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Research article

Dynamics and approximation of positive solution of the stochastic SIS model affected by air pollutants


  • Received: 09 December 2021 Revised: 07 January 2022 Accepted: 10 February 2022 Published: 03 March 2022
  • In this paper, we develop a stochastic susceptible-infective-susceptible (SIS) model, in which the transmission coefficient is a function of air quality index (AQI). By using Markov semigroup theory, the existence of kernel operator is obtained. Then, the sufficient conditions that guarantee the stationary distribution and extinction are given by Foguel alternative, Khasminskˇl function and Itô formula. Next, a positivity-preserving numerical method is used to approximate the stochastic SIS model, meanwhile for all p>0, we show that the algorithm has the pth-moment convergence rate. Finally, numerical simulations are carried out to illustrate the corresponding theoretical results.

    Citation: Qi Zhou, Huaimin Yuan, Qimin Zhang. Dynamics and approximation of positive solution of the stochastic SIS model affected by air pollutants[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4481-4505. doi: 10.3934/mbe.2022207

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  • In this paper, we develop a stochastic susceptible-infective-susceptible (SIS) model, in which the transmission coefficient is a function of air quality index (AQI). By using Markov semigroup theory, the existence of kernel operator is obtained. Then, the sufficient conditions that guarantee the stationary distribution and extinction are given by Foguel alternative, Khasminskˇl function and Itô formula. Next, a positivity-preserving numerical method is used to approximate the stochastic SIS model, meanwhile for all p>0, we show that the algorithm has the pth-moment convergence rate. Finally, numerical simulations are carried out to illustrate the corresponding theoretical results.



    Nowadays, hundreds of millions of residents have shrouded in the heavy smog especially in winter [1]. Numerous epidemiological studies have shown associations of particulate air pollution with risk for various adverse health outcomes, in which the most affected pathologies are chronic obstructive pulmonary disease, lung cancer, influenza and respiratory infectious diseases (e.g., measles) [2,3,4,5,6,7]. Therefore, it is of great significance to study the respiratory infectious disease affected by air pollution through mathematical means.

    Actually, for the respiratory infectious disease, the susceptible-infective(SI) and SIS models are usually used to describe this epidemic dynamic. For instance, Wang et al.[8] discussed the persistence and periodic orbits for a SIS model in a polluted environment. Liu et al.[9] analyzed dynamics behavior of an SI epidemic model. Note that these studies did not consider the influence of random environmental factors. In fact, AQI depends on pollutants emissions, weather conditions, wind speed and air temperature, which are generated randomly. Therefore, it is more reasonable to use stochastic differential equation to establish an infectious disease model with air pollution.

    Considering random air pollution, there have been many studies on epidemic models. For instance, He et al.[10] studied the dynamics of infectious respiratory disease spread by developed a stochastic SIS model. Zhao et al.[11] discussed the existence of a positive stochastic periodic solution of a SI epidemic model by constructing a nonnegative function. He et al.[12] analyzed dynamics behavior of a stochastic SIS model by applying Itô formula. However, it is worth pointing out that stationary distribution of epidemic models with stochastic air pollution has not been investigated until now.

    In addition, it is almost impossible to know the analytical solution of stochastic SIS epidemic model. In view of this, in order to quickly predict the number of infected people at a certain time t, it is important to seek an effective numerical algorithm. It is well known that classical Euler-Maruyama (EM) algorithm is widely used to approximate stochastic differential equation (SDE) because of its fast calculation speed and simple format. However, if EM method is used to approximate stochastic SIS model, it will produce negative values [13], which is meaningless. Hence, there is the challenging work to establish positive preserving numerical algorithm for stochastic SIS model which the transmission coefficient is a function of AQI by using EM scheme.

    In this paper, we study dynamics and approximation of positive solution of the stochastic SIS model. Our main contributions of the work are as follows:

    ● We develop a stochastic SIS model with air pollution which the transmission coefficient is a function of AQI. It extends the work of [12].

    ● We identify the conditions that guarantee the extinction and the stationary distribution by Foguel alternative, Khasminskˇl function and Itô formula.

    ● We establish the positive preserving numerical algorithm for stochastic SIS model which the transmission coefficient is a function of AQI through the logarithmic transformed EM method and prove the positive preserving algorithm has first-order convergence rate. It extends the work of [14,15,16] which can only achieve convergence rate of 1/2.

    The rest of the paper is so organized as. In Section 2, preliminaries and introduction of a stochastic SIS model affected by air pollutants. The existence of kernel operator and the sufficient conditions that guarantee the stationary distribution and the extinction are obtained in Section 3. In Section 4, to approximate the solution a positivity-preserving numerical method is introduced. Section 5 verifies the theoretical results by numerical simulations. In Section 6, we conclude the paper.

    In this section, we obtain a stochastic SIS model affected by air pollutants and some preliminary knowledge is given for future needs.

    Since the proof of result is based on the theory of integral Markov semigroups, we need some auxiliary definitions and results concerning Markov semigroups (see, [17,18,19]). For the convenience of the reader, these definitions and results are presented. Let Σ=B(X) be the σ-algebra of Borel subset of X and m the σ-finite measure on (X,Σ), then the triple (X,Σ,m) be a σ-finite measure space. Denote by D the subset of the space L1=L1(X,Σ,m) which contains all densities, i.e.,

    D={fL1:f0,f=1},

    where represent the norm in L1. A linear mapping P:L1L1 is called a Markov operator if P(D)D.

    Assume that k:X×X[0,) is a measurable function such that

    Xk(x,y)m(dx)=1 (2.1)

    for almost all yX, and

    Pf(x)=Xk(x,y)f(y)m(dy)

    is an integral Markov operator. The function k is called a kernel operator of the Markov operator P.

    A family {P(t)}t0 of Markov operator is called a Markov semigroup, if {P(t)}t0 satisfies

    (a) P(0)=Id,

    (b) P(t+s)=P(t)P(s) for s,t0,

    (c) The function tP(t)f is continuous for every fL1.

    A Markov semigroup {P(t)}t0 is called integral, if for each t>0, the operator P(t) ia an integral Markov operator, i.e., there exists a measurable function k:(0,)×X×X[0,) such that

    P(t)f(x)=Xk(t,x,y)f(y)m(dy)

    for every density f.

    We need also two definitions concerning the asymptotic behavior of a Markov semigroup. A density f is called invariant if P(t)f=f for each t>0. The Markov semigroup {P(t)}t0 is called asymptotically stable if there is an invariant density f such that

    limtP(t)ff=0forfD.

    A Markov semigroup {P(t)}t0 is called sweeping with respect to a set AΣ if for every fD

    limtAP(t)f(x)m(dx)=0.

    The following Lemma summarizes result of asymptotic stability and sweeping.

    Lemma 2.1. Let X be a metric space and Σ be the σ-algebra of Borel sets [17]. Let {P(t)}t0 be an integral Markov semigroup with a continuous kernel k(t,x,y) for t>0, which satisfies Eq (2.1) for any yX. We assume that for every fD we have

    0P(t)f(x)dt>0a.e.,

    then the semigroup {P(t)}t0 is asymptotically stable or is sweeping with respect to compact sets.

    The property that a Markov semigroup {P(t)}t0 is asymptotically stable or sweeping from a sufficiently large family of sets(e.g. from all compact sets) is called the Foguel alternative.

    Throughout this paper we use the following notation. || is Euclidean norm on R. denote the empty set. Given a,bR, ab and ab denote the maximum of a and b and the minimum of a and b, respectively. For any Δ(0,1] and T(0,), T/Δ represents the integer part of T/Δ. C stands for the generic positive real constants whose value may change between occurrences and is independent of Δ and positive integer T.

    According to the mechanism of respiratory infectious diseases, He et al.[12] proposed the following model:

    {dS(t)dt=γI(t)βF(t)S(t)I(t),dF(t)dt=cθF(t),dI(t)dt=βF(t)S(t)I(t)γI(t), (2.2)

    with initial values S0,F0 and I0, where S(t), I(t) and F(t) represent the number of susceptible humans, the number of infected humans and AQI at time t, respectively. c, θ and γ denote the inflow rate of pollutants into the air, the clearance rate of pollutants and the recovery rate for infected individuals, respectively. βF(t) is the infection rate. S(t)+I(t)=N and N is the total number of people. c, θ, γ and β are non-negative numbers.

    Because the inflow rate of pollutants c including the emission of automobile exhaust, industrial exhaust and so on is affected by random noise. Hence, we now perturb the inflow rate of pollutants c. In this case, c changes to a random variable and then we replace c by c+σB(t), where B(t) is a white noise. In addition, the infectious disease model with birth rate and natural mortality is more realistic. Therefore, based on Eq (2.2), we can get the following model:

    {dS(t)=[μNβF(t)S(t)I(t)+γI(t)μS(t)]dt,dF(t)=[cθF(t)]dt+σdB(t),dI(t)=[βF(t)S(t)I(t)γI(t)μI(t)]dt. (2.3)

    where B(t) is a standard Brownian motion defined on a complete probability space (Ω,F,P) with a filtration {F0}t0 satisfying the usual conditions(i.e., it is right continuous and F0 contains all P-null sets). μ and σ denote the per capita death rate and the noise intensity. μ and σ are non-negative numbers. The meaning of other symbol is similar to that in Eq (2.2). Since the second equation of model (2.3) does not appear S(t) and I(t), through the same method as literature[12] the stochastic SIS model affected by air pollutants can be represented as follow:

    {dS(t)=[μN+γI(t)βm(t)S(t)I(t)μS(t)]dtσn(t)S(t)I(t)dB(t),dI(t)=[βm(t)S(t)I(t)γI(t)μI(t)]dt+σn(t)S(t)I(t)dB(t) (2.4)

    where m(t)=cθ+(F0cθ)eθt, n(t)=βθ(1eθt). Combining with S(t)+I(t)=N, it is immediate to get an equation about I(t):

    dI(t)=[βm(t)(NI(t))γμ]I(t)dt+σn(t)(NI(t))I(t)dB(t). (2.5)

    In this section, the existence and uniqueness of the positive solution of Eq (2.5) is proved. Then, we have obtained the sufficient conditions that guarantee the existence of stationary distribution and the extinction of the disease.

    In order to make Eq (2.5) meaningful, we need to show not only that it has a unique global solution but also that the solution will remain within (0,N) whenever it starts from (0,N). To guarantee these properties, it is therefore necessary to establish the following theorem.

    Theorem 3.1. For any given initial value I0(0,N), there exists a unique global positive solution I(t)(0,N) for allt0 with probability one, namely,

    P{I(t)(0,N)t0}=1.

    Proof. By the same method as literature[12], we can complete the proof.

    Denote

    R+={xR:x>0}. (3.1)

    Since the existence of positive solution of Eq (2.5) has been obtained by Theorem 3.1, we let X=R+. Moreover, it is easy to check that the region Γ={(S(t),I(t))R+×R+:0<S(t)+I(t)<N} is a positively invariant set of Eq (2.4). Hence, we always assume that (S0,I0)Γ.

    For Eq (2.5), we get the condition which lead to the extinction of the disease under the stochastic disturbance. Denote

    Rs0=βcNθ(λ+μ)σ2β2N22θ2(λ+μ), (3.2)

    which can be seen as a threshold of the extinction (i.e., disease-free) or persistence (i.e., endemic) of disease for Eq (2.5).

    Theorem 3.2. If Rs0<1 and δ2<cθβN, then for any initial value I(0)(0,N) the solution I(t) of Eq (2.5) has the following property:

    P{limtI(t)=0}=1. (3.3)

    That is, the disease will die out with probability one.

    Proof. By Itô formula, it is easy to get that

    lim suptlnI(t)t=lim supt[lnI(0)t+t0(f1(I(s))+f2(I(s),s))dst+t0f3(I(s),s)dB(s)t], (3.4)

    where

    f1(x)=σ2β22θ2(Nx)2+βcθ(Nx)λμ,
    f2(I,t)=[β(F0cθ)(NI)+σ2β2θ2(NI)2σ2β22θ2(NI)2eθt]eθt, (3.5)
    f3(I,t)=σn(t)(NI).

    For the function f1(I(s)), when

    σ2β2θ2Nβcθ<0 , i.e. σ2<cθβN,

    it is immediate to get that

    f1(I)=σ2β22θ2(N2+I2)+(σ2β2θ2Nβcθ)I+βcθN(λ+μ)<σ2β22θ2N2+βcθN(λ+μ)=(λ+μ)(βcNθ(λ+μ)σ2β2N22θ2(λ+μ)1)=(λ+μ)(Rs01). (3.6)

    From Eq (3.5), it follows that

    lim supt1tt0f2(I(s),s)ds=lim supt1tt0β(F0cθ)(NI(s))eθsds+lim supt1tt0σ2β2θ2(NI(s))2eθsdslim supt1tt0σ2β22θ2(NI(s))2e2θsds. (3.7)

    With the help of the L'Hospital law, it is immediate to get that

    lim supt1tt0|β(F0cθ)(NI(s))eθs|dslim supt|β(F0cθ)Neθt|=0

    Hence, it follows that

    lim supt1tt0β(F0cθ)(NI(s))eθsds=0. (3.8)

    Similarly, we can get

    lim supt1tt0σ2β2θ2(NI(s))2eθsds=lim supt1tt0σ2β22θ2(NI(s))2e2θsds=0. (3.9)

    Bring Eqs (3.8)–(3.9) into Eq (3.7), it is easy to get that

    lim supt1tt0f2(I(s),s)ds=0. (3.10)

    Through the large number theorem for martingales, it is easy to get

    lim supt1tt0f3(I(s),s)ds=0. (3.11)

    Bring Eqs (3.6), (3.10) and (3.11) into Eq (3.4), it follows that lim suptlnI(t)t=0. Therefore, based on the analyses above we conclude that I(t) tends to zero exponentially almost surely. This completes the proof.

    For any AΣ, the transition probability function is denoted by P(t,x0,A) for the diffusion process I(t), i.e.,

    P(t,x0,A)=Prob{I(t)A}

    with the initial value I(0)=x, where I(t) be a solution of Eq (2.5) such that the distribution of I(0) is absolutely continuous and has the density v(x). Then I(t) has also the density U(t,x), where U(0,x)=v(x), and U(t,x) satisfies the following Fokker-Planck equation (see [19,20])

    Ut=12σ2n2(t)2(x2(Nx)2U)x2(fU)x, (3.12)

    where

    f(x)=(βm(t)(Nx)γμ)x.

    Now we introduce a Markov semigroup associated with Eq (3.12). Let P(t)v(x)=U(t,x) for v(x)D. Since the operator P(t) is a contraction on D, it can be extended to a contraction on L1. Hence, the operator {P(t)}t0 generates a Markov semigroup. Denote A the infinitesimal generator of semigroup {P(t)}t0, i.e.,

    Av=12σ2n2(t)2(x2(Nx)2v)x2(fv)x.

    The adjoint operator of A is as follows:

    Av=12σ2n2(t)x2(Nx)22vx2+(fv)x.

    For the convenience of discussing the main results of this section, Some useful lemmas are given.

    Lemma 3.1. For every point x0(0,N) and t>0, the transition probability function P(t,x0,A) has a continuous density k(t,x,x0)C(R+,R+,R+).

    Proof. Let

    b(ϕ)=σn(t)(Nϕ)ϕ,

    where ϕ(0,N). Since for any ϕ(0,N) we have b(ϕ)>0, b(ϕ) span the space (0,N). Based on Hörmander theorem(See Theorem 8 in [19]), the transition probability function P(t,x0,A) has a continuous density k(t,x,x0)C(R+,R+,R+).

    According to Lemma 3.1, it follows that for any fD,

    P(t)f(x)=N0k(t,x,u)f(u)du.

    Hence, the semigroup {P(t)}t0 is an integral Markov semigroup. Next, we rewrite Eq (2.5) of Itô type as Stratonovitch type:

    ˙I(t)=ˆf(I(t))dt+σn(t)(NI(t))I(t)dB(t),

    where

    ˆf(x)=[βm(t)(Nx)γμ]x12σ2n2(t)(Nx)(N2x)x.

    Now we briefly describe the method based on support theorems [21,22,23] which allows us to check where the kernel k is positive. Fixing a point x0R+ and a function φL2([0,T];R), consider the following system of integral equations:

    xφ(t)=x0+t0[ˆf(xφ(s))+σn(s)φ(Nxφ(s))xφ(s)]ds. (3.13)

    Let Dx0,φ be the Frechét derivative of the function hxφ+h(t) from L2([0,T];R) to R. If for some φL2([0,T];R) the Frechét derivative Dx0,φ has rank 1, then k(t,x,x0)>0 for x=xφ(T). The derivative Dx0,φ can be found by means of the perturbation method for ordinary differential equations. Let Γ(t)=ˆf(xφ(t))+b(xφ(t))φ. For 0t0tT, let Q(t,t0) is the matrix function such that Q(t0,t0)=1, Q(t,t0)t=Γ(t)Q(t,t0). Then,

    Dx0,φh=T0Q(T,s)b(s)h(s)ds.

    Lemma 3.2. For each x0R+, and for every xR+, there exists T>0 such that k(t,x,x0)>0.

    Proof. We first verify that the rank of Dx0,φ is 1. Let ϵ(0,T) and h(t)=1[Tϵ,T](t)n(t)xφ(t)(Nxφ(t)), t[0,T], where 1[Tϵ,T](t) is the characteristic function of interval [Tϵ,T]. Since Q(T,s)=1Γ(T)(Ts)+o(Ts), it is easy to get that

    Dx0,φh=ϵσ+12ϵ2Γ(T)σ+o(ϵ2).

    Since Γ(T)0, Dx0,φ has rank 1.

    Next we show that for any two points x0R+ and xR+, there exist a control function φ and T>0 such that xφ(0)=x0,xφ(T)=x. The Eq (3.13) can be replaced by the following equation:

    ˙xφ(t)=ˆf(xφ(t))σn(t)φ(Nxφ(t))xφ(t).

    By the same discussion as in reference [24], it is easy to find a control function φ and T>0 such that xφ(0)=x0,xφ(T)=x1. This claims k(t,x,x0)>0.

    Lemma 2.1 and Lemma 3.2 yield that the Markov semigroup {P(t)}t0 is asymptotically stable or is sweeping. Next, the sufficient condition that guarantee Markov semigroup {P(t)}t0 is asymptotically stable will be given by using Foguel alternative.

    Theorem 3.3. Let (S(t),I(t)) be a solution of the Eq (2.4) for any given initial value (S(0),I(0))Γ. If

    Rs0>1andσ2<2μθ2γIβ2N2min{S2,I2}2βmax(I,S)|F0cμ|θ2NIβ2 (3.14)

    hold, then the semigroup {P(t)}t0 is asymptotically stable, where S(t)=βcθ(μ+γ), I(t)=Nβcθ(μ+γ).

    Proof. According to Lemma 3.1, it follows that {P(t)}t0 is an integral Markov semigroup with a continuous kernel k(t,x,x0). Then from Lemma 3.2 for every fD, it is immediate to get that

    0P(t)fdt>0.

    By virtue of Lemma 2.1, it follows that the semigroup {P(t)}t0 is asymptotically stable or sweeping with respect to compact sets. In order to exclude the sweeping case, we shall construct a non-negative C2-function V and a closed set OΣ such that

    sup(S,I)R2+OAV<0.

    Such function is called Khasminskiˇl function. In fact, it is easy to get that Eq (2.4) has an endemic equilibrium E=(S,I)=(βcθ(μ+γ),Nβcθ(μ+γ)). Then we know

    {μN+γIcθβSIμS=0,cθβSIγIμI=0.

    Let

    V=12(SS+II)2+λ(IIIlnII)=V1+λV2.

    where λ=2θμcβ.Then,

    AV1=(SS+II)(μNμIμS)=(SS+II)(μS+μIμIμS)=μ(SS)2μ(II)22μ(SS)(II),
    AV2=(II)(βm(t)Sγμ)+12σ2n2(t)S2I=(II)(βm(t)SβcθS)+12σ2n2(t)S2Iβcθ(SS)(II)+βNmax(I,S)|F0cμ|+12σ2n2(t)S2I,
    AV=AV1+AV2=μ[(SS)2+(II)2]2μ(SS)(II)+λβcθ(SS)(II)+λβNmax(I,S)|F0cμ|+λ2σ2n2(t)S2Iμ[(SS)2+(II)2]+λβNmax(I,S)|F0cμ|+λβ22θ2σ2N2I:=b1(SS)2b1(II)2+b3.

    Conditions Eq (3.14) implies that the ball

    b1(SS)2+b1(II)2=b3.

    lies entirely in the positive zone of R2+. Hence there exists a closed set OΣ which contains the ellipsoid and C>0 such that

    sup(S,I)R2+OAVC<0.

    The proof is hence completed.

    Since it is impossible to know the explicit solution of Eq (2.5), constructing appropriate numerical methods to approximate Eq (2.5) and even preserve properties of Eq (2.5) is important and necessary. In this section, to approximate Eq (2.5) a positivity-preserving numerical method is introduced.

    In order to obtain a positive numerical solution, we first make a transformation

    y(t)=ln(I(t))ln(NI(t))=lnI(t)NI(t). (4.1)

    According to Eq (2.5), applying Itô formula to y(t) yields

    dy(t)=F(y(t))dt+σNn(t)dB(t), (4.2)

    with y0=ln(I0)ln(NI0), where

    F(y(t))=βm(t)N(γ+μ)(1+ey(t))+N2(2Ney(t)1+ey(t)N)σ2n2(t). (4.3)

    Now, we apply the EM scheme to Eq (4.2) then obtain

    X(tk+1)=X(tk)+F(X(tk))Δ+σNn(tk)ΔB(tk), (4.4)

    for any Δ(0,1] and k0, where ΔB(tk)=B(tk+1)B(tk) and tk=kΔ. Transforming back, i.e.,

    I(tk+1)=NN1+eX(tk)=NeX(tk)1+eX(tk),

    gives a strictly positive approximation of Eq (2.5). The forthcoming results concern exponential property of I(t) and X(t), t[0,T], which will be used in convergence analysis of the numerical scheme.

    Theorem 4.1. For any p0

    (supt[0,T]E[Ip(t)])(supt[0,T]E[(NI(t))p])Kp,

    where

    Kp=(Ip0(NI0)p)e[γ+μ+βN2c+θF0θ+p+12(βσNθ)2]pT.

    Proof. Define a continuous function V:(0,N)R+ by

    V=Ip.

    By the Itô formula, it is easy to get that

    dV=p[γ+μβm(t)(NI)+p+12(σn(t)(NI(t)))2]Ipdt+JdB(t)p[γ+μ+βN2c+θF0θ+p+12(βσNθ)2]Ipdt+JdB(t), (4.5)

    where J=pσn(t)(NI)Ip. we choose m0>0 sufficiently large such that 1/m0<I0<N1/m0. For each integer mm0, define a stopping time

    τm=inf{t[0,T]:I(t)(1/m,N(1/m))}.

    Integrating both sides of Eq (4.5) and then taking expectation, it is immediate to get that

    EIp(tτm)=Ip0+Etτm0p[γ+μβm(s)(NI(s))+p+12(σn(s)(NI(s)))2]Ip(s)dsIp0+p[γ+μ+βN2c+θF0θ+p+12(βσNθ)2]Et0Ip(sτm)ds. (4.6)

    With the help of the Gronwall inequality, it follows that

    EIp(tτm)Ip0e[γ+μ+βN2c+θF0θ+p+12(βσNθ)2]pT. (4.7)

    Then, letting m and using the Fatou lemma yield

    supt[0,T]E[Ip(t)]Kp. (4.8)

    According to calculations similar to those for obtaining Eqs (4.6)–(4.8), it is easy to get that

    supt[0,T]E[(NI(t))p]Kp.

    Therefore the desired conclusion holds.

    Remark 4.2. As for moment boundedness, Theorem 4.1 proves the boundedness of inverse moments and we can obtain the boundedness of positive moments by using Theorem 3.2.

    Now, we will concern exponential integrability property of analysis solution y(t), t[0,T] through using Theorem 4.1.

    Theorem 4.3. For any pR, Eq (4.2) has the following exponential integrability property

    supt[0,T]E[epy(t)]N|p|K|p|,

    where Kp is given by Theorem 4.1.

    Proof. Through Eq (4.1) and Theorem 4.1, for any p0, it is easy to get that

    supt[0,T]E[epy(t)]=supt[0,T]E[Ip(t)(NI(t))p]Npsupt[0,T]E[(NI(t))p]NpKp.

    For any p<0, it follows that

    supt[0,T]E[epy(t)]=supt[0,T]E[Ip(t)(NI(t))p]N|p|supt[0,T]E[Ip(t)]N|p|K|p|.

    Hence the desired conclusion holds.

    The forthcoming theorem concerns exponential property of numerical solution X(t), t[0,T], which plays an important role in discussing strong convergence rate of arithmetic.

    Theorem 4.4. For any P>0, Eq (4.4) has the property that

    supΔ(0,1]sup0kT/ΔE[epX(tk)]C,T>0.

    Proof. By Eq (4.1), it is immediate to get that

    F(X(tk1))(F0cθ)βN+σ2N2β2θ2. (4.9)

    Substituting Eq (4.9) into Eq (4.4), it is immediate to get that

    X(tk)X(tk1)+[(F0cθ)βN+σ2N2β2θ2]Δ+σβNθΔB(tk1)y0+[(F0cθ)βN+σ2N2β2θ2]kΔ+σβNθk1i=0ΔBi,

    for any integer k1. For any p>0, using the Itô formula and the Gronwall inequality further yields

    E[epX(tk)]epy0+pT[(F0cθ)βN+σ2N2β2θ2]E[eσpβNθk1i=0ΔBi]epy0+pT[(F0cθ)βN+σ2N2β2θ2]e(σpβN2θ)2TC.

    The proof is complete.

    In this subsection, the first order strong convergence of positivity preserving logarithmic EM method for Eq (2.5) is proved by using Theorem 4.1, Theorem 4.3 and Theorem 4.4.

    Theorem 4.5. For any q>0 there exists a constant C>0 such that for any Δ(0,1]

    E[supk=0,1,,T/Δ|X(tk)y(tk)|q]CΔq,T>0.

    Proof. By integrating on both sides of Eq (4.2), it is easy to get that

    y(tk+1)=y(tk)+tk+1tkF(y(s))ds+tk+1tkσNn(s)dB(s). (4.10)

    Using Eqs (4.1), (4.4) and (4.10), it follows that

    X(tk+1)y(tk+1)=X(tk)y(tk)+(F(X(tk))F(y(tk)))Δ+σβNθ(1eθtk)ΔB(tk)+tk+1tk(F(y(tk))F(y(s)))dstk+1tkσβNθ(1eθs)dB(s)=X(tk)y(tk)+(γ+μ)Ntk+1tkstk1(NI(s))(NI(tk))dI(u)dstk+1tkσβNθ(1eθs)dB(s)+σ2β2Nθ2tk+1tktks(1eθs)2dI(u)ds+2σ2β2N(NI(tk))θtk+1tkstkeθu(1eθu)duds+βN(cF0θ)tk+1tktkseθududs+2σ2β2N(NI(tk))θtk+1tkstkeθu(1eθu)duds(γ+μ)(eX(tk)ey(tk))Δ+σ2β2N2θtk+1tktkseθu(1eθu)duds+σβNθ(1eθtk)ΔB(tk)+(11+ey(tk)11+eX(tk))(σβNθ(1eθtk))2Δ=X(tk)y(tk)+eX(tk)ey(tk)(1+ey(tk))(1+eX(tk))(σβNθ(1eθtk))2Δ(γ+μ)(eX(tk)ey(tk))Δ+J(1)k+J(2)k+J(3)k+J(4)k+Z(1)k+Z(2)k.

    where

    J(1)k=βN(cF0θ)tk+1tktkseθududs,J(3)k=σβNθ(1eθtk)ΔB(tk)tk+1tkσβNθ(1eθs)dB(s), (4.11)
    J(2)k=σ2β2N2θtk+1tktkseθu(1eθu)duds,J(4)k=2σ2β2N(NI(tk))θtk+1tkstkeθu(1eθu)duds, (4.12)
    Z(1)k=(γ+μ)Ntk+1tkstk1(NI(s))(NI(tk))[(βcθ+(F0βcβθ)eθu)(NI(u))γμ]I(u)duds+σ2β2Nθ2tk+1tktks(1eθs)2[(βcθ+(F0βcβθ)eθu)(NI(u))γμ]I(u)duds, (4.13)
    Z(2)k=(γ+μ)Ntk+1tkstk1(NI(s))(NI(tk))σβθ(1eθu)(NI(u))I(u)dB(u)ds+σ2β2Nθ2tk+1tktks(1eθs)2σβθ(1eθu)(NI(u))I(u)dB(u)ds. (4.14)

    Let uk=X(tk)y(tk), Jk=J(1)k+J(2)k+J(3)k+J(4)k and Zk=Z(1)k+Z(2)k, then

    u2k+1=u2k+(γ+μ)2(eX(tk)ey(tk))2Δ2+(eX(tk)ey(tk))2(1+ey(tk))2(1+eX(tk))2(σβNθ(1eθtk))4Δ22(γ+μ)uk(eX(tk)ey(tk))Δ+2ukeX(tk)ey(tk)(1+ey(tk))(1+eX(tk))(σβNθ(1eθtk))2Δ+J2k+Z2k+2ukJk+2ukZk2(γ+μ)(eX(tk)ey(tk))2(1+ey(tk))(1+eX(tk))(σβNθ(1eθtk))2Δ22(γ+μ)(eX(tk)ey(tk))JkΔ2(γ+μ)(eX(tk)ey(tk))ZkΔ+2JkZk+2(1eθtk)2ΔJkeX(tk)ey(tk)(1+ey(tk))(1+eX(tk))(σβNθ)2+2ZkeX(tk)ey(tk)(1+ey(tk))(1+eX(tk))(σβNθ(1eθtk))2Δ. (4.15)

    Further, it is immediate to get that

    uk(eX(tk)ey(tk))(1+ey(tk))(1+eX(tk))|uk||eX(tk)ey(tk)|(1+ey(tk))(1+eX(tk))u2k,

    By using Lagrange mean value theorem, it follows that

    (eX(tk)ey(tk))2(1+ey(tk))2(1+eX(tk))2(X(tk)y(tk))2e2ξ(1+ey(tk))2(1+eX(tk))2u2ke2(X(tk)y(tk))(1+e(X(tk)y(tk)))2u2k,

    where ξ(X(tk)y(tk),X(tk)y(tk)).Then, Eq (4.15) can be estimated as

    u2k+12u2k+4(γ+μ)2(eX(tk)ey(tk))2Δ2+2u2k(σβNθ)4Δ2+3u2k(σβNθ)2Δ+4J2k+4Z2k+2ukJk+2ukZk[2+(σβNθ)2(3+2(σβNθ)2Δ)Δ]u2k+4(γ+μ)2(eX(tk)ey(tk))2Δ2+4J2k+4Z2k+2ukJk+2ukZkki=0(2+CΔ)ki[4J2i+4Z2i+4(γ+μ)2(eX(ti)ey(ti))2Δ2]+2ki=0(2+CΔ)ki[uiJi+uiZi]Cki=0[J2i+Z2i]+Cki=0[(eX(ti)ey(ti))2Δ2]+2ki=0(2+CΔ)ki[uiJi+uiZi]. (4.16)

    Let M0=0, and Mk=k1i=0(2+CΔ)k1iuiZ(2)i for any k1, since

    E[Z(2)k|Fk]=σβN(γ+μ)θE[tk+1tkstk1(NI(s))(NI(tk))(1eθu)(NI(u))I(u)dB(u)ds|Fk]+σ3β3Nθ3E[tk+1tktks(1eθs)2(1eθu)(NI(u))I(u)dB(u)ds|Fk]=0,

    it is then easy to show that E[Mk+1|Fk]=Mk+ukE[Z(2)k|Fk]=Mk. This implies immediate that Mk is a martingale. Using Burkholder-Davis-Gundy inequality and Hölder inequality, it is easy to get that

    E[supk=0,1,,l|Mk|q]CE[(l1i=0u2i|Z(2)i|2)q2]CE[(T/Δ)q/21li=0|ui|q|Z(2)i|q], (4.17)

    for any q2 and l=0,,T/Δ.Using Eq (4.17) and the basic inequality (ni=1ai)pCni=1api for Eq (4.16), it follows that

    E[supk=0,1,,l|uk+1|2q]E[supk=0,1,,l|Cki=0(J2i+Z2i)+Cki=0(eX(ti)ey(ti))2Δ2+2ki=0(2+CΔ)ki(uiJi+uiZi)|q]CE[(li=0(J2i+Z2i))q+(li=0(ex(ti)ey(ti))2)qΔ2q+supk=0,1,,l|ki=0(2+CΔ)ki(uiJi+uiZi)|q],

    then, using the Hölder inequality, the triangle inequality and the basic inequality, it is easy to get that

    E[supk=0,1,,l|uk+1|2q]CE[supk=0,1,,l(|Mk+1|q+|ki=0(2+CΔ)kiuiJi|q)+(T/Δ)q1(li=0|J2i+Z2i|q+li=0|ex(ti)ey(ti)|2qΔ2q+li=0|uiZ(1)i|q)]CE[(T/Δ)q1li=0(|J(1)i|2q+|J(2)i|2q+|J(3)i|2q+|J(4)i|2q+|Z(1)i|2q+|Z(2)i|2q+|ex(ti)ey(ti)|2qΔ2q+|uiZ(1)i|q+((2+CΔ)li|uiJi|)q)+supk=0,1,,l|Mk+1|q] (4.18)

    for any q2 and l=0,1,,T/Δ.By using Lagrange mean value theorem, it follows that

    E[(ex(ti)ey(ti))2q]E[(ex(ti)+ey(ti))q|ex(ti)ey(ti)|q]E[(ex(ti)+ey(ti))2q|x(ti)y(ti)|q]. (4.19)

    Combining Eq (4.19), Theorem 4.3, Theorem 4.4 and Young inequality, it is easy to get that

    E[Δ2q(T/Δ)q1li=0(ex(ti)ey(ti))2q]Tq1li=0E[Δq+12(ex(ti)+ey(ti))2q|ui|qΔ12]Tq12li=0E[Δ2q+1(ex(ti)+ey(ti))4q+|ui|2qΔ]CTqΔ2q+CTq1Δli=0E|ui|2q. (4.20)

    For any q2, using triangle inequality, basic inequality, Hölder inequality, Theorem 3.1 and Theorem 4.1 for Eq (4.13), it is immediate to get that

    E|Z(1)i|2qCE[(tk+1tkstk1(NI(s))(NI(tk))|(βcθ+(F0ββcθ)eθu)(NI(u))(γ+μ)|I(u)duds)2q+(tk+1tkstk(1eθs)2|(cβθ+(F0βcβθ)eθu)(NI(u))(γ+μ)|duds)2q]CΔ2qE[(tk+1tk1(NI(s))(NI(tk))ds)2q]+CΔ4qCΔ2qE[(tk+1tk12q2q1ds)2q1(tk+1tk((NI(s))(NI(tk)))2qds)]+CΔ4qCΔ4q1tk+1tk(E[(NI(s))4q])12(E(NI(tk))4q)12ds+CΔ4qCΔ4q. (4.21)

    Using Hölder inequality, Burkholder-Davis-Gundy inequality, Theorem 3.1 and Theorem 4.1 for Eq (4.14), it is immediate to get that

    E|Z(2)i|2qCE[|tk+1tkstk1(NI(s))(NI(tk))σβθ(1Eθu)(NI(u))I(u)dB(u)ds|2q+|tk+1tkstk(1Eθs)2σβθ(1eθu)(NI(u))I(u)dB(u)ds|2q]CE[|tk+1tkstk1(NI(s))(NI(tk))dB(u)ds|2q+|tk+1tkstkdB(u)ds|2q]CΔ2q1[tk+1tkE[|stk1(NI(s))(NI(tk))dB(u)|2q]ds+tk+1tkE[|stkdB(u)|2q]ds]CΔ3q2[tk+1tkstkE[((NI(s))(NI(tk)))2q]duds+Δ2]CΔ3q2[tk+1tkstk(E[(NI(s))4q])12(E(NI(tk))4q)12duds+Δ2]CΔ3q. (4.22)

    Thus, the Hölder inequality gives that

    E[|ui|q|Z(1)i|q](E|ui|2q)12(E|Z(1)i|2q)12C(E|ui|2q)12Δ2q. (4.23)
    E[|ui|q|Z(2)i|q](E|ui|2q)12(E|Z(2)i|2q)12C(E|ui|2q)12Δ3q2. (4.24)

    For any q2, using Eqs (4.11) and (4.12) and Theorem 3.1, it follows that

    E|J(1)k|2qE|βN(cF0θ)tk+1tktkseθududs|2qCΔ4q, (4.25)
    E|J(2)k|2qE|σ2β2N2θtk+1tktksEθu(1eθu)duds|2qCΔ4q, (4.26)
    E|J(4)k|2qE|2σ2β2N(NI(tk))θtk+1tkstkeθu(1eθu)duds|2qCΔ4q. (4.27)

    using Eq (4.12), Burkholder-Davis-Gundy inequality and Lagrange mean value theorem, it follows that

    E|J(3)k|2qE|σβNθ(1e0tk)ΔB(tk)tk+1tkσβNθ(1eθs)dB(s)|2qCE|tk+1tk(eθseθtk)dB(s)|2qCE[(tk+1tk|eθseθtk|2ds)q]CΔ3q. (4.28)

    For any q2, the Canchy-Schwarz inequality gives that

    E[|ui|q|J(j)i|q](E|ui|2q)12(E|J(j)i|2q)12C(E|ui|2q)12Δ2q,j=1,2,4, (4.29)
    E[|ui|q|J(3)i|q](E|ui|2q)12(E|J(3)i|2q)12C(E|ui|2q)12Δ3q2. (4.30)

    Thus, substituting Eqs (4.20)–(4.30) into Eq (4.18), it is immediate to get that

    E[supk=0,1,,l|uk+1|2q]CE[(T/Δ)q1li=0(|J(1)i|2q+|J(2)i|2q+|J(3)i|2q+|J(4)i|2q+|Z(1)i|2q+|Z(2)i|2q+|ex(ti)ey(ti)|2qΔ2q+|uiZ(1)i|q+((2+CΔ)li|uiJi|)q)+supk=0,1,,l|Mk+1|q]CTq1Δq1[CΔ3q+Cli=0(E|ui|2q)12Δ2q+Cli=0(E|ui|2q)12Δ3q2]+CTqΔ2q+CTq1Δli=0E|ui|2q+CE[(T/Δ)q/21li=0|ui|q|Z(2)i|q]CΔ2q+CΔli=0E|ui|2q

    for any q2, and l=0,1,,T/Δ, and applying the Gromwall inequality we obtain

    E[supk=0,1,,l|uk+1|2q]CΔ2q

    for any q2, and l=0,1,,T/Δ. This completes now the proof of the assertion for q2. The Hölder inequality yields

    E[supk=0,1,,l|uk+1|2p]E[supk=0,1,,l|uk+1|2q]pqCΔ2p

    for any p(0,2), and l=0,1,,T/Δ. The proof is complete.

    Above, the convergence rate of the EM method of Eq (4.2) is given. Below, we will give the convergence rate of the original Eq (2.5) of Eq (4.2).

    Theorem 4.6. For any p>0, there exists a constant C>0 such that for any Δ(0,1]

    E[supk=0,1,,T/Δ|I(tk)ˉI(tk)|p]CΔp,T>0,

    Where ˉI(tk) and I(tk) denote the value of the analytical solution at time tk and the value of the numerical solution at time tk respectively.

    Proof. The Lamperti-type transformation yields

    E[supk=0,1,,T/Δ|I(tk)ˉI(tk)|p]NE[supk=0,1,,T/Δ|eytk1+eytkeXtk1+eXtk|]NE[supk=0,1,,T/Δ|y(tk)X(tk)|p].

    Thus, by applying Theorem 4.5, we infer that

    E[supk=0,1,,T/Δ|I(tk)ˉI(tk)|p]NE[supk=0,1,,T/Δ|y(tk)X(tk)|p]CΔp

    for any Δ(0,1]. The proof is complete.

    Remark 4.7. For positive preserving numerical methods of nonlinear SDE, Mao et al.[25] have established a truncated EM method for stochastic Lotka-Volterra competition model but without any convergence rate of the algorithm. Theorem 4.6 has stated that for a stochastic SIS model which the transmission coefficient and the noise intensity are the function of AQI, the logarithmic EM method has the first-order pth-moment convergence rate over a finite time for all p>0. This is a major innovation of this paper.

    In this section, we want to illustrate the correctness of our theoretical results obtained in previous section by numerical simulations.

    In this subsection, in order to show theoretical result of stationary distribution, some numerical simulations are presented. By using explicit Milsteins method [26], the numerical scheme for Eq (2.4) is given by:

    {S(tk+1)=S(tk)+μN+γI(tk)β[cθ+(F0cθ)eθtk]S(tk)I(tk)μS(tk)+σβθ[1eθtk]S(tk)I(tk)Δtξk+β2σ2S2(tk)I(tk)2θ2[1eθtk]2(ξ2k1)Δt,I(tk+1)=I(tk)+β[cθ+(F0cθ)eθtk]S(tk)I(tk)γI(tk)μI(tk)+σβθ[1eθtk]S(tk)I(tk)Δtξk+β2σ2S(tk)I2(tk)2θ2[1eθtk]2(ξ2k1)Δt, (5.1)

    where ξk(k=1,2,) are independent Gaussian random variables N(0,1).

    With the parameters in Table 1 and taking T=600,000day,Δt=1day, σ=0.05μgm3(resp.σ=0.10μgm3), simple calculation further yields that Rs0=2.569865>1,σ2=2.5×103μg2m9<2.47×103μg2m9=2μθ2γIβ2N2min{S2,I2}2βmax(I,S)|F0cμ|θ2NIβ2(resp.Rs0=2.569875>1,σ2=1×102μg2m9<2.47×102μg2m9=2μθ2γIβ2N2min{S2,I2}2βmax(I,S)|F0cμ|θ2NIβ2) by Eqs (3.2) and (3.14). Thus, it is immediate to get that Eq (2.4) exists stationary distribution under noise intensity \sigma = 0.05\; \mu g \cdot m^{-3} and \sigma = 0.1\; \mu g \cdot m^{-3} by Theorem 4.1. Actually, as shown in from Figure 2, for Eq (2.4), the probability density functions of S(600000\; day) and I(600000\; day) population satisfy normal distribution under two different values of \sigma = 0.05\; \mu g \cdot m^{-3} and \sigma = 0.10\; \mu g \cdot m^{-3} . This is the same conclusion as given by Theorem 4.1.

    Table 1.  Parameter values of stationary distribution.
    Parameters and the epidemiological meaning Value Unit Source of data
    N : The total number of people 50 person\cdot km^{-3} Assumption
    S_{0} : The total number of people 47 person\cdot km^{-3} Assumption
    I_{0} : The total number of people 3 person\cdot km^{-3} Assumption
    F_{0} : The initial concentration of AQI 3000/7.07 \mu g \cdot m^{-3} Assumption
    \beta : Baseline transmission coefficient 2.4769\times10^{-4} m^{3}\cdot km^{3} \cdot person^{-1}\cdot \mu g ^{-1}\cdot day^{-1} Reference [10]
    \theta : Clearance rate of pollutants 0.39 day^{-1} Reference [12]
    c : Inflow rate of pollutants 30 \mu g \cdot m^{-3}\cdot day^{-1} Reference [12]
    \gamma : Recovery rate for infected 0.3 day^{-1} Reference [12]
    \mu : The per capita death rate 0.0707 day^{-1} Assumption

     | Show Table
    DownLoad: CSV
    Figure 1.  The paths of S(t), I(t) of Eq (2.4) under different noise intensities \sigma = 0.05\mu g \cdot m^{-3} (a) and \sigma = 0.10\mu g \cdot m^{-3} (b).
    Figure 2.  Histogram of the probability density function of S(600, 000~ day) (left column) and I(600, 000~ day) (right column) population for Eq (2.4) with two different values of \sigma: \sigma = 0.05 \mu g \cdot m^{-3} and \sigma = 0.10 \mu g \cdot m^{-3} , the smoothed curves are the probability density functions of S(t) and I(t) , respectively.

    In this subsection, in order to illustrate the efficiency of the logarithmic Euler-Maruyama scheme, some numerical simulations are given.

    By using logarithmic Euler-Maruyama method, Eq (2.5) can be discretized in the following form:

    \begin{equation} \begin{split} \left\{ \begin{array}{l} X(t_{k+1}) = &X(t_{k})+[\beta(\frac{c}{\theta}+(F_{0}-\frac{c}{\theta})e^{-\theta t_{k}})N-(\gamma+\mu)(1+e^{X(t_{k})})\\ &+\frac{N}{2}(\frac{2Ne^{X(t_{k})}}{1+e^{X(t_{k})}}-N)(\frac{\sigma \beta}{\theta}(1-e^{-\theta t_{k}}))^{2}]\Delta t+\frac{\sigma \beta N}{\theta}(1-e^{-\theta t_{k}})\sqrt{\Delta t}\xi_{k}, \\ I(t_{k+1}) = &N-\frac{N}{1+e^{X(t_{k})}} = \frac{N e^{X(t_{k})}}{1+e^{X(t_{k})}}. \end{array} \right. \end{split} \end{equation} (5.2)

    With the parameters in Table 2, Figure 3 plots the sample paths of the numerical solutions of the logarithmic EM scheme, the classic EM scheme and I = 0\; person\cdot km^{-3} under different initial values ( I_{0} = 1\; person\cdot km^{-3} and I_{0} = 10\; person\cdot km^{-3} , respectively). It is clear to see from Figure 3 that the purple line will have negative values, but red line will achieve the effect of maintaining positive.

    Table 2.  Parameter values of approximation of positive solution.
    Parameters and the epidemiological meaning Value Unit Source of data
    N : The total number of people 100 person\cdot km^{-3} Assumption
    F_{0} : The initial concentration of AQI 100 \mu g \cdot m^{-3} Assumption
    \beta : Baseline transmission coefficient 2.4769\times10^{-4} m^{3}\cdot km^{-3}\cdot person^{-1}\cdot \mu g ^{-1}\cdot day^{-1} Reference [10]
    \theta : Clearance rate of pollutants 0.3 day^{-1} Reference [12]
    c : Inflow rate of pollutants 5 \mu g \cdot m^{-3}\cdot day^{-1} Assumption
    \mu : The per capita death rate 0.0707 day^{-1} Assumption
    \gamma : Recovery rate for infected 0.4 day^{-1} Reference [12]

     | Show Table
    DownLoad: CSV
    Figure 3.  When T = 400~ day , \Delta = 1~ day and \sigma = 5~ \mu g \cdot m^{-3} , the paths of I(t) of Eq (2.4) with R_{0}^{s} = 0.69 . Left: I_{0} = 1 ~ person\cdot km^{-3} . Right: I_{0} = 10 ~ person\cdot km^{-3} .

    In this subsubsection, the parameters of simulations are those in Table 2. Since the exact solution of Eq (2.5) cannot be obtained, we regard the classic EM scheme with small step size \Delta = 2^{-19}\; day as the replacement of the exact solution I(t) of Eq (2.5). Figure 4 respectively shows that when p = 2 and p = 3 the \log_{2}\; day-\log_{2}\; person\cdot km^{-3} approximation errors Error(2) and Error(3) between the exact solution I(t) and the numerical solution I(t_{k}) with different step sizes \Delta\in[2^{-9}\; day, 2^{-8}\; day, \cdot\cdot\cdot, 2^{-4}\; day] for 10, 000 simulations. According to Theorem 4.6, we can get that when p is other positive values, it also has the first-order convergence rate. In Figure 4, the blue solid line depicts \log_{2}\; day-\log_{2}\; person\cdot km^{-3} error while the red dashed is a reference line of slope 1\; person\cdot km^{-3}\cdot day^{-1} , and

    \begin{equation} \label{90} \text{Error(p)}: = ( \mathbb{E}[\sup\limits_{k = 1, 2, \cdot\cdot\cdot, 2 /\Delta}|I(t_{k})-\bar{I}(t_{k})|^{p}])^{\frac{1}{p}} \approx(\frac{1}{10000}\sum\limits_{j = 1}^{10000}[\sup\limits_{k = 1, 2, \cdot\cdot\cdot, 2 /\Delta}|I^{(j)}(t_{k})-\bar{I}(t^{(j)}_{k})|^{p}])^{\frac{1}{p}}, \nonumber \end{equation}
    Figure 4.  The approximation error of the exact solution and the numerical solution by the logarithmic EM scheme (5.2) as the function of step size \Delta\in[2^{-9}~ day, 2^{-8}~ day, \cdot\cdot\cdot, 2^{-4}~ day] , with \sigma = 0.005~ \mu g \cdot m^{-3}, ~ T = 2~ day .

    where j stands for the j th sample path. It is clear to see from Figure 4 that the logarithmic Euler-Maruyama method has the first-order convergence rate. This is the same conclusion as given by Theorem 4.6.

    In this paper, we obtain an epidemic model affected by air pollution through disturbing the inflow rate of pollutants. First, we prove existence and uniqueness of positive solution of Eq (2.5) in Theorem 3.1. Then, the sufficient conditions for diseases extinction and existence of stationary distribution is given by Theorem 3.2 and Theorem 3.3. Next, to approximate Eq (2.5) a positivity-preserving numerical method logarithmic Euler-Maruyama method is introduced. Finally, for Eq (2.5), we prove that the logarithmic Euler-Maruyama method has the p th-moment convergence rate over a finite time interval for all p > 0 in Theorem 4.5.

    It is well known that different places have huge divergence in the degree of air pollution. In view of this, considering the heterogeneity of each individual is an important factor in constructing more realistic models, that is, spatially heterogeneous epidemic models affected by air pollutants. The theoretical analysis of these models affected by air pollutants may be more complicated and we leave it for further investigation.

    The authors thank the editor and referees for their careful reading and valuable comments. The research was supported by the Ningxia Natural Science Foundation Project (2020AAC03067) and the Natural Science Foundation of China (12161068).

    The authors have no conflict of interest.



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