Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Balancing mitigation strategies for viral outbreaks

  • Received: 30 September 2024 Revised: 15 November 2024 Accepted: 27 November 2024 Published: 04 December 2024
  • Control and prevention strategies are indispensable tools for managing the spread of infectious diseases. This paper examined biological models for the post-vaccination stage of a viral outbreak that integrate two important mitigation tools: social distancing, aimed at reducing the disease transmission rate, and vaccination, which boosts the immune system. Five different scenarios of epidemic progression were considered: (ⅰ) the "no control" scenario, reflecting the natural evolution of a disease without any safety measures in place, (ⅱ) the "reconstructed" scenario, representing real-world data and interventions, (ⅲ) the "social distancing control" scenario covering a broad set of behavioral changes, (ⅳ) the "vaccine control" scenario demonstrating the impact of vaccination on epidemic spread, and (ⅴ) the "both controls concurrently" scenario incorporating social distancing and vaccine controls simultaneously. By comparing these scenarios, we provided a comprehensive analysis of various intervention strategies, offering valuable insights into disease dynamics. Our innovative approach to modeling the cost of control gave rise to a robust computational algorithm for solving optimal control problems associated with different public health regulations. Numerical results were supported by real data for the Delta variant of the COVID-19 pandemic in the United States.

    Citation: Hamed Karami, Pejman Sanaei, Alexandra Smirnova. Balancing mitigation strategies for viral outbreaks[J]. Mathematical Biosciences and Engineering, 2024, 21(12): 7650-7687. doi: 10.3934/mbe.2024337

    Related Papers:

    [1] N. E. Cho, G. Murugusundaramoorthy, K. R. Karthikeyan, S. Sivasubramanian . Properties of λ-pseudo-starlike functions with respect to a boundary point. AIMS Mathematics, 2022, 7(5): 8701-8714. doi: 10.3934/math.2022486
    [2] Pinhong Long, Huo Tang, Wenshuai Wang . Functional inequalities for several classes of q-starlike and q-convex type analytic and multivalent functions using a generalized Bernardi integral operator. AIMS Mathematics, 2021, 6(2): 1191-1208. doi: 10.3934/math.2021073
    [3] Sadaf Umar, Muhammad Arif, Mohsan Raza, See Keong Lee . On a subclass related to Bazilevič functions. AIMS Mathematics, 2020, 5(3): 2040-2056. doi: 10.3934/math.2020135
    [4] Mohammad Faisal Khan, Jongsuk Ro, Muhammad Ghaffar Khan . Sharp estimate for starlikeness related to a tangent domain. AIMS Mathematics, 2024, 9(8): 20721-20741. doi: 10.3934/math.20241007
    [5] Wenzheng Hu, Jian Deng . Hankel determinants, Fekete-Szegö inequality, and estimates of initial coefficients for certain subclasses of analytic functions. AIMS Mathematics, 2024, 9(3): 6445-6467. doi: 10.3934/math.2024314
    [6] Hava Arıkan, Halit Orhan, Murat Çağlar . Fekete-Szegö inequality for a subclass of analytic functions defined by Komatu integral operator. AIMS Mathematics, 2020, 5(3): 1745-1756. doi: 10.3934/math.2020118
    [7] Pinhong Long, Xing Li, Gangadharan Murugusundaramoorthy, Wenshuai Wang . The Fekete-Szegö type inequalities for certain subclasses analytic functions associated with petal shaped region. AIMS Mathematics, 2021, 6(6): 6087-6106. doi: 10.3934/math.2021357
    [8] K. R. Karthikeyan, G. Murugusundaramoorthy, N. E. Cho . Some inequalities on Bazilevič class of functions involving quasi-subordination. AIMS Mathematics, 2021, 6(7): 7111-7124. doi: 10.3934/math.2021417
    [9] Muhammad Ghaffar Khan, Sheza.M. El-Deeb, Daniel Breaz, Wali Khan Mashwani, Bakhtiar Ahmad . Sufficiency criteria for a class of convex functions connected with tangent function. AIMS Mathematics, 2024, 9(7): 18608-18624. doi: 10.3934/math.2024906
    [10] Ahmad A. Abubaker, Khaled Matarneh, Mohammad Faisal Khan, Suha B. Al-Shaikh, Mustafa Kamal . Study of quantum calculus for a new subclass of q-starlike bi-univalent functions connected with vertical strip domain. AIMS Mathematics, 2024, 9(5): 11789-11804. doi: 10.3934/math.2024577
  • Control and prevention strategies are indispensable tools for managing the spread of infectious diseases. This paper examined biological models for the post-vaccination stage of a viral outbreak that integrate two important mitigation tools: social distancing, aimed at reducing the disease transmission rate, and vaccination, which boosts the immune system. Five different scenarios of epidemic progression were considered: (ⅰ) the "no control" scenario, reflecting the natural evolution of a disease without any safety measures in place, (ⅱ) the "reconstructed" scenario, representing real-world data and interventions, (ⅲ) the "social distancing control" scenario covering a broad set of behavioral changes, (ⅳ) the "vaccine control" scenario demonstrating the impact of vaccination on epidemic spread, and (ⅴ) the "both controls concurrently" scenario incorporating social distancing and vaccine controls simultaneously. By comparing these scenarios, we provided a comprehensive analysis of various intervention strategies, offering valuable insights into disease dynamics. Our innovative approach to modeling the cost of control gave rise to a robust computational algorithm for solving optimal control problems associated with different public health regulations. Numerical results were supported by real data for the Delta variant of the COVID-19 pandemic in the United States.



    Let A denote the class of functions of the form

    f(z)=z+a2z2+a3z3+a4z4+, (1.1)

    which are analytic in the open unit disk D=(z:∣z∣<1) and normalized by f(0)=0 and f(0)=1. Recall that, SA is the univalent function in D=(z:∣z∣<1) and has the star-like and convex functions as its sub-classes which their geometric condition satisfies Re(zf(z)f(z))>0 and Re(1+zf(z)f(z))>0. The two well-known sub-classes have been used to define different subclass of analytical functions in different direction with different perspective and their results are too voluminous in literature.

    Two functions f and g are said to be subordinate to each other, written as fg, if there exists a Schwartz function w(z) such that

    f(z)=g(w(z)),zϵD (1.2)

    where w(0) and w(z)∣<1 for zϵD. Let P denote the class of analytic functions such that p(0)=1 and p(z)1+z1z, zϵD. See [1] for details.

    Goodman [2] proposed the concept of conic domain to generalize convex function which generated the first parabolic region as an image domain of analytic function. The same author studied and introduced the class of uniformly convex functions which satisfy

    UCV=Re{1+(zψ)f(z)f(z)}>0,(z,ψA).

    In recent time, Ma and Minda [3] studied the underneath characterization

    UCV=Re{1+zf(z)f(z)>|zf(z)f(z)|},zϵD. (1.3)

    The characterization studied by [3] gave birth to first parabolic region of the form

    Ω={w;Re(w)>∣w1}, (1.4)

    which was later generalized by Kanas and Wisniowska ([5,6]) to

    Ωk={w;Re(w)>kw1,k0}. (1.5)

    The Ωk represents the right half plane for k=0, hyperbolic region for 0<k<1, parabolic region for k=1 and elliptic region for k>1 [30].

    The generalized conic region (1.5) has been studied by many researchers and their interesting results litter everywhere. Just to mention but a few Malik [7] and Malik et al. [8].

    More so, the conic domain Ω was generalized to domain Ω[A,B], 1B<A1 by Noor and Malik [9] to

    Ω[A,B]={u+iv:[(B21)(U2+V2)2(AB1)u+(A21)]2
    >[2(B+1)(u2v2)+2(A+B+C)u2(A+1)]2+4(AB)2v2}

    and it is called petal type region.

    A function p(z) is said to be in the class UP[A,B], if and only if

    p(z)(A+1)˜p(z)(A1)(B+1)˜p(z)(B1), (1.6)

    where ˜p(z)=1+2π2(log1+z1z)2.

    Taking A=1 and B=1 in (1.8), the usual classes of functions studied by Goodman [1] and Kanas ([5,6]) will be obtained.

    Furthermore, the classes UCV[A,B] and ST[A,B] are uniformly Janoski convex and Starlike functions satisfies

    Re((B1)(zf(z))f(z)(A1)(B+1)(zf(z))f(z)(A+1))>|(B1)(zf(z))f(z)(A1)(B+1)(zf(z))f(z)(A+1)1| (1.7)

    and

    Re((B1)zf(z)f(z)(A1)(B+1)zf(z)f(z)(A+1))>|(B1)zf(z)f(z)(A1)(B+1)zf(z)f(z)(A+1)1|, (1.8)

    or equivalently

    (zf(z))f(z)UP[A,B]

    and

    zf(z)f(z)UP[A,B].

    Setting A=1 and B=1 in (1.7) and (1.8), we obtained the classes of functions investigated by Goodman [2] and Ronning [10].

    The relevant connection to Fekete-Szegö problem is a way of maximizing the non-linear functional |a3λa22| for various subclasses of univalent function theory. To know much of history, we refer the reader to [11,12,13,14] and so on.

    The error function was defined because of the normal curve, and shows up anywhere the normal curve appears. Error function occurs in diffusion which is a part of transport phenomena. It is also useful in biology, mass flow, chemistry, physics and thermomechanics. According to the information at hand, Abramowitz [15] expanded the error function into Maclaurin series of the form

    Erf(z)=2πz0et2dt=2πn=0(1)nz2n+1(2n+1)n! (1.9)

    The properties and inequalities of error function were studied by [16] and [4] while the zeros of complementary error function of the form

    erfc(z)=1erf(z)=2πzet2dt, (1.10)

    was investigated by [17], see for more details in [18,19] and so on. In recent time, [20,21,22] and [23] applied error functions in numerical analysis and their results are flying in the air.

    For f given by [15] and g with the form g(z)=z+b2z2+b3z3+ their Hadamard product (convolution) by fg and at is defined as:

    (fg)(z)=z+n=2anbnzn (1.11)

    Let Erf be a normalized analytical function which is obtained from (1.9) and given by

    Erf=πz2erf(z)=z+n=2(1)n1zn(2n1)(n1)! (1.12)

    Therefore, applying a notation (1.11) to (1.1) and (1.12) we obtain

    ϵ=AErf={F:F(z)=(fErf)(z)=z+n=2(1)n1anzn(2n1)(n1)!,fA}, (1.13)

    where Erf is the class that consists of a single function or Erf. See concept in Kanas et al. [18] and Ramachandran et al. [19].

    Babalola [24] introduced and studied the class of λpseudo starlike function of order β(0β1) which satisfy the condition

    Re(z(f(z))λf(z))>β, (1.14)

    where λ1(zD) and denoted by λ(β). We observed from (1.14) that putting λ=2, the geometric condition gives the product combination of bounded turning point and starlike function which satisfy

    Ref(z)(z(f(z))f(z))>β

    Olatunji [25] extended the class λ(β) to βλ(s,t,Φ) which the geometric condition satisfy

    Re((st)z(f(z))λf(sz)f(tz))>β,

    where s,tC,st,λ1,0β<1,zD and Φ(z) is the modified sigmoid function. The initial coefficient bounds were obtained and the relevant connection to Fekete-Szegö inequalities were generated. The contributions of authors like Altinkaya and Özkan [26] and Murugusundaramoorthy and Janani [27] and Murugusundaramoorthy et al. [28] can not be ignored when we are talking on λ-pseudo starlike functions.

    Inspired by earlier work by [18,19,29]. In this work, the authors employed the approach of [13] to study the coefficient inequalities for pseudo certain subclasses of analytical functions related to petal type region defined by error function. The first few coefficient bounds and the relevant connection to Fekete-Szegö inequalities were obtained for the classes of functions defined. Also note that, the results obtained here has not been in literature and varying of parameters involved will give birth to corollaries.

    For the purpose of the main results, the following lemmas and definitions are very necessary.

    Lemma 1.1. If p(z)=1+p1z+p2z2+ is a function with positive real part in D, then, for any complex μ,

    |p2μp21|2max{1,|2μ1|}

    and the result is sharp for the functions

    p0(z)=1+z1zorp(z)=1+z21z2(zD).

    Lemma 1.2. [29] Let pUP[A,B],1B<A1 and of the form p(z)=1+n=1pnzn. Then, for a complex number μ, we have

    |p2μp21|4π2(AB)max(1,|4π2(B+1)23+4μ(ABπ2)|). (1.15)

    The result is sharp and the equality in (1.15) holds for the functions

    p1(z)=2(A+1)π2(log1+z1z)2+22(B+1)π2(log1+z1z)2+2

    or

    p2(z)=2(A+1)π2(log1+z1z)2+22(B+1)π2(log1+z1z)2+2.

    Proof. For hP and of the form h(z)=1+n=1cnzn, we consider

    h(z)=1+w(z)1w(z)

    where w(z) is such that w(0)=0 and |w(z)|<1. It follows easily that

    w(z)=h(z)1h(z)+1=12z+(c22c214)z2+(c32c2c12+c318)z3+ (1.16)

    Now, if ˜p(z)=1+R1z+R2z2+, then from (1.16), one may have,

    ˜p(w(z))=1+R1w(z)+R2(w(z))2+R3(w(z))3 (1.17)

    where R1=8π2,R2=163π2, and R3=18445π2, see [30]. Substitute R1,R2 and R3 into (1.17) to obtain

    ˜p(w(z))=1+4c1π2z+4π2(c2c216)z2+4π2(c3c1c23+2c3145)z3+ (1.18)

    Since pUP[A,B], so from relations (1.16), (1.17) and (1.18), one may have,

    p(z)=(A+1)˜p(w(z))(A1)(B+1)˜p(w(z))(B1)=2+(A+1)4π2c1z+(A+1)4π2(c2c216)z2+2+(B+1)4π2c1z+(B+1)4π2(c2c216)z2+

    This implies that,

    p(z)=1+2(AB)c1π2z+2(AB)π2(c2c2162(B1)c21π2)z2+8(AB)π2[((B+1)2π4+B+16π2190)c21(B+1π2+112)c1c2+c34]z3+ (1.19)

    If p(z)=1+n=1pnzn, then equating coefficients of z and z2, one may have,

    p1=2π2(AB)c1

    and

    p2=2π2(AB)(c2c2162(B1)c21π2).

    Now for a complex number μ, consider

    p2μp21=2(AB)π2[c2c21(16+2(B+1)π2+2μ(AB)π2)]

    This implies that

    |p2μp21|=2(AB)π2|c2c21(16+2(B+1)π2+2μ(AB)π2)|.

    Using Lemma 1.1, one may have

    |p2μp21|=4(AB)π2max{1,|2v1|},

    where v=16+2(B+1)π2+2μ(AB)π2, which completes the proof of the Lemma.

    Definition 1.3. A function FϵA is said to be in the class UCV[λ,A,B], 1B<A1, if and only if,

    Re((B1)(z(F(z)λ))F(z)(A1)(B+1)(z(F(z)λ))F(z)(A+1))>|(B1)(z(F(z)λ))F(z)(A1)(B+1)(z(F(z)λ))F(z)(A+1)1|, (1.20)

    where λ1ϵR or equivalently (z(F(z)λ))F(z)ϵUP[A,B].

    Definition 1.4. A function FϵA is said to be in the class US[λ,A,B], 1B<A1, if and only if,

    Re((B1)z(F(z)λ)F(z)(A1)(B+1)z(F(z)λ)F(z)(A+1))>|(B1)z(F(z)λ)F(z)(A1)(B+1)z(F(z)λ)F(z)(A+1)1|, (1.21)

    where λ1ϵR or equivalently z(F(z)λ)F(z)ϵUP[A,B].

    Definition 1.5. A function FϵA is said to be in the class UMα[λ,A,B], 1B<A1, if and only if,

    Re((B1)[(1α)z(F(z)λ)F(z)+α(z(F(z)λ))F(z)](A1)(B+1)[(1α)z(F(z)λ)F(z)+α(z(F(z)λ))F(z)](A+1))>|(B1)[(1α)z(F(z)λ)F(z)+α(z(F(z)λ))F(z)](A1)(B+1)[(1α)z(F(z)λ)F(z)+α(z(F(z)λ))F(z)](A+1)1|,

    where α0 and λ1ϵR or equivalently (1α)z(F(z)λ)f(z)+α(z(f(z)λ))f(z)UP[A,B].

    In this section, we shall state and prove the main results, and several corollaries can easily be deduced under various conditions.

    Theorem 2.1. Let FUS[λ,A,B], 1B<A1, and of the form (1.13). Then, for a real number μ, we have

    |a3μa22|40(AB)|13λ|π2max{1,|4(B+1)π2132(AB)(12λ)2π2(2(2λ24λ+1)9μ(13λ)5)|}.

    Proof. If FUS[λ,A,B], 1B<A1, the it follows from relations (1.18), (1.19), and (1.20),

    z(F(z)λ)F(z)=(A+1)˜p(w(z))(A1)(B+1)˜p(w(z))(B1),

    where w(z) is such that w(0)=0 and w(z)∣<1. The right hand side of the above expression get its series form from (1.13) and reduces to

    z(F(z)λ)F(z)=1+2(AB)c1π2z+2(AB)π2(c2c2162(B1)c21π2)z2
    +8(AB)π2[((B+1)2π4+B+16π2190)c21(B+1π2+112)c1c2+c34]z3+. (2.1)

    If F(z)=z+n=2(1)n1anzn(2n1)(n1)!, then one may have

    z(F(z)λ)F(z)=1+12λ3a2z+(2λ24λ+19a2213λ10a3)z2+ (2.2)

    From (2.1) and (2.2), comparison of coefficient of z and z2 gives,

    a2=6(AB)(12λ)π2c1 (2.3)

    and

    2λ24λ+19a2213λ10a3=2(AB)π2(c216c212(B+1)π2c21).

    This implies, by using (2.3), that

    a3=20(AB)(13λ)π2[c216c212(B+1)π2c212(2λ24λ+1)(AB)(12λ)2π2c21].

    Now, for a real number μ consider

    |a3μa22|=
    |20(AB)(13λ)π2(c216c212(B+1)π2c21)+40(AB)2(2λ24λ+1)(12λ)2(13λ)π436μ(AB)2c21(12λ)2π4|
    =20(AB)(13λ)π2|c2c21(16+2(B+1)π22(AB)(2λ24λ+1)(12λ)2π2+9μ(AB)(13λ)5(12λ)2π2)|
    =20(AB)(13λ)π2|c2vc21|

    where v=16+2(B+1)π2(AB)(12λ)2π2(2(2λ24λ+1)9μ(13λ)5).

    Theorem 2.2. Let FUCV[λ,A,B], 1B<A1, and of the form (1.13). Then, for a real number μ, we have

    |a3μa22|40(AB)3|1+3λ|π2max{1,|4(B+1)π2132(1+3λ)(AB)(1+2λ)2π2(λ27μ20)|}

    Proof. If FUCV[λ,A,B], 1B<A1, then it follows from relations (1.18), (1.19), and (1.21),

    (zF(z)λ)F(z)=(A+1)˜p(w(z))(A1)(B+1)˜p(w(z))(B+1),

    where w(z) is such that w(0)=0 and w(z)∣<1. The right hand side of the above expression get its series form from (1.13) and reduces to,

    (zF(z)λ)F(z)=1+2(AB)c1π2z+2(AB)π2(c2c2162(B+1)π2c21)z2+8(AB)π2[(B+1π4+B+16π2+190)c31(B+1π2+112)c1c2+c34]z3+ (2.4)

    If F(z)=z+(1)n1anzn(2n1)(n1)!, then we have,

    (zF(z)λ)F(z)=12(1+2λ)3a2z+(1+3λ)(3a310+2λ9a22)z2+ (2.5)

    From (2.4) and (2.5), comparison of coefficients of z and z2 gives,

    a2=3(AB)c1(1+2λ)π2 (2.6)

    and

    (1+3λ)(3a310+2λ9a22)=2(AB)π2(c2c2162(B+1)c21π2)

    This implies, by using (2.6), that

    a3=103[2(AB)(1+3λ)π2(c2c2162(B+1)c21π2)+2λ(AB)2c21(1+2λ)2π4].

    Now, for a real number μ, consider

    |a3μa22|=|20(AB)3(1+3λ)π2(c216c12(B+1)π2c21)+20(AB)2c213(1+2λ)π49μ(AB)2c21(1+2λ)2π4|
    =20(AB)3(1+3λ)π2|c2c21(16+2(B+1)π2λ(1+3λ)(AB)(1+2λ)2π2+27μ(AB)(1+3λ)20(1+2λ)2π2)|
    =20(AB)3(1+3λ)π2|c2vc21|,

    where

    v=16+2(B+1)π2(1+3λ)(AB)(1+2λ)2π2(λ27μ20).

    Theorem 2.3. FMα[λ,A,B], 1B<A1, α0 and of the form (1.13). Then, for a real number μ, we have

    |a3μa22|40(AB)π2|3(λ+α+2αλ)+α1|max{1,|4(B+1)π2134(AB)[12λα(3+2λ)]2π2(2λ2(1+2α)+2λ(3α2)+1α9μ(3(λ+α+2αλ)+α1)10)|}.

    Proof. Let FMα[λ,A,B], 1B<A1, α0 and of the form (1.13). Then, for a real number μ, we have

    (1α)z(F(z))λF(z)+α(z(F(z))λ)F(z)=(A+1)˜p(w(z))(A1)(B+1)˜p(w(z))(B1), (2.7)

    where w(z) is such that w(z0)=0 and |w(z)|<1. The right hand side of the above expression get its series form from (2.7) and reduces to

    (1α)z(F(z))λF(z)+α(z(F(z))λ)F(z)=1+2(AB)Gπ2z+2(AB)π2(c2c2162(B+1)π2c21)z2+... (2.8)

    If F(z)=z+n=2(1)n1anzn(2n1)(n1)!, then one may have

    (1α)z(F(z))λF(z)+α(z(F(z))λ)F(z)=(1α)[1+12λ3a2z+(2λ24λ+19a2213λ10a3)z2+...]+α[12(1+2λ)3a2z+(1+3λ)(3a310+2λ9a22)z2+...] (2.9)

    from (2.8) and (2.9), comparison of coefficients of z and z2 gives

    a2=6(AB)c1[12λα(3+2λ)]π2 (2.10)

    and

    3(λ+α+2αλ)+α110a32λ2(1+2λ)+α19a22=2(AB)π2(c2c2162(B+1)π2c21)

    This implies, by using (2.10), that

    a3=103(λ+α+2αλ)+α1[2(AB)π2(c2c2162(B+1)π2c21)+4(AB)2[2λ2(1+2λ)+2λ(3α2)+1α][12λα(3+2λ)]2π4c21]

    Now, for a real number μ, consider

    |a3μa22|=|103(λ+α+2αλ)+α1[2(AB)π2(c2c2162(B+1)π2c21)+4(AB)2[2λ2(1+2λ)+2λ(3α2)+1α][12λα(3+2λ)]2π4c21]36(AB)2μG2[12λα(3+2λ)]2π4|
    =|20(AB)π(3(λ+α+2αλ)+α1)|c2c21[16+2(B+1)π22(AB)[2λ2(1+2α)+2λ(3α2)+1α](12λα(3+2λ))2π2+18μ(AB)[3(λ+α+2αλ)+α1]10[12λα(3+2λ)]2π2
    =20(AB)π(3(λ+α+2αλ)+α1)|c2vc21|,

    where

    v=16+2(B+1)π22(AB)[2λ2(1+2α)+2λ(3α2)+1α](12λα(3+2λ))2π2+18μ(AB)[3(λ+α+2αλ)+α1]10[12λα(3+2λ)]2π2.

    The force applied on certain subclasses of analytical functions associated with petal type domain defined by error function has played a vital role in this work. The results obtained are new and varying the parameters involved in the classes of function defined, these will bring new more results that has not been in existence.

    The authors would like to thank the referees for their valuable comments and suggestions.

    The authors declare that they have no conflict of interests.



    [1] W. Wu, Y. Yang, G. Sun, Recent insights into antibiotic resistance in Helicobacter pylori eradication, Gastroenterol. Res. Pract., 2012 (2012), 723183. https://doi.org/10.1155/2012/723183 doi: 10.1155/2012/723183
    [2] M. Wierup, The control of microbial diseases in animals: alternatives to the use of antibiotics, Int. J. Antimicrob. Agents, 14 (2000), 315–319. https://doi.org/10.1016/S0924-8579(00)00143-6 doi: 10.1016/S0924-8579(00)00143-6
    [3] R. C. Waite, Y. Velleman, G. Woods, A. Chitty, M. C. Freeman, Integration of water, sanitation and hygiene for the control of neglected tropical diseases: a review of progress and the way forward, Int. Health, 8 (2015), i22–i27. https://doi.org/10.1093/inthealth/ihw003 doi: 10.1093/inthealth/ihw003
    [4] A. Hinman, Eradication of vaccine-preventable diseases, Annu. Rev. Public Health, 20 (1999), 211–229. https://doi.org/10.1146/annurev.publhealth.20.1.211 doi: 10.1146/annurev.publhealth.20.1.211
    [5] S. Barrett, Eradication versus control: the economics of global infectious disease policies, Bull. World Health Organ., 82 (2004), 683–688.
    [6] P. Aaby, C. S. Benn, Stopping live vaccines after disease eradication may increase mortality, Vaccine, 38 (2020), 10–14. https://doi.org/10.1016/j.vaccine.2019.10.034 doi: 10.1016/j.vaccine.2019.10.034
    [7] N. L. Stepan, Eradication: Ridding the World of Diseases Forever?, Reaktion Books, 2013.
    [8] W. R. Dowdle, The principles of disease elimination and eradication, Bull. World Health Organ., 76 (1998), 22–25.
    [9] B. Beović, The issue of antimicrobial resistance in human medicine, Int. J. Food Microbiol., 112 (2006), 280–287. https://doi.org/10.1016/j.ijfoodmicro.2006.05.001 doi: 10.1016/j.ijfoodmicro.2006.05.001
    [10] D. S. Schneider, J. S. Ayres, Two ways to survive infection: what resistance and tolerance can teach us about treating infectious diseases, Nat. Rev. Immunol., 8 (2008), 889–895. https://doi.org/10.1038/nri2393 doi: 10.1038/nri2393
    [11] B. Spellberg, R. Guidos, D. Gilbert, J. Bradley, H. W. Boucher, W. M. Scheld, et al., The epidemic of antibiotic-resistant infections: a call to action for the medical community from the Infectious Diseases Society of America, Clin. Infect. Dis., 46 (2008), 155–164. https://doi.org/10.1086/524891 doi: 10.1086/524891
    [12] G. J. Armelagos, P. J. Brown, B. Turner, Evolutionary, historical and political economic perspectives on health and disease, Social Sci. Med., 61 (2005), 755–765. https://doi.org/10.1016/j.socscimed.2004.08.066 doi: 10.1016/j.socscimed.2004.08.066
    [13] C. J. Neiderud, How urbanization affects the epidemiology of emerging infectious diseases, Infect. Ecol. Epidemiol., 5 (2015), 27060. https://doi.org/10.3402/iee.v5.27060 doi: 10.3402/iee.v5.27060
    [14] R. Reyes, R. Ahn, K. Thurber, T. F. Burke, Urbanization and infectious diseases: general principles, historical perspectives, and contemporary challenges, Challenges Infect. Dis., (2013), 123–146. https://doi.org/10.1007/978-1-4614-4496-1_4 doi: 10.1007/978-1-4614-4496-1_4
    [15] I. Frost, T. P. Van Boeckel, J. Pires, J. Craig, R. Laxminarayan, Global geographic trends in antimicrobial resistance: the role of international travel, J. Travel Med., 26 (2019), taz036. https://doi.org/10.1093/jtm/taz036 doi: 10.1093/jtm/taz036
    [16] A. C. Steere, J. Coburn, L. Glickstein, The emergence of Lyme disease, J. Clin. Invest., 113 (2004), 1093–1101. https://doi.org/10.1172/JCI200421681 doi: 10.1172/JCI200421681
    [17] G. Dehner, Legionnaires' disease: Building a better world for you, Environ. Hist., 2018. https://doi.org/10.1093/envhis/emy046
    [18] P. M. Schlievert, T. J. Tripp, M. L. Peterson, Reemergence of staphylococcal toxic shock syndrome in Minneapolis-St. Paul, Minnesota, during the 2000–2003 surveillance period, J. Clin. Microbiol., 42 (2004), 2875–2876. https://doi.org/10.1128/JCM.42.6.2875–2876.2004 doi: 10.1128/JCM.42.6.2875–2876.2004
    [19] W. M. Lee, J. E. Polson, D. S. Carney, B. Sahin, M. Gale Jr, Reemergence of hepatitis C virus after 8.5 years in a patient with hypogammaglobulinemia: evidence for an occult viral reservoir, J. Infect. Dis., 192 (2005), 1088–1092. https://doi.org/10.1086/432917 doi: 10.1086/432917
    [20] S. Sridhar, S. K. P. Lau, P. C. Y. Woo, Hepatitis E: A disease of reemerging importance, J. Formosan Med. Assoc., 114 (2015), 681–690. https://doi.org/10.1016/j.jfma.2015.02.003 doi: 10.1016/j.jfma.2015.02.003
    [21] M. Pal, K. P. Gutama, Hantavirus disease: An emerging and re-emerging viral disease of public health concern, Am. J. Infect. Dis., 12 (2024), 19–22. https://doi.org/10.12691/ajidm-12-1-4 doi: 10.12691/ajidm-12-1-4
    [22] M. T. P. Gilbert, A. Rambaut, G. Wlasiuk, T. J. Spira, A. E. Pitchenik, M. Worobey, The emergence of HIV/AIDS in the Americas and beyond, Proc. Natl. Acad. Sci. U.S.A., 104 (2007), 18566–18570. https://doi.org/10.1073/pnas.070532910 doi: 10.1073/pnas.070532910
    [23] T. W. Chun, R. T. Davey Jr, D. Engel, H. C. Lane, A. S. Fauci, Re-emergence of HIV after stopping therapy, Nature, 401 (1999), 874–875. https://doi.org/10.1038/44755 doi: 10.1038/44755
    [24] R. E. Baker, A. S. Mahmud, I. F. Miller, M. Rajeev, F. Rasambainarivo, B. L. Rice, et al., Infectious disease in an era of global change, Nat. Rev. Microbiol., 20 (2022), 193–205. https://doi.org/10.1038/s41579-021-00639-z doi: 10.1038/s41579-021-00639-z
    [25] H. Liao, C. J. Lyon, B. Ying, T. Hu, Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge, Emerging Microbes Infect., 13 (2024), 2356143. https://doi:10.1080/22221751.2024.2356143 doi: 10.1080/22221751.2024.2356143
    [26] S. P. Luby, J. Davis, R. R. Brown, S. M. Gorelick, T. H. F. Wong, Broad approaches to cholera control in Asia: Water, sanitation and handwashing, Vaccine, 38 (2020), A110–A117. https://doi.org/10.1016/j.vaccine.2019.07.084 doi: 10.1016/j.vaccine.2019.07.084
    [27] G. Chowell, H. Nishiura, Transmission dynamics and control of Ebola virus disease (EVD): a review, BMC Med., 12 (2014), 196. https://doi.org/10.1186/s12916-014-0196-0 doi: 10.1186/s12916-014-0196-0
    [28] G. Ledder, Mathematical Modeling for Epidemiology and Ecology, Springer Undergraduate Texts in Mathematics and Technology, Springer International Publishing, 2023.
    [29] H. W. Hethcote, The mathematics of infectious diseases, SIAM Rev., 42 (2000), 599–653. https://doi.org/10.1137/S003614450037190 doi: 10.1137/S003614450037190
    [30] E. G. Nepomuceno, M. L. C. Peixoto, M. J. Lacerda, A. S. L. O. Campanharo, R. H. C. Takahashi, L. A. Aguirre, Application of optimal control of infectious diseases in a model-free scenario, SN Comput. Sci., 2 (2021), 405. https://doi.org/10.1007/s42979-021-00794-3 doi: 10.1007/s42979-021-00794-3
    [31] O. Sharomi, T. Malik, Optimal control in epidemiology, Ann. Oper. Res., 251 (2017), 55–71. https://doi.org/10.1007/s10479-015-1834-4 doi: 10.1007/s10479-015-1834-4
    [32] T. K. Kar, S. Jana, A theoretical study on mathematical modelling of an infectious disease with application of optimal control, Biosystems, 111 (2013), 37–50. https://doi.org/10.1016/j.biosystems.2012.10.003 doi: 10.1016/j.biosystems.2012.10.003
    [33] S. Lenhart, J. T. Workman, Optimal Control Applied to Biological Models, Chapman and Hall/CRC, 2007. https://doi.org/10.1201/9781420011418
    [34] S. S. Musa, S. Qureshi, S. Zhao, A. Yusuf, U. T. Mustapha, D. He, Mathematical modeling of COVID-19 epidemic with effect of awareness programs, Infect. Dis. Modell., 11 (2021), 448–460. https://doi.org/10.1016/j.idm.2021.01.012 doi: 10.1016/j.idm.2021.01.012
    [35] H. Gaff, E. Schaefer, Optimal control applied to vaccination and treatment strategies for various epidemiological models, Math. Biosci. Eng., 6 (2009), 469–492. https://doi.org/10.3934/mbe.2009.6.469 doi: 10.3934/mbe.2009.6.469
    [36] F. Lin, K. Muthuraman, M. Lawley, An optimal control theory approach to non-pharmaceutical interventions, BMC Infect. Dis., 10 (2010), 1–13. https://doi.org/10.1186/1471-2334-10-32 doi: 10.1186/1471-2334-10-32
    [37] D. Huremović, Psychiatry of Pandemics: a Mental Health Response to Infection Outbreak, Springer, (2019), 85–94. https://doi.org/10.1007/978-3-030-15346-5_8
    [38] S. Guerstein, V. Romeo-Aznar, M. Dekel, O. Miron, N. Davidovitch, R. Puzis, et al., The interplay between vaccination and social distancing strategies affects COVID-19 population-level outcomes, PLoS Comput. Biol., 17 (2021), e1009319. https://doi.org/10.1371/journal.pcbi.1009319 doi: 10.1371/journal.pcbi.1009319
    [39] R. Katz, A. Vaught, S. J. Simmens, Local decision making for implementing social distancing in response to outbreaks, Public Health Rep., 134 (2019), 150–154. https://doi.org/10.1177/0033354918819755 doi: 10.1177/0033354918819755
    [40] G. Ledder, S. Manzoni, An optimal control problem for resource utilisation by microorganisms, Int. J. Math. Educ. Sci. Technol., 55 (2024), 547–564. https://doi.org/10.1080/0020739X.2023.2254314 doi: 10.1080/0020739X.2023.2254314
    [41] A. Keimer, L. Pflug, Modeling infectious diseases using integro-differential equations: optimal control strategies for policy decisions and applications in COVID-19, Res Gate, 10 (2020). https://doi.org/10.13140/RG.2.2.10845.44000 doi: 10.13140/RG.2.2.10845.44000
    [42] J. Mondal, S. Khajanchi, P. Samui, Impact of media awareness in mitigating the spread of an infectious disease with application to optimal control, Eur. Phys. J. Plus, 137 (2022), 983. https://doi.org/10.1140/epjp/s13360-022-03156-x doi: 10.1140/epjp/s13360-022-03156-x
    [43] A. Rachah, A mathematical model with isolation for the dynamics of Ebola virus, in Journal of Physics: Conference Series, 1132 (2018), 012058. https://doi.org/10.1088/1742-6596/1132/1/012058
    [44] M. D. Ahmad, M. Usman, A. Khan, M. Imran, Optimal control analysis of Ebola disease with control strategies of quarantine and vaccination, Infect. Dis. Poverty, 5 (2016), 1–12. https://doi.org/10.1186/s40249-016-0161-6 doi: 10.1186/s40249-016-0161-6
    [45] E. Bonyah, K. Badu, S. K. Asiedu-Addo, Optimal control application to an Ebola model, Asian Pac. J. Trop. Biomed., 6 (2016), 283–289. https://doi.org/10.1016/j.apjtb.2016.01.012 doi: 10.1016/j.apjtb.2016.01.012
    [46] I. Area, F. Ndairou, J. J. Nieto, C. J. Silva, D. F. M. Torres, Ebola model and optimal control with vaccination constraints, preprint, arXiv: 1703.01368.
    [47] G. Chowell, B. Cazelles, H. Broutin, C. V. Munayco, The influence of geographic and climate factors on the timing of dengue epidemics in Perú, 1994–2008, BMC Infect. Dis., 11 (2011), 164. https://doi.org/10.1186/1471-2334-11-164 doi: 10.1186/1471-2334-11-164
    [48] J. H. Arias-Castro, H. J. Martinez-Romero, O. Vasilieva, Biological and chemical control of mosquito population by optimal control approach, Games, 11 (2020), 62. https://doi.org/10.3390/g11040062 doi: 10.3390/g11040062
    [49] F. B. Agusto, M. A. Khan, Optimal control strategies for dengue transmission in Pakistan, Math. Biosci., 305 (2018), 102–121. https://doi.org/10.1016/j.mbs.2018.09.007 doi: 10.1016/j.mbs.2018.09.007
    [50] M. A. L. Caetano, T. Yoneyama, Optimal and sub-optimal control in Dengue epidemics, Optim. Control. Appl. Methods, 22 (2001), 63–73. https://doi.org/10.1002/oca.683 doi: 10.1002/oca.683
    [51] K. P. Wijaya, T. Götz, E. Soewono, An optimal control model of mosquito reduction management in a dengue endemic region, Int. J. Biomath., 7 (2014), 1450056. https://doi.org/10.1142/S1793524514500569 doi: 10.1142/S1793524514500569
    [52] L. Lin, Y. Liu, X. Tang, D. He, The disease severity and clinical outcomes of the SARS-CoV-2 variants of concern, Front. Public Health, 9 (2021), 775224. https://doi.org/10.3389/fpubh.2021.775224 doi: 10.3389/fpubh.2021.775224
    [53] H. R. Sayarshad, An optimal control policy for COVID-19 pandemic until a vaccine deployment, MedRxiv, (2020), 2020-09. https://doi.org/10.1101/2020.09.26.20202325
    [54] G. A. Salcedo-Varela, F. Peñuñuri, D. González-Sánchez, S. Díaz-Infante, Synchronizing lockdown and vaccination policies for COVID-19: An optimal control approach based on piecewise constant strategies, Optim. Control. Appl. Methods, 45 (2024), 523–543. https://doi.org/10.1002/oca.3032 doi: 10.1002/oca.3032
    [55] A. Rachah, Optimal control strategies for assessing the impact of medical masks on COVID-19 dynamics: global perspectives and societal well-being, Open J. Social Sci., 12 (2024), 315–330. https://doi.org/10.4236/jss.2024.123022 doi: 10.4236/jss.2024.123022
    [56] H. Bohloli, H. R. Jamshidi, A. Ebraze, F. Rabbani Khah, Combining government, non-pharmaceutical interventions and vaccination in optimal control COVID-19, Int. J. Healthcare Manage., 16 (2023), 61–69. https://doi.org/10.1080/20479700.2022.2071803 doi: 10.1080/20479700.2022.2071803
    [57] L. Mari, R. Casagrandi, E. Bertuzzo, D. Pasetto, S. Miccoli, A. Rinaldo, et al., The epidemicity index of recurrent SARS-CoV-2 infections, Nat. Commun., 12 (2021), 2752. https://doi.org/10.1038/s41467-021-22878-7 doi: 10.1038/s41467-021-22878-7
    [58] J. C. Lemaitre, D. Pasetto, M. Zanon, E. Bertuzzo, L. Mari, S. Miccoli, et al., Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study, PLoS Comput. Biol., 18 (2022), e1010237. https://doi.org/10.1371/journal.pcbi.1010237 doi: 10.1371/journal.pcbi.1010237
    [59] D. Louz, H. E. Bergmans, B. P. Loos, R. C. Hoeben, Emergence of viral diseases: mathematical modeling as a tool for infection control, policy and decision making, Crit. Rev. Microbiol., 36 (2010), 195–211. https://doi.org/10.3109/10408411003604619 doi: 10.3109/10408411003604619
    [60] E. Jung, S. Iwami, Y. Takeuchi, T. Jo, Optimal control strategy for prevention of avian influenza pandemic, J. Theor. Biol., 260 (2009), 220–229. https://doi.org/10.1016/j.jtbi.2009.05.031 doi: 10.1016/j.jtbi.2009.05.031
    [61] S. Lee, G. Chowell, C. Castillo-Chávez, Optimal control for pandemic influenza: the role of limited antiviral treatment and isolation, J. Theor. Biol., 265 (2010), 136–150. https://doi.org/10.1016/j.jtbi.2010.04.003 doi: 10.1016/j.jtbi.2010.04.003
    [62] K. O. Okosun, R. Ouifki, N. Marcus, Optimal control strategies and cost-effectiveness analysis of a malaria model, BioSystems, 111 (2013), 83–101. https://doi.org/10.1016/j.biosystems.2012.09.008 doi: 10.1016/j.biosystems.2012.09.008
    [63] W. Valega-Mackenzie, K. Ríos-Soto, S. Lenhart, Optimal control applied to Zika virus epidemics in Colombia and Puerto Rico, J. Theor. Biol., 575 (2023), 111647. https://doi.org/10.1016/j.jtbi.2023.111647 doi: 10.1016/j.jtbi.2023.111647
    [64] H. R. Joshi, Optimal control of an HIV immunology model, Optim. Control. Appl. Methods, 23 (2002), 199–213. https://doi.org/10.1002/oca.710 doi: 10.1002/oca.710
    [65] S. Lee, G. Chowell, Exploring optimal control strategies in seasonally varying flu-like epidemics, J. Theor. Biol., 412 (2017), 36–47. https://doi.org/10.1016/j.jtbi.2016.09.023 doi: 10.1016/j.jtbi.2016.09.023
    [66] U. Ledzewicz, H. Maurer, H. Schättler, Bang-bang optimal controls for a mathematical model of chemo-and immunotherapy in cancer, Discrete Contin. Dyn. Syst. - Ser. B, 29 (2024), 1481–1500. https://doi.org/10.3934/dcdsb.2023141 doi: 10.3934/dcdsb.2023141
    [67] G. Giordano, F. Blanchini, R. Bruno, P. Colaneri, A. Di Filippo, A. Di Matteo, et al., Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy, Nat. Med., 26 (2020), 855–860. https://doi.org/10.1038/s41591-020-0883-7 doi: 10.1038/s41591-020-0883-7
    [68] J. M. Read, J. R. E. Bridgen, D. A. T. Cummings, A. Ho, C. P. Jewell, Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates, Phil. Trans. R. Soc. B, 376 (2021), 20200265. https://doi.org/10.1098/rstb.2020.0265 doi: 10.1098/rstb.2020.0265
    [69] R. Dandekar, G. Barbastathis, Quantifying the effect of quarantine control in COVID-19 infectious spread using machine learning, MedRxiv, (2020), 2020-04. https://doi.org/10.1101/2020.04.03.20052084
    [70] D. Zou, L. Wang, P. Xu, J. Chen, W. Zhang, Q. Gu, Epidemic model guided machine learning for COVID-19 forecasts in the United States, MedRxiv, (2020), 2020-05. https://doi.org/10.1101/2020.05.24.20111989
    [71] J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395 (2020), 689–697. https://doi.org/10.1016/S0140-6736(20)30260-9 doi: 10.1016/S0140-6736(20)30260-9
    [72] Z. Yang, Z. Zeng, K. Wang, S. S. Wong, W. Liang, M. Zanin, et al., Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions, J. Thoracic Dis., 12 (2020), 165. https://doi.org/10.21037/jtd.2020.02.64 doi: 10.21037/jtd.2020.02.64
    [73] A. Scherer, A. McLean, Mathematical models of vaccination, Br. Med. Bull., 62 (2002), 187–199. https://doi.org/10.1093/bmb/62.1.187 doi: 10.1093/bmb/62.1.187
    [74] B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Tang, Y. Xiao, et al., Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions, J. Clin. Med., 9 (2020), 462. https://doi.org/10.3390/jcm9020462 doi: 10.3390/jcm9020462
    [75] C. Tsay, F. Lejarza, M. A. Stadtherr, M. Baldea, Modeling, state estimation, and optimal control for the US COVID-19 outbreak, Sci. Rep., 10 (2020), 10711. https://doi.org/10.1038/s41598-020-67459-8 doi: 10.1038/s41598-020-67459-8
    [76] R. Luo, A. D. Herrera-Reyes, Y. Kim, S. Rogowski, D. White, A. Smirnova, Estimation of time-dependent transmission rate for COVID-19 SVIRD model using predictor–corrector algorithm, in Mathematical Modeling for Women's Health: Collaborative Workshop for Women in Mathematical Biology, (2024), 213–237. https://doi.org/10.1007/978-3-031-58516-6
    [77] A. Smirnova, M. Baroonian, Reconstruction of incidence reporting rate for SARS-CoV-2 Delta variant of COVID-19 pandemic in the US, Infect. Dis. Modell., 9 (2024), 70–83. https://doi.org/10.1016/j.idm.2023.12.001 doi: 10.1016/j.idm.2023.12.001
    [78] Centers for Disease Control and Prevention, United States COVID-19 Cases and Deaths by State over Time (ARCHIVED). Available from: https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36.
    [79] CDC, Trends in Number of COVID-19 Vaccinations in the US, 2022. Available from: https://covid.cdc.gov/covid-data-tracker/#vaccination-trends.
    [80] E. P. Esteban, L. Almodovar-Abreu, Assessing the impact of vaccination in a COVID-19 compartmental model, Inf. Med. Unlocked, 27 (2021), 100795. https://doi.org/10.1016/j.imu.2021.100795 doi: 10.1016/j.imu.2021.100795
    [81] M. Dashtbali, M. Mirzaie, A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19, Sci. Rep., 11 (2021), 8191. https://doi.org/10.1038/s41598-021-86873-0 doi: 10.1038/s41598-021-86873-0
    [82] A. Smirnova, M. Baroonian, X. Ye, Optimal epidemic control with nonmedical and medical interventions, Mathematics, 12 (2024), 2811. https://www.mdpi.com/2227-7390/12/18/2811
    [83] L. S. Pontryagin, Mathematical Theory of Optimal Processes, Routledge, 2018. https://doi.org/10.1201/9780203749319
    [84] A. Smirnova, X. Ye, On optimal control at the onset of a new viral outbreak, Infect. Dis. Modell., 9 (2024), 995–1006. https://doi.org/10.1016/j.idm.2024.05.006 doi: 10.1016/j.idm.2024.05.006
    [85] N. Tuncer, A. Timsina, M. Nuno, G. Chowell, M. Martcheva, Parameter identifiability and optimal control of a SARS-CoV-2 model early in the pandemic, J. Biol. Dyn., 16 (2022), 412–438. https://doi.org/10.1080/17513758.2022.2078899 doi: 10.1080/17513758.2022.2078899
    [86] M. L. Diagne, F. B. Agusto, H. Rwezaura, J. M. Tchuenche, S. Lenhart, Optimal control of an epidemic model with treatment in the presence of media coverage, Sci. Afr., 24 (2024), e02138. https://doi.org/10.1016/j.sciaf.2024.e02138 doi: 10.1016/j.sciaf.2024.e02138
    [87] E. Howerton, K. Dahlin, C. J. Edholm, L. Fox, M. Reynolds, B. Hollingsworth, et al., The effect of governance structures on optimal control of two-patch epidemic models, J. Math. Biol., 87 (2023), 74. https://doi.org/10.1007/s00285-023-02001-8 doi: 10.1007/s00285-023-02001-8
    [88] U. Ledzewicz, H. Schättler, On optimal singular controls for a general SIR-model with vaccination and treatment, in Conference Publications, 2011 (2011), 981–990. https://doi.org/10.3934/proc.2011.2011.981
    [89] P. E. Parham, J. Waldock, G. K. Christophides, D. Hemming, F. Agusto, K. J. Evans, et al., Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission, Philos. Trans. R. Soc. London, Ser. B, 370 (2015), 20130551. http://dx.doi.org/10.1098/rstb.2013.0551 doi: 10.1098/rstb.2013.0551
    [90] J. A. Patz, P. Daszak, G. M. Tabor, A. A. Aguirre, M. Pearl, J. Epstein, et al., Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence, Environ. Health Perspect., 112 (2004), 1092–1098. https://doi.org/10.1289/ehp.68 doi: 10.1289/ehp.68
  • This article has been cited by:

    1. Sheza M. El-Deeb, Luminita-Ioana Cotîrlă, Coefficient Estimates for Quasi-Subordination Classes Connected with the Combination of q-Convolution and Error Function, 2023, 11, 2227-7390, 4834, 10.3390/math11234834
    2. Arzu Akgül, 2024, Chapter 8, 978-981-97-3237-1, 159, 10.1007/978-981-97-3238-8_8
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(984) PDF downloads(61) Cited by(0)

Figures and Tables

Figures(27)  /  Tables(8)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog