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An SIS epidemic model with time delay and stochastic perturbation on heterogeneous networks

  • An SIS epidemic model with time delay and stochastic perturbation on scale-free networks is established in this paper. And we derive sufficient conditions guaranteeing extinction and persistence of epidemics, respectively, which are related to the basic reproduction number R0 of the corresponding deterministic model. When R0<1, almost surely exponential extinction and p-th moment exponential extinction of epidemics are proved by Razumikhin-Mao Theorem. Whereas, when R0>1, the system is persistent in the mean under sufficiently weak noise intensities, which indicates that the disease will prevail. Finally, the main results are demonstrated by numerical simulations.

    Citation: Meici Sun, Qiming Liu. An SIS epidemic model with time delay and stochastic perturbation on heterogeneous networks[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6790-6805. doi: 10.3934/mbe.2021337

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  • An SIS epidemic model with time delay and stochastic perturbation on scale-free networks is established in this paper. And we derive sufficient conditions guaranteeing extinction and persistence of epidemics, respectively, which are related to the basic reproduction number R0 of the corresponding deterministic model. When R0<1, almost surely exponential extinction and p-th moment exponential extinction of epidemics are proved by Razumikhin-Mao Theorem. Whereas, when R0>1, the system is persistent in the mean under sufficiently weak noise intensities, which indicates that the disease will prevail. Finally, the main results are demonstrated by numerical simulations.



    Transmission dynamics studies problems arising in the real world, for example, spread of diseases in population, virus propagation in computer networks and diffusion of information. It strongly depends on properties of the contact network. Scale-free network by Barabási and Albert [1] can well depict complex connectivity patterns in nature and human society such as, e.g., social network, computer network and World Wide Web. Therefore, compared with classical epidemic models on homogeneous networks, it is more significant to study spreading dynamics on heterogeneous networks, i.e., scale-free networks.

    The spreading dynamics on complex networks has attracted increasing attention. A lot of epidemiological models on complex networks, including SI [2,3], SIS [4,5], SIR [6], SIQS [7], and so on [8,9], have been established successively. The pioneering work of Pastor-Satorras and Vespignani [4,5] introduced the SIS model on scale-free networks by mean-field approximation. They showed that the epidemic threshold is infinitesimal with network size increasing, which makes the spread of infections tremendously strengthened. Wang and Dai [10] analyzed the network-based SIS model theoretically for the first time. They derived that the epidemic threshold is the critical parameter for global stability of the disease-free and endemic equilibria. In the meantime, d'Onofrio [11] conducted further research on this aspect.

    In recent years, as for modification and extension of the network-based SIS model, two directions have always been of great concern. One is introducing time delays to simulate incubation periods, infection periods, immunity periods and so on. Models expressed by functional differential equations (FDE) have been formulated. Liu et al. [12,13] proposed and discussed SIS epidemic models on scale-free networks with discrete and distributed delays, respectively, in which the time delays represent infection periods. Kang et al. [14] established an SIS model with time delay denoting the incubation period of disease in a vector's body and analyzed the global stability of equilibria. Furthermore, Kang and Fu [15] considered two transmitting ways (by human and by vector) and discussed another new delayed SIS model on heterogeneous networks. Another way is to enter interactions of uncertain environments in the models. Stochastic differential equation (SDE) SIS models on complex networks have been developed. Bonaccorsi et al. [16] proposed an SIS model with stochastic infection rates on networks and proved the conditions for extinction and stochastic persistence of epidemics. Krause et al. [17] analyzed dynamical behaviors of a stochastic SIS epidemic model in metapopulation by numerical simulation. Some control approaches were designed to control epidemic spreading. Yang and Jin [18] introduced a stochastic SIS model driven by Lévy noise on networks. The stability of the disease-free equilibrium and the sufficient condition for persistence were proved.

    However, to the best of our knowledge, there has been little research about the interplay of noise and delay on spreading dynamics in complex networks. We will present an epidemic model with time delay and stochastic perturbation. It is based on the following general version of the delayed SIS model with an infective vector [14],

    {dSk(t)dt=Λλ(k)Sk(t)Θ(tτ)edmτγSk(t)+μIk(t),dIk(t)dt=λ(k)Sk(t)Θ(tτ)edmτγIk(t)μIk(t), (1.1)

    where λ(k) is the degree-dependent infection rate which is bounded [19], such as λk [14], λc(k) [20], and so on. Sk(t) and Ik(t), k=1,,n, represent the relative densities of healthy and infected nodes with degree k at time t, respectively. Here, μ is the recovery rate, and γ is the mortality rate which is equal to the birth rate Λ, i.e., the network size is time invariant. dm is the nature death of the vector and τ is the incubation. edmτ represents the probability of infected vectors who were infected at time tτ but did not die during the time period τ. The parameters aforesaid are all positive. The vector's density is simply proportional to Θ(tτ) expressed as

    Θ(tτ)=1knj=1φ(j)P(j)Ij(tτ),

    where P(j) is the degree distribution, k=nk=1kP(k) is the average degree of the network, φ(j) denotes an infected node, with degree j, occupied edges which can transmit the disease [21], and n is the maximum degree of nodes in this network. We also define f(k)=nk=1f(k)P(k).

    By analogy with the results of Ref. [14], the following equivalent system of (1.1)

    dIkdt=λ(k)(1Ik)Θ(tτ)edmτγIkμIk (1.2)

    always has a disease-free equilibrium I0=(0,0,,0). If R0<1, I0 is globally asymptotically stable. Whereas, I0 is unstable and there exists a globally stable endemic equilibrium I=(I1,I2,,In) when R0>1. And here,

    R0=λ(k)φ(k)edmτ(γ+μ)k.

    Now, we introduce interactions of random environments to system (1.1) by replacing the infection rate λ(k) with

    λ(k)λ(k)+σkIkdBk(t),

    where σk>0,k=1,,n represent the noise intensities, and dBk(t)(k=1,,n) is an n-dimensional standard white noise, i.e., Bk(t)(k=1,,n) is an n-dimensional standard Brownian motion with Bk(0)=0. It is essential to assume that the diffusion coefficient depends on relative densities Ik. The infection rate varies around a mean value, and the variance gets smaller with the relative densities of infected nodes decreasing. It guarantees that the solution has physical meaning, namely, it remains above zero. Moreover, the dependence of noise intensities on the solution is typical in previous articles concerning population dynamics [22] and spread dynamics [16] with environmental noise. Then the stochastic system we study takes the following form

    {dSk=[Λλ(k)SkΘ(tτ)edmτγSk+μIk]dtσkIkSkΘ(tτ)edmτdBk(t),dIk=[λ(k)SkΘ(tτ)edmτγIkμIk]dt+σkIkSkΘ(tτ)edmτdBk(t). (1.3)

    Let D=(0,1)n be the n-th Cartesian power of the interval (0,1). And denote by C=C([τ,0],D) the Banach space of continuous functions mapping the interval [τ,0] into D with norm

    |ϕ|=(nk=1|ϕk(θ)|2τ)12,

    where |ϕk(θ)|τ=supτθ0|ϕk(θ)|. For a practical consideration, the initial condition of system (1.3) can be considered as

    Ik(θ)=ϕk(θ),Sk(θ)=1ϕk(θ),θ[τ,0],k=1,2,,n, (1.4)

    where ϕ=(ϕ1,ϕ2,,ϕn)C.

    In this paper, we focus on the dynamical behaviors of system (1.3). The existence, uniqueness and boundedness of the solution to system (1.3) are discussed in Section 2. In Section 3, we investigate the dynamics of system (1.3) and present sufficient conditions for the exponential extinction and permanence of the disease, respectively. Numerical simulations are given to demonstrate the theoretical results in Section 4. Section 5 draws the conclusion.

    Considering the practical meaning, the first concern is whether there exists a global positive and bounded solution to system (1.3). That is to verify the well-posedness of system (1.3). In this section, we shall discuss this issue by the means of Lyapunov analysis method [23]. Denote

    Ω={(S1,I1,,Sn,In)R2n+|Sk+Ik=1,k=1,,n}.

    Theorem 2.1. For any initial condition (1.4), there exists a unique global solution to system (1.3) on t>0. Moreover the solution remains in Ω almost surely (a.s.) for all times.

    Proof. Because of the locally Lipschitzian continuity of the coefficients in system (1.3), for any initial condition (1.4) there exists a unique local solution on t[τ,τe), where τe is the explosion time [24]. By summing the equations of system (1.3), we get

    d(Sk+Ik)=[Aγ(Sk+Ik)]dt,t[τ,τe).

    Noting that A=γ and initial condition (1.4), it follows that

    Sk+Ik=(1Sk(0)Ik(0))eγt+1=1,t[τ,τe). (2.1)

    To verify that the solution is global, we only need to show that τe=+. Now, we show this by proving a stronger property of the solution, i.e., it always remains in Ω a.s.. Define the stopping time as

    ˉτ=inf{t[τ,τe):mink=1,,nSk(t)0ormink=1,,nIk(t)0}, (2.2)

    where we let inf=+. According to initial condition (1.4) and property (2.1), ˉτ is the first leaving time of the solution from Ω. Clearly, ˉττe. Thus, we only need to prove ˉτ=+, which implies that the solution remains in Ω for all times. Next, we will prove it by contradiction. If ˉτ< a.s., there would exist a pair of constants T>0 and ϵ(0,1) such that P(ˉτT)>ϵ. Let's define a C2-function V:ΩR+ as

    V(S1,I1,,Sn,In)=nk=1[lnSk+lnIk].

    From the definition of ˉτ, it is easily obtained that

    limtˉτV(S1(t),I1(t),,Sn(t),In(t))=+, (2.3)

    for almost all ω{ˉτT}. On the other hand, using Itô's formula on V for t[0,ˉτ) and ω{ˉτT}, one has

    dV=nk=1[1SkdSk+12S2k(dSk)21IkdIk+12I2k(dIk)2]=nk=1[ΛSk+λ(k)Θ(tτ)edmτ+γμIkSkλ(k)SkΘ(tτ)edmτIk+γ+μ+12(S2k+I2k)σ2ke2dmτΘ2(tτ)]dt+dM(t)nk=1[λ(k)Θ(tτ)edmτ+(2γ+μ)+12(S2k+I2k)σ2ke2dmτΘ2(tτ)]dt+dM(t)nk=1[λ(k)φ(k)edmτk+(2γ+μ)+σ2ke2dmτφ(k)28k2]dt+dM(t):=Kdt+dM(t), (2.4)

    where

    M(t)=t0nk=1σk(2Ik(s)1)Θ(sτ)edmτdBk(s).

    Integrating both sides of (2.4) from 0 to t and then letting tˉτ, for almost all ω{ˉτT}, we get

    V(S1(t),I1(t),,Sn(t),In(t))V(S1(0),I1(0),,Sn(0),In(0))Kt+M(t)Kˉτ+M(ˉτ)<+,

    which contradicts with (2.3). Thus, ˉτ=τe=+. This completes the proof.

    By Theorem 2.1, if initial functions (S1(θ),I1(θ),,Sn(θ),In(θ))Ω for all θ[τ,0], then

    P((S1(t),I1(t),,Sn(t),In(t))Ω)=1,t0.

    That is to say, the bounded region Ω is the almost surely positive invariant set of system (1.3). From now on, we always assume that (S1(t),I1(t),,Sn(t),In(t))Ω.

    Since Sk+Ik=1, as deterministic system (1.1), we analyze the following equivalent system of (1.3) instead

    dIk=[λ(k)(1Ik)Θ(tτ)edmτγIkμIk]dt+σkIk(1Ik)Θ(tτ)edmτdBk(t). (3.1)

    Denote I=(I1,,In). System (1.3) can be represented by the vector-valued stochastic differential delay equation

    dI=f(I,I(tτ))dt+g(I,I(tτ))dB(t), (3.2)

    where the k-th component of f is λ(k)(1Ik)Θ(tτ)edmτγIkμIk, g is a diagonal matrix with entries σkIk(1Ik)Θ(tτ)edmτ, k=1,2,,n, and B(t) is an n-dimensional standard Brownian motion with B(0)=0. Define the differential generator associated with system (3.2) as

    L=t+nk=1fk(I,I(tτ))Ik+12nk=1g2kk(I,I(tτ))2I2k.

    By Itô's formula, we have

    dV(t,I)=LVdt+VIg(I,I(tτ))dB(t).

    Obviously, the disease-free equilibrium I0 of system (1.2) is also that of stochastic system (3.1). In the following, the exponential stability of I0 for system (3.1) will be deduced by Razumikhin-Mao type theorem [25].

    Theorem 3.1. If R0<1, then for any initial condition (1.4), the disease-free equilibrium I0 is p-th (p>0) moment exponentially stable in D for system (3.1), and it is also almost surely exponentially stable in D. Moreover,

    lim suptln|I|t(γ+μ)(1qR0), (3.3)
    lim suptlnE|I|ptp(γ+μ)(1qR0), (3.4)

    where q(1,1R0) is the unique root of equation (γ+μ)(1qR0)τ=lnq and || represents the Euclidean norm of a vector or the trace norm of a matrix.

    Proof. First, we prove the first moment exponential stability and a.s. exponential stability. Define the function Θ:DR+ as

    Θ=1knk=1φ(k)P(k)Ik(t). (3.5)

    Obviously,

    ϱ_knk=1IkΘ¯ϱknk=1Ik,

    where ϱ_=min{φ(k)P(k),k=1,,n} and ¯ϱ=max{φ(k)P(k),k=1,,n}. Using Cauchy-Schwarz inequality, we get

    ϱ_k|I|Θn¯ϱk|I|. (3.6)

    Noting that 0<Ik<1, a direct calculation yields

    LΘ=edmτknk=1λ(k)φ(k)P(k)(1Ik)Θ(tτ)(γ+μ)Θ(γ+μ)Θ+R0(γ+μ)Θ(tτ). (3.7)

    By Theorem 6.4 of Mao [25], if R0<1, the trivial solution I0 of system (3.1) is the first moment exponentially stable.

    For system (3.1), by triangle inequality, it can be easily obtained that

    |f(I,I(tτ))|¯λedmτΘ(tτ)|1I|+(γ+μ)|I|, (3.8)

    where ¯λ=max{λ(k),k=1,2,n}. Since 0<Ik<1, one has |1I|<n. Combining with (3.6), it follows from (3.8) that

    |f(I,I(tτ))|n¯λ¯ϱedmτk|I(tτ)|+(γ+μ)|I|.

    It holds that Ik(1Ik)14 for 0<Ik<1, which together with (3.6) yields

    |g(I,I(tτ))|14σmaxedmτΘ(tτ)14nσmaxedmτ¯ϱ|I(tτ)|.

    By Theorem 6.4 of Mao [25], the trivial solution I0 of system (3.1) is a.s. exponentially stable and the estimate (3.3) of its sample Lyapunov exponent holds.

    Next, we prove the p-th moment (p>0) exponential stability. By Jensen's inequality, we get

    lim suptlnE|I|ptlim suptE[ln|I|pt]. (3.9)

    Since 0<Ik<1, then ln|I|p/t<plnn holds on t1. Using inverse Fatou's lemma, it follows that

    lim suptE[ln|I|pt]E[lim suptln|I|pt]=pE[lim suptln|I|t],

    which together with (3.3) and (3.9) implies the p-th moment exponential stability and the estimate (3.4). This completes the proof.

    The condition for extinction of the disease has been derived in Theorem 3.1. Another problem we are interested in is when the disease will prevail. For deterministic system (1.2), as mentioned in the Introduction, when R0>1 there is a globally stable endemic equilibrium I, which implies the prevalence. However, there is no endemic equilibrium for system (3.1). Next, we will study dynamics of system (3.1) around I to reveal the persistence of the disease.

    Theorem 3.2. If R0>1, then for any initial condition (1.4), the solution of (3.1) has the property

    lim supt1tt0nk=1φ(k)P(k)(Ik(s)Ik)2dsσ2maxe2dmτφ(k)32(γ+μ)k2, (3.10)

    where σ2max=max{σ2k,k=1,,n}, and I=(I1,,In) is the endemic equilibrium of corresponding deterministic system (1.2).

    Proof. When R0>1, the unique endemic equilibrium I of deterministic system (1.2) satisfies

    λ(k)SkΘedmτ=γIk+μIk, (3.11)

    where Sk=1Ik,Θ=1knj=1φ(j)P(j)Ij. Define a C2-function V:DR+ as

    V(I(t))=1knk=1φ(k)P(k)VSk(t)+VΘ(t)+pttτVΘ(s)ds,

    where Sk=1Ik and

    Vx(t)=x(t)xxlnx(t)x,p=1knk=1λ(k)P(k)φ(k)edmτSk,

    in which x=Sk,Θ. Clearly, V(I) is positive definite, i.e. V(I)=0 and V(I)>0,II.

    Applying Itô's formula to V along the solution of system (3.1), together with (3.11) and the property Sk+Ik=1, we have

    dVSk=(1SkSk)(dIk)+Sk2Sk(dIk)2=[(1SkSk)[λ(k)edmτ(SkΘ(tτ)SkΘ)+(γ+μ)(IkIk)]+Sk2σ2kI2kΘ2(tτ)e2dmτ]dt+σkIk(SkSk)Θ(tτ)edmτdBk(t)=[(γ+μ)(IkIk)2Sk+λ(k)edmτSkΘ(SkΘ(tτ)SkΘ+1+Θ(tτ)ΘSkSk)+Sk2σ2kI2kΘ2(tτ)e2dmτ]dt+σkIk(SkSk)Θ(tτ)edmτdBk(t):=LVSkdt+dMSk(t), (3.12)

    where

    MSk(t)=t0σkIk(s)(Sk(s)Sk)Θ(τs)edmτdBk(s).

    Since (3.11) holds, one has

    γ+μ=edmτknk=1λ(k)φ(k)P(k)Sk. (3.13)

    Using Itô's formula on VΘ and then substituting (3.13) into it, it follows that

    dVΘ(t)=(1ΘΘ)dΘ+Θ2Θ2(dΘ)2=[(1ΘΘ)edmτknk=1λ(k)φ(k)P(k)(SkΘ(tτ)SkΘ)+Θe2dmτ2Θ2k2nk=1σ2kφ2(k)P2(k)I2kS2kΘ2(tτ)]dt+dMΘ(t)=[edmτknk=1λ(k)φ(k)P(k)SkΘ(SkΘ(tτ)SkΘΘΘSkΘ(tτ)SkΘ+1)+Θe2dmτ2Θ2k2nk=1σ2kφ2(k)P2(k)I2kS2kΘ2(tτ)]dt+dMΘ(t):=LVΘdt+dMΘ(t), (3.14)

    where

    MΘ(t)=edmτknk=1t0(1ΘΘ(s))σkφ(k)P(k)Ik(s)Sk(s)Θ(sτ)dBk(s).

    From formulas (3.12) and (3.14), it can be obtained that

    LV=1knk=1φ(k)P(k)LVSk+LVΘ+p(ΘΘ(tτ)ΘlnΘΘ(tτ))=γ+μknk=1φ(k)P(k)(IkIk)2Sk+edmτknk=1λ(k)φ(k)P(k)SkΘ[H(SkSk)H(SkΘ(tτ)SkΘ)]+e2dmτ2knk=1σ2kφ(k)P(k)SkI2kΘ2(tτ)+Θe2dmτ2Θ2k2nk=1σ2kφ2(k)P2(k)I2kS2kΘ2(tτ),

    where H(x)=x1lnx. Since 0<Sk<1, then

    1k2nk=1σ2kφ2(k)P2(k)I2kS2kσ2maxk2(nk=1φ(k)P(k)Ik)2=σ2maxΘ2. (3.15)

    Using the fact that H(x)0 for x>0 and the property 0<Sk,Ik<1, together with (3.15), it follows that

    LVγ+μknk=1φ(k)P(k)(IkIk)2+σ2maxe2dmτφ(k)22k2(1knk=1φ(k)P(k)Sk+Θ)=γ+μknk=1φ(k)P(k)(IkIk)2+σ2maxe2dmτφ(k)32k3:=F(t).

    Thus,

    dVF(t)dt+1knk=1φ(k)P(k)dMSk(t)+dMΘ(t). (3.16)

    Integrating both sides of (3.16) from 0 to t yields

    V(t)V(0)t0F(s)ds+1knk=1φ(k)P(k)MSk(t)+MΘ(t). (3.17)

    Obviously, MΘ is a continuous local martingale with M(0)=0. By formula (3.15) and the property 0<Ik<1, we obtain that

    1tMΘ,MΘt=e2dmτk2tt0(1ΘΘ(s))2nk=1σ2kφ2(k)P2(k)I2k(s)S2k(s)Θ2(sτ)dse2dmτσ2maxtt0(Θ(s)Θ)2Θ2(sτ)dse2dmτσ2maxtt0(Θ2(s)+Θ2)Θ2(sτ)dse2dmτσ2maxφ(k)2k2(φ(k)2k2+Θ2)<+.

    By Strong Law of Large Numbers [25], we get

    limt+MΘ(t)t=0,a.s.. (3.18)

    Similarly,

    limt+MSk(t)t=0,a.s.. (3.19)

    Because of the positivity of V, it follows from (3.17)-(3.19) that

    lim supt1tt0F(s)ds0,

    which implies the property (3.10). Thus, Theorem 3.2 is proved.

    Remark 3.1. Theorem 3.2 shows that, when R0>1, the Euclidian distance between the solution I(t) and the endemic equilibrium I of system (1.2) in time average takes the following form

    lim supt1tt0|I(s)I|2dsCe2dmτσ2max,

    where C is a positive constant. It indicates that the solution of system (3.1) oscillates around I. The amplitude is correlated positively with the noise intensities but negatively with the time delay. It is reasonable that the solution is approximately stable, given that the disturbance intensities are sufficiently small. Under this assumption, we infer that the disease will prevail.

    We denote by ˉI(t) the average relative density of infected nodes at time t, then

    ˉI(t)=nk=1P(k)Ik(t). (3.20)

    From the result of Theorem 3.2, we conclude that system (3.1) is persistent, which also reflects that the disease is prevalent.

    Definition 3.1. [26] System (3.1) is said to be persistent in the mean, if

    lim inft1tt0ˉI(s)ds>0,a.s..

    Corollary 3.1. If R0>1 and

    nk=1φ(k)P(k)I2k>σ2maxe2dmτφ(k)32(γ+μ)k2, (3.21)

    then system (3.1) is persistent in the mean.

    Proof. Obviously,

    2IkIkI2k(IkIk)2,k=1,2,,n,

    which together with (3.10) and (3.21) yields

    lim inft1tt0nk=1φ(k)P(k)Ik(s)Ikds12nk=1φ(k)P(k)I2k12lim supt1tt0nk=1φ(k)P(k)(Ik(s)Ik)2ds12nk=1φ(k)P(k)I2kσ2maxe2dmτφ(k)34(γ+μ)k2>0. (3.22)

    Consequently,

    lim inft1tt0ˉI(s)ds1ϑlim inft1tt0nk=1φ(k)P(k)Ik(s)Ikds>0,

    where ϑ=max{φ(k)Ik,k=1,,n}. This completes the proof.

    In this section, numerical simulations of system (3.1) are shown to illustrate the theoretical results aforesaid. Moreover, we numerically simulate the solution of corresponding deterministic system (1.2) for comparison.

    Consider a finite scale-free network whose maximum connectivity of any node n=100. The degree distribution of the network is P(k)=Ckr with r=2.3, nk=1P(k)=1. We fix the parameters γ=0.06,μ=0.05,dm=0.2. Let λ(k)=λk and φ(k)=akα/(1+bkα) [27] with α=0.75,a=0.8,b=0.01. The initial functions are Ik(t)=0.05,k=1,,n for t[τ,0]. By the Milstein's method [28], the discretized difference equations of system (3.1) are represented by

    Ik,i+1=Ik,i+[λk(1Ik,i)Θimedmτ(γ+μ)Ik,i]Δt+σkIk,i(1Ik,i)Θimedmτξk,iΔt+12σ2kI2k,i(1Ik,i)2Θ2ime2dmτ(ξ2k,i1)Δt, (4.1)

    where m=τ/Δt, ξk,i(k=1,,n,i=1,,N) are independent standard normal random variables, and

    Θim=1knk=1φ(k)P(k)Ik,im. (4.2)

    First, we consider the values of λ and τ such that R0<1. Fig. 1 (imaginary lines) shows that the solution of deterministic system (1.2) converges to zero. Besides, Figure 1 (solid lines) depicts the dynamical behaviors of infected nodes with degree k=10,35,80 by computing one sample path of the solution to system (3.1). The noise intensities σk=10 and σk=30,k=1,,100 in (a) and (b), respectively. It can be seen that the noise intensities do not affect the ultimate trend of the solution. It confirms the stability result in Theorem 3.1.

    Figure 1.  Dynamics of relative densities of infected nodes with degree 10,35, and 80, where λ=0.04, τ=3, R0=0.6972: the solution of system (1.2) versus system (3.1). (a) σk=10, (b) σk=35, k=1,2,,100.

    Second, we choose the values of λ and τ such that R0>1. Then there is an endemic equilibrium I for system (1.2) and it is globally asymptotically stable, which is illustrated in Figure 2 (imaginary lines). Figure 2 (solid lines) shows one sample path of the solution to system (3.1) with the same τ=3 but different noise intensities. Specifically, the noise intensities σk=0.5 in (a) and σk=1 in (b) for k=1,,100, which all meet condition (3.21). As expected, we can recognize the behavior in Theorem 3.2, that the solution of system (3.1) fluctuates around I. Moreover, the oscillation amplitude is positively correlated with the noise intensities.

    Figure 2.  Dynamics of relative densities of infected nodes with degree 10,35, and 80, where λ=0.13, τ=3 R0=2.3876: the solution of system (1.2) versus system (3.1). (a) σk=0.5, (b) σk=1, k=1,2,,100.

    Finally, we simulate dynamical behaviors of system (3.1) in the case where R0>1 and the condition (3.21) is unsatisfied. In Figure 3(a), the average relative density ˉI(t) of system (3.1) does not fluctuate around that of deterministic system (1.2). Its fluctuation center seems less than the infection level of system (1.2). Figure 3(b) shows the density ˉI(t) of system (3.1) averaged over 100 sample paths. It is not in excess of the infection level of system (1.2). In other words, the infection level of system (1.2) provides an upper bound for it. Therefore, we surmise that the strong noise intensities may reduce the infection size in statistical average meaning.

    Figure 3.  Dynamics of average infected density ˉI(t) with λ=0.08, R0=1.8367, σk=40,k=1,2,,100: system (1.2) versus system (3.1). (a) one sample path of the relative density ˉI(t), (b) the relative density ˉI(t) averaged over 100 sample paths.

    In addition, we study the influence of the time delay. In Figure 4 (solid lines), we depict one sample path of the solution with the same noise intensities but different time delays. By comparing Figure 2(b), Figure 4(a) and Figure 4(b), it is found out that when R0>1, the oscillation amplitude reduces with the time delay increasing. Moreover, as it continues to increase such that R0<1, the disease will die out.

    Figure 4.  Dynamics of relative densities of infected nodes with degree 10,35, and 80, where λ=0.13, σk=1, k=1,2,,100: the solution of system (1.2) versus system (3.1). (a) τ=1, R0=3.5619, (b) τ=4.5, R0=1.7688.

    An SIS epidemic model with time delay and stochastic perturbation on scale-free networks is established in this paper. Here, we introduce the incubation period of the disease in a vector's body as time delay and enter stochastic perturbation in the infection rate. We prove that it possesses a unique global solution that remains within Ω whenever it starts from this region. Then we mainly discuss the dynamics of the stochastic system. The basic reproduction number R0 of the corresponding deterministic system is a critical parameter. The disease-free equilibrium I0 is exponentially stable (a.s. and p-th moment) when R0<1, which means rapid extinction of the disease. Compared with the deterministic system, stochastic perturbation does not change the final extinction of the disease. Conversely, we analyze permanence of the stochastic system. Under the conditions R0>1 and sufficiently weak noise intensities, the solution of the stochastic system ultimately fluctuates around endemic equilibrium I, which implies permanence in the mean. Moreover, the oscillation amplitude gets smaller with the white noise intensities decreasing, while it becomes greater with the time delay decreasing.

    These results reveal the combined influences of time delay and stochastic perturbation on the dynamics of SIS epidemic model on networks. One may analyze the same issue about SIR [6], SIQS [7] model on scale-free networks and so on. In particular, because of little research about the SIRA model [29,30] in heterogeneous networks, this model itself and influences of time delay and stochastic perturbation on it are all worth studying. These will be considered in our future work.

    This research was supported by the Foundation for Basic Disciplines of Army Engineering University under Grant no. KYSZJQZL2011.

    The authors declare that they have no conflict of interest.



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