Research article Special Issues

Dynamics and simulations of stochastic COVID-19 epidemic model using Legendre spectral collocation method

  • Received: 02 October 2022 Revised: 22 November 2022 Accepted: 28 November 2022 Published: 02 December 2022
  • MSC : 34K50, 37H30, 65M70

  • The aim of this study is to investigate the dynamics of epidemic transmission of COVID-19 SEIR stochastic model with generalized saturated incidence rate. We assume that the random perturbations depends on white noises, which implies that it is directly proportional to the steady states. The existence and uniqueness of the positive solution along with the stability analysis is provided under disease-free and endemic equilibrium conditions for asymptotically stable transmission dynamics of the model. An epidemiological metric based on the ratio of basic reproduction is used to describe the transmission of an infectious disease using different parameters values involve in the proposed model. A higher order scheme based on Legendre spectral collocation method is used for the numerical simulations. For the better understanding of the proposed scheme, a comparison is made with the deterministic counterpart. In order to confirm the theoretical analysis, we provide a number of numerical examples.

    Citation: Ishtiaq Ali, Sami Ullah Khan. Dynamics and simulations of stochastic COVID-19 epidemic model using Legendre spectral collocation method[J]. AIMS Mathematics, 2023, 8(2): 4220-4236. doi: 10.3934/math.2023210

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  • The aim of this study is to investigate the dynamics of epidemic transmission of COVID-19 SEIR stochastic model with generalized saturated incidence rate. We assume that the random perturbations depends on white noises, which implies that it is directly proportional to the steady states. The existence and uniqueness of the positive solution along with the stability analysis is provided under disease-free and endemic equilibrium conditions for asymptotically stable transmission dynamics of the model. An epidemiological metric based on the ratio of basic reproduction is used to describe the transmission of an infectious disease using different parameters values involve in the proposed model. A higher order scheme based on Legendre spectral collocation method is used for the numerical simulations. For the better understanding of the proposed scheme, a comparison is made with the deterministic counterpart. In order to confirm the theoretical analysis, we provide a number of numerical examples.



    In 1873, Clifford-Klein space forms made their way into mathematics history with a talk given by W. K. Clifford at the British Association for the Advancement of Sciences meeting in Bradford in September 1873 and a paper he published in June of the same year. Clifford's talk was titled on a surface of zero curvature and finite extension, and this is the only information that is available in the meeting proceedings. However, we have further information about it because to F. Klein, who attended Clifford's discussion and provided various versions of it [11]. In the context of elliptic geometry—which Clifford conceived in Klein's way as the geometry of the part of projective space limited by a purely imaginary quartic—Clifford described a closed surface which is locally flat, the today so-called Clifford surface (this name was introduced by Klein [11]). This surface is constructed by using Clifford parallels; Bianchi later provided a description by moving a circle along an elliptic straight line in such a way that it is always orthogonal to the straight line. So Clifford's surface is the analogue of a cylinder; but since it closed - it is often called a torus.

    Let (M,g) be a compact minimal hypersurface of the unit sphere Sn+1 with the immersion ψ:MSn+1. Then we have the immersion ¯ψ=ιψ:MRn+2 in the Euclidean space Rn+2, where ı:Sn+1Rn+2 is the inclusion map. The problem of finding sufficient conditions for the hypersurface M of the unit sphere Sn+1 to be the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n or the unit sphere Sn is one of an by the interesting questions in the differential geometry and specifically in the geometry of the hypersurfaces in a sphere. Many authors including the author of the article with others studied this problem in various ways (cf. [1,4,5,6,7,8]). For notation and background information, the interested reader is referred to [2,3].

    We denote by A=AN and A¯N=I the shape operators of the immersion ψ and ¯ψ corresponding to the unit normal vector field NX(Sn+1) and ¯NX(Rn+2), respectively, where X(Sn+1) and X(Rn+2) are the Lie algebras of smooth vector field on Sn+1 and Rn+2, respectively.

    Note that we can express the immersion ¯ψ as

    ¯ψ=v+f¯N=u+ρN+f¯N,

    where v is the vector field tangential to Sn+1, u is the vector field tangential to M, ρ=<¯ψ,N>, f=<¯ψ,¯N> and <,> is the Euclidean metric.

    In [7], we obtained the Wang-type inequality [12] for compact minimal hypersurfaces in the unit sphere S2n+1 with Sasakian structure and used those inequalities to characterize minimal Clifford hypersurfaces in the unit sphere. Indeed, we obtained two different characterisations (see [7, Theorems 1 and 2]).

    In this paper, our main aim is to obtain the classification by imposing conditions over the tangent and normal components of the immersion. Precisely, we will prove that if ρ2(1β)+φ2(α1α)0 and Z(φ)={xM:φ(x)=0} is a discrete set where α and β are two constants satisfies (n1)αRicβ,β1, then M isometric to the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n (cf. Theorem 3.1). Also, in this paper, we will show that if M has constant scalar curvature S with u is a nonzero vector field and ρ=λf, λR, then M isometric to the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n (cf. Theorem 5.2). Also, we will study the cases:

    (i) when v is a nonzero vector field normal to M or tangent to M,

    (ii) if u is a nonzero conformal vector field,

    (iii) the case if v is a nonzero vector field with ρ is a constant or f is a constant.

    Let (M,g) be a compact minimal hypersurface of the unit sphere Sn+1, nZ+ with the immersion ψ:MSn+1 and let ¯ψ=ιψ:MRn+2. We shall denoted by g the induced metric on the hypersurface M as well as the induced metric on Sn+1. Also, we denote by , ¯N and D the Riemannian connections on M, Sn+1 and Rn+2, respectively. Let NX(Sn+1) and ¯NX(Rn+2) be the unit normal vector fields on Sn+1 and Rn+2, respectively and let AN and A¯N=I be the shape operators of the immersions ψ and ¯ψ, respectively.

    The curvature tensor field of the hypersurface M is given by the Gauss formula:

    R(X,Y)Z=g(Y,Z)Xg(X,Z)Y+g(AY,Z)AXg(AX,Z)AY, (2.1)

    for all X,Y,ZX(M).

    The global tensor field for orthonormal frame of vector field {e1,,en} on Mn is defined as

    Ric(X,Y)=ni=1{g(R(ei,X)Y,ei)}, (2.2)

    for all X,Yχ(M), the above tensor is called the Ricci tensor.

    From (2.1) and (2.2) we can derive the expression for Ricci tensor as follows:

    Ric(X,Y)=(n1)g(X,Y)g(AX,AY), (2.3)

    If we fix a distinct vector eu from {e1,,en} on M, suppose which is u. Then, the Ricci curvature Ric is defined by

    Ric=np=1,pu{g(R(ei,eu)eu,ei)}=(n1)u2Au2, (2.4)

    and the scalar curvature S of M is given by

    S=n(n1)A2, (2.5)

    where A is the length of the shape operator A.

    The Ricci operator Q is the symmetric tensor field defined by

    QX=ni=1R(X,ei)ei, (2.6)

    where {e1,...,en} is a local orthonormal frame and it is well known that the Ricci operator Q satisfies g(QX,Y)=Ric(X,Y) for all X,YX(M). Also, it is known that

    (Q)(ei,ei)=12(S) (2.7)

    where the covariant derivative (Q)(X,Y)=XQYQ(XY) and S is the gradient of the scalar curvature S.

    The Codazzi equation of the hypersurface is

    (A)(X,Y)=(A)(Y,X), (2.8)

    for all X,YX(M), where the covariant derivative (A)(X,Y)=XAYA(XY). A smooth vector field ζ is called conformal vector field if its flow consists of conformal transformations or equivalently,

    {Ł}ζg=2τg,

    where Łζg is the Lie derivative of g with respect to ζ.

    For a smooth function k, we denote by k the gradient of k and we define the Hessian operator Ak:X(M)X(M) by AkX=Xk. Also, we denote by the Laplace operator acting on C(M) the set of all smooth functions on M. It is well known that the sufficient and necessary condition for a connected and complete n -dimensional Riemannian manifold (M,g) to be isometric to the sphere Sn(c), is there is a non-constant smooth function kC(M) satisfying Ak=ckI, which is called Obata's equation.

    Now, we will introduce some lemmas that we will use to prove the results of this paper:

    Lemma 2.1. (Bochner's Formula) [9] Let (M,g) be a compact Riemannian manifold and hC(M). Then,

    M{Ric(h,h)+Ah2(h)2}=0.

    Lemma 2.2. [9] Let (M,g) be a Riemannian manifold and hC(M). Then

    ni=1(Ah)(ei,ei)=Q(h)+(h),

    where {e1,...,en} is a local orthonormal frame and (Ah)(X,Y)=XAh(Y)Ah(XY),X,YX(M).

    Lemma 2.3. Let (M,g) be a compact minimal hypersurface of the unit sphere Sn+1, nZ+ with the immersion ψ:MSn+1 and let ¯ψ=ιψ:MRn+2. Then

    (i) Xu=(1f)X+ρAX for any XX(M), ρ=Au and f=u.

    (ii) ρ=ρA2 and f=n(1f).

    (iii) {ρtrA3+(n21)(1f)}=0 and ρ2A2=Au2.

    (iv) Let φ=1f. Then φ=u, φ=nφ and u2=nφ2, where v is the vector field tangential to Sn+1, u is the vector field tangential to M, ρ=<¯ψ,N>, f=<¯ψ,¯N> and <,> is the Euclidean metric on Rn+2.

    Proof. (i) Note that as ¯ψ=u+ρN+f¯N, for any XX(M):

    X=DXu+X(ρ)N+ρDXN+X(f)¯N+fDX¯N=Xu+g(AX,u)Ng(X,u)¯N+X(ρ)NρAX+X(f)¯N+fX,

    by equating tangential and normal component, we get

    Xu=(1f)X+ρAX,
    ρ=Au,

    and

    f=u.

    (ii) As M is a minimal hypersurface of Sn+1,

    ρ=g(eiAu,ei)=[g((1f)ei+ρAei,Aei)+g(u,eiAei)]=ρA2,

    and

    f=g(eiu,ei)=[g((1f)ei+ρAei,ei)=n(1f).

    (iii)

    divAρ=[g((1f)ei+ρAei,A2ei)+g(u,eiA2ei)]=(1f)A2ρtrA3+12S=(1f)(Sn(n1))ρtrA3+12divSun2S(1f)=(1n2)(1f)Sn(n1)(1f)ρtrA3+12divSu.

    So, if M is a compact, we get

    {ρtrA3+(n21)(1f)S}=0.

    Also, note that

    12ρ2=ρ2A2+Au2.

    So, since M is a compact, we get

    ρ2A2=Au2.

    (iv) Let φ=1f. Then,

    φ=f=u.

    Also,

    φ=f=nφ

    Also, note that

    12φ2=nφ2+u2.

    Since M is a compact, we get

    u2=nφ2.

    Note that as f=n(1f), f is a constant if and only if f=1. In Section 3, we study the case when Z(φ)={xM:φ(x)=0} is a discrete set and ρ2(1β)+φ2(α1)0, where α and β are two constants satisfying (n1)αRic(n1)β, β<1. In Section 4, we study the cases v is a nonzero vector field with f or ρ is a constant, the cases v is a nonzero vector field tangent or normal to the minimal hypersurface M and the case if u is a nonzero conformal vector field. In Section 5, we study the case under the restriction Au=λu, λR.

    Note that on using Lemma 2.3(iii), we get

    0={ρ2A2Au2}.

    Combining Lemma 2.3(iv) with Eq (2.2), we conclude

    0={ρ2(n(n1)S)+Ric(u,u)n(n1)φ2}.

    Let α and β be two constants satisfying (they exist owing to compactness of M) (n1)αRic(n1)β,β<1. Then, using above equation, we get

    0{n(n1)ρ2n(n1)βρ2+(n1)αu2n(n1)φ2}=n(n1){ρ2(1β)+φ2(α1)}.

    Assume that ρ2(1β)+φ2(α1)0, which in view of the above inequality implies

    ρ2=(1α1β)φ2.

    Assume that Z(φ) is a discrete set, then on using (ρφ)2=1α1β on MZ(φ). As ρ and φ are continuous functions and Z(φ) is a discrete set we get (ρφ)2=1α1β on M. So ρ=κφ, κ=1α1β is a constant. Thus, by Lemma 2.3(i), we have

    A2ρ=nκφ,

    and hence

    κφ(A2n)=0,

    thus either κ=0 or φ(A2n)=0. If κ=0, then α=1 and therefore M isometric to the unit sphere Sn and it will imply β=1, which is a contradiction with our assumption β1. So φ(A2n)=0, but as φ0 on MZ(φ) and Z(φ) is a discrete set we get A2=n on all M, by continuity of the function A2, and therefore M isometric to the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n. Thus, we have proved the following theorem:

    Theorem 3.1. Let M be a compact connected minimal hypersurface of Sn+1 and α and β be two constants such that (n1)αRic(n1)β,β<1. If ρ2(1β)+φ2(α1)0 and Z(φ)={xM:φ(x)=0} is discrete, then M isometric to the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n.

    In this section, we study the cases v is a nonzero vector field that is either tangent or normal to the minimal hypersurface M. Also, we will study the cases v is a nonzero vector field with f or ρ is a constant, and the case if u is a nonzero conformal vector field.

    Theorem 4.1. Let M be a complete minimal simply connected hypersurface of Sn+1.

    (i) If v is a nonzero vector field tangent to M, then M isometric to the unit sphere Sn.

    (ii) If v is a nonzero vector field normal to M, then M isometric to the unit sphere Sn.

    Proof. (i) As ρ=0, using Lemma 2.3(i) and (iv), we get

    X(φ)=φX,

    for any XX(M). So AφX=φX for any XinX(M). If φ is a constant then u=0 and v=0, which is a contradiction. So φ is nonconstant function satisfies the Obata's equation and therefore M isometric to the unit sphere Sn.

    (ii) Note that as u=0 and divu=n(1f) (Lemma 3.2(ii)), we get f=1, so by Lemma 3.1(i), we have ρAX=0 for all XX(M), but ρ0 since v is nonzero vector field. So AX=0 for all XX(M) and therefore M isometric to the unit sphere Sn.

    Theorem 4.2. Let M be a complete minimal simply connected hypersurface of Sn+1.

    (i) If v is a nonzero vector field and ρ is a constant, then M isometric to the unit sphere Sn.

    (ii) If v is a nonzero vector field and f is a constant, then M isometric to the unit sphere Sn.

    Proof. (i) If ρ0, then by using Lemma 2.3(ii), we get ρA2=0 and therefore M isometric to the unit sphere Sn. If ρ=0, then by using Lemma 2.3(i) and (iv) we get Xu=φX for any XX(M). So AφX=φX for any XX(M). If φ is a constant then u=0 and v=0, which is a contradiction. So φ is nonconstant function satisfies the Obata's equation and therefore M isometric to the unit sphere Sn.

    (ii) If f is a constant, then u=f=0, so ρ=Au=0, so ρ is a constant and hence ρA2=0, so either ρ=0 or M isometric to the unit sphere Sn. Assume, ρ=0 then u=f=0 and thus v=0, which is a contradiction. So M isometric to the unit sphere Sn.

    Theorem 4.3. Let M be a complete minimal simply connected hypersurface of Sn+1. If u is a nonzero conformal vector field, then M isometric to the unit sphere Sn.

    Proof. Assume u is a conformal vector field with potential map σ then for any X,YX(M):

    2σg(X,Y)=g(Xu,Y)+g(Yu,X)=2(1f)g(X,Y)+2ρg(AX,Y).

    So ρAX=(σ+f1)X for any XX(M).

    If ρ=0, then M isometric to the unit sphere Sn (by Theorem 4.2(i)).

    If ρ0, then A=FI, F=κ+f1ρ, that is M is a totally umplical hypersurface of Sn+1 but M is minimal hypersurface of Sn+1 so M isometric to the unit sphere Sn.

    Theorem 5.1. Let M be a complete minimal simply connected hypersurface of Sn+1. If Au=λu, λR and ρ0, then

    A2=λ2(n1)u2+nφ2n(n1)φ2(n1λ2)u2.

    Proof. We know that

    Aρ2=g(Aρei,Aρei)=λ2g((1f)ei+ρAei,(1f)ei+ρAei)=λ2[nφ2+ρ2A2].

    Also,

    Af2=g(Afei,Afei)=g((1f)ei+ρAei,(1f)ei+ρAei)=nφ2+ρ2A2.

    By using Lemma 2.3(iii), we get

    ρ2A2=λ2A2.

    Using the Bochner's Formula (Lemma 2.1) for the smooth function ρ:

    0={Ric(ρ,ρ)+Aρ2(ρ)2}={λ2(n1λ2)u2+λ2nφ2+λ4u2ρ2A4}={λ2(n1)u2+λ2nφ2ρ2A4}.

    This implies

    ρ2A4=λ2[(n1)u2+nφ2].

    Now, using the Bochner's Formula (Lemma 2.1) for the smooth function f:

    0={Ric(f,f)+Af2(f)2}={(n1λ2)u2+ρ2A2n(n1)φ2}.

    Now as ρ0, ρρ0 and hence ρ2A20 and therefore

    A2=λ2(n1)u2+nφ2n(n1)φ2(n1λ2)u2.

    Theorem 5.2. Let M be a complete minimal simply connected hypersurface of Sn+1 with constant scalar curvature S. If u is a nonzero vector field, Au=λu, λR, then M isometric to the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n.

    Proof. We notice that

    (Aρ)(ei,ei)=eieiAu=λ[ei(f)ei+ei(ρ)Aei]=λ(1+λ2)u.

    Also for any Xχ(M), we have

    g(Qρ,X)=λRic(u,X)=λ(n1λ2)g(u,X).

    Thus

    Qρ=λ(n1λ2)u,

    and

    (ρ)=(ρA2)=[ρA2λA2u].

    Using Lemma 2.2, we get

    λ(1+λ2)u=λ(n1λ2)uρA2+λA2u.

    But A2 is a constant, since the scalar curvature S is a constant (see Eq (2.2)). Thus

    λ(1+λ2)u=λ(1+λ2)uλnu+λA2u,

    and so

    λ(nA2)u=0,

    since λ0 and u is a nonzero vector field, A2=n and therefore M isometric to the Clifford hypersurface S(n)×Sm(mn), where ,mZ+,+m=n.

    Remark 5.1. Note that the structure of [10] can be viewed as an example of the current article's structure in specific cases. In other words, the structure used for the article [10] can be recovered specifically if we select l=1,m=2 and n=3. Because of this, the structure used in this article is the generalized case of [10].

    The authors declare no conflict of interest.



    [1] P. K. Anderson, A. A. Cunningham, N. G. Patel, F. J. Morales, P. R. Epstein, P. Daszak, Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers, Trends Ecol. Evol., 19 (2004), 535–544. https://doi.org/10.1016/j.tree.2004.07.021 doi: 10.1016/j.tree.2004.07.021
    [2] D. Wang, B. Hu, C. Hu, F. Zhu, X. Liu, J. Zhang, et al., Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China, JAMA, 323 (2020), 1061–1069. https://doi.org/10.1001/jama.2020.1585 doi: 10.1001/jama.2020.1585
    [3] Y. G. Sanchez, Z. Sabir, J. L. Guirao, Design of a nonlinear SITR fractal model based on the dynamics of a novel coronavirus (COVID-19), Fractals, 28 (2020), 2040026. https://doi.org/10.1142/S0218348X20400265 doi: 10.1142/S0218348X20400265
    [4] B. C. Baumann, K. M. MacArthur, J. C. Baumann, Emotional support animals on commercial flights: a risk to allergic patients, Lancet Resp. Med., 4 (2016), 544–545. https://doi.org/10.1016/S2213-2600(16)30143-6 doi: 10.1016/S2213-2600(16)30143-6
    [5] World Health Organization, Coronavirus disease 2019 (COVID-19): situation report, 2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
    [6] F. Evirgen, S. Uçar, N. Özdemir, System analysis of HIV infection model with CD4+ T under non-singular kernel derivative, Appl. Math. Nonlinear Sci., 5 (2020), 139–146. https://doi.org/10.2478/amns.2020.1.00013 doi: 10.2478/amns.2020.1.00013
    [7] N. H. Sweilam, S. M. Al-Mekhlafi, D. Baleanu, Optimal control for a fractional tuberculosis infection model including the impact of diabetes and resistant strains, J. Adv. Res., 17 (2019), 125–137. https://doi.org/10.1016/j.jare.2019.01.007 doi: 10.1016/j.jare.2019.01.007
    [8] W. Gao, P. Veeresha, D. G. Prakasha, H. M. Baskonus, Novel dynamic structures of 2019-nCoV with nonlocal operator via powerful computational technique, Biology, 9 (2020), 107. https://doi.org/10.3390/biology9050107 doi: 10.3390/biology9050107
    [9] A. Atangana, S. İğret Araz, Mathematical model of COVID-19 spread in Turkey and South Africa: theory, methods, and applications, Adv. Differ. Equ., 2020 (2020), 659. https://doi.org/10.1186/s13662-020-03095-w doi: 10.1186/s13662-020-03095-w
    [10] J. Tanimoto, Sociophysics approach to epidemics, Springer, 2021.
    [11] A. Din, A. Khan, Y. Sabbar, Long-term bifurcation and stochastic optimal control of a triple delayed Ebola virus model with vaccination and quarantine strategies, Fractal Fract., 6 (2022), 578. https://doi.org/10.3390/fractalfract6100578 doi: 10.3390/fractalfract6100578
    [12] A. Khan, Y. Sabbar, A. Din, Stochastic modeling of the Monkey pox 2022 epidemic with cross infection hypothesis in a highly disturbed environment, Math. Biosci. Eng., 19 (2022), 13560–13581.
    [13] Y. Sabbar, D. Kiouacha, S. P. Rajasekarb, S. El AzamiEl-idrissia, The influence of quadratic Lévy noise on the dynamic of an SIC contagious illness model: new framework, critical comparison and an application to COVID-19 (SARS-CoV-2) case, Chaos, Solitons Fract., 159 (2022), 112110. https://doi.org/10.1016/j.chaos.2022.112110 doi: 10.1016/j.chaos.2022.112110
    [14] D. Lehotzky, T. Insperger, G. Stepan, Extension of the spectral element method for stability analysis of time-periodic delay-differential equations with multiple and distributed delays, Commun. Nonlinear Sci. Numer. Simul., 35 (2016), 177–189. https://doi.org/10.1016/j.cnsns.2015.11.007 doi: 10.1016/j.cnsns.2015.11.007
    [15] S. U. Khan, I. Ali, Application of Legendre spectral-collocation method to delay differential and stochastic delay differential equation, AIP Adv., 8 (2018), 035301. https://doi.org/10.1063/1.5016680 doi: 10.1063/1.5016680
    [16] S. U. Khan, I. Ali, Convergence and error analysis of a spectral collocation method for solving system of nonlinear Fredholm integral equations of second kind, Comput. Appl. Math., 38 (2019), 125. https://doi.org/10.1007/s40314-019-0897-2 doi: 10.1007/s40314-019-0897-2
    [17] S. U. Khan, I. Ali, Applications of Legendre spectral collocation method for solving system of time delay differential equations, Adv. Mech. Eng., 12 (2020). https://doi.org/10.1177/1687814020922113
    [18] I. Ali, S. U. Khan, Analysis of stochastic delayed SIRS model with exponential birth and saturated incidence rate, Chaos, Solitons Fract., 138 (2020), 110008. https://doi.org/10.1016/j.chaos.2020.110008 doi: 10.1016/j.chaos.2020.110008
    [19] I. Ali, S. U. Khan, Threshold of stochastic SIRS epidemic model from infectious to susceptible class with saturated incidence rate Using spectral method, Symmetry, 14 (2022), 1838. https://doi.org/10.3390/sym14091838 doi: 10.3390/sym14091838
    [20] S. U. Khan, M. Ali, I. Ali, A spectral collocation method for stochastic Volterra integro-differential equations and its error analysis, Adv. Differ. Equ., 2019 (2019), 1–14. https://doi.org/10.1186/s13662-019-2096-2 doi: 10.1186/s13662-019-2096-2
    [21] N. Gul, S. U. Khan, I. Ali, F. U. Khan, Transmission dynamic of stochastic hepatitis C model by spectral collocation method, Comput. Methods Biomech. Biomed. Eng., 25 (2022), 578–592. https://doi.org/10.1080/10255842.2021.1970143 doi: 10.1080/10255842.2021.1970143
    [22] I. Ali, S. U. Khan, Asymptotic behavior of three connected stochastic delay neoclassical growth systems using spectral technique, Mathematics, 10 (2022), 3639. https://doi.org/10.3390/math10193639 doi: 10.3390/math10193639
    [23] S. U. Khan, I. Ali, Numerical analysis of stochastic SIR model by Legendre spectral collocation method, Adv. Mech. Eng., 11 (2019). https://doi.org/10.1177/1687814019862918
    [24] A. Ali, S. U. Khan, I. Ali, F. U. Khan, On dynamics of stochastic avian influenza model with asymptomatic carrier using spectral method, Math. Methods Appl. Sci., 45 (2022), 8230–8246. https://doi.org/10.1002/mma.8183 doi: 10.1002/mma.8183
    [25] I. Ali, S. U. Khan, Threshold of stochastic SIRS epidemic model from infectious to susceptible class with saturated incidence rate using spectral method, Symmetry, 9 (2022), 1838. https://doi.org/10.3390/sym14091838 doi: 10.3390/sym14091838
    [26] D. Wang, J. Zhou, Z. Wang, W. Wang, Random gradient-free optimization for multiagent systems with communication noises under a time-varying weight balanced digraph, IEEE Trans. Syst., Man, Cybern.: Syst., 50 (2020), 281–289, https://doi.org/10.1109/TSMC.2017.2757265 doi: 10.1109/TSMC.2017.2757265
    [27] C. Li, J. Lian, Y. Wang, Stability of switched memristive neural networks with impulse and stochastic disturbance, Neurocomputing, 275 (2018), 2565–2573. https://doi.org/10.1016/j.neucom.2017.11.031 doi: 10.1016/j.neucom.2017.11.031
    [28] Y. Cai, J. Jiao, Z. Gui, Y. Liu, W. Wang, Environmental variability in a stochastic epidemic model, Appl. Math. Comput., 329 (2018), 210–226. https://doi.org/10.1016/j.amc.2018.02.009 doi: 10.1016/j.amc.2018.02.009
    [29] Y. Cai, Y. Kang, W. Wang, A stochastic SIRS epidemic model with nonlinear incidence rate, Appl. Math. Comput., 305 (2017), 221–240. https://doi.org/10.1016/j.amc.2017.02.003 doi: 10.1016/j.amc.2017.02.003
    [30] Y. Song, A. Miao, T. Zhang, X. Wang, J. Liu, Extinction and persistence of a stochastic SIRS epidemic model with saturated incidence rate and transfer from infectious to susceptible, Adv. Differ. Equ., 2018 (2018), 293. https://doi.org/10.1186/s13662-018-1759-8 doi: 10.1186/s13662-018-1759-8
    [31] X. Meng, S. Zhao, T. Feng, T. Zhang, Dynamics of a novel nonlinear stochastic SIS epidemic model with double epidemic hypothesis, J. Math. Anal. Appl., 433, (2016), 227–242. https://doi.org/10.1016/j.jmaa.2015.07.056
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