L 9.1393e-5 2.2778e-5 5.6900e-6 1.4225e-6 3.5562e-7 8.8905e-8
    2.004 2.001 2.000 2.000 2.000 -
    1.6 L2 4.2366e-5 1.0582e-5 2.6447e-6 6.6114e-7 1.6528e-7 4.1321e-8
    2.001 2.000 2.000 2.000 2.000 -
    L 6.0036e-5 1.4956e-5 3.7359e-6 9.3403e-7 2.3350e-7 5.8373e-8
    2.005 2.001 2.000 2.000 2.000 -
    1.8 L2 2.1742e-5 5.4325e-6 1.3587e-6 , 3.3967e-7 8.4917e-8 2.1229e-8
    2.001 1.999 2.000 2.000 2.000 -
    L 3.3046e-5 8.2120e-6 2.0523e-6 5.1287e-7 1.2821e-7 3.2051e-8
    2.009 2.001 2.001 2.000 2.000 -

     | Show Table
    DownLoad: CSV

    Tables 14 show that the numerical solution of the integral-difference discretization scheme on a graded mesh converges to the exact solution with second-order accuracy for both the spatial discretization and the time discretization in the discrete L2-norm and in the discrete L-norm, respectively. Moreover, compared with the previous methods with only first-order convergence for the time discretization, our discretization scheme improves the previous results given in [2,3].

    In this paper, the TFTE is transformed into an equivalent integral-differential equation with a weakly singular kernel by using the integral transformation. Then an integral-difference discretization scheme on a graded mesh is developed to approximate the integral-differential equation. The stability and convergence are proved by using the L2-norm. The possible singularity of the exact solution is taken into account in the convergence analysis. It is shown that the scheme is second-order convergent for both the spatial discretization and the time discretization. The numerical experiments demonstrate the validity of our theoretical results and also verify that this scheme is more accurate than the methods given in [2,3]. In future we will extend this method to variable-order fractional differential equations.

    We would like to thank the anonymous reviewers for their valuable suggestions and comments for the improvement of this paper. The authors declare that there is no conflict of interests regarding the publication of this article. The work was supported by Ningbo Municipal Natural Science Foundation (Grant Nos. 2021J178, 2021J179) and Zhejiang Province Public Welfare Technology Application Research Project (Grant No. LGF22H260003).

    The authors declare there is no conflict of interest.



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