
In this paper, we represented the optimal control and dynamics of a stochastic SEIR epidemic model with nonlinear incidence and treatment rate. By using the Lyapunov function method, the existence and uniqueness of the global positive solution of the model were proved. The dynamic analysis of the stochastic model was studied and we found that the model has an ergodic stationary distribution when Rs0>1. The disease was extinct when Re0<1. The optimal solution of the disease was obtained by using the stochastic control theory. The numerical simulation of our conclusion was carried out. The results showed that the disease decreased with the increase of the control variables.
Citation: Jinji Du, Chuangliang Qin, Yuanxian Hui. Optimal control and analysis of a stochastic SEIR epidemic model with nonlinear incidence and treatment[J]. AIMS Mathematics, 2024, 9(12): 33532-33550. doi: 10.3934/math.20241600
[1] | Muhammad Imran Asjad, Naeem Ullah, Asma Taskeen, Fahd Jarad . Study of power law non-linearity in solitonic solutions using extended hyperbolic function method. AIMS Mathematics, 2022, 7(10): 18603-18615. doi: 10.3934/math.20221023 |
[2] | Mahmoud Soliman, Hamdy M. Ahmed, Niveen Badra, M. Elsaid Ramadan, Islam Samir, Soliman Alkhatib . Influence of the β-fractional derivative on optical soliton solutions of the pure-quartic nonlinear Schrödinger equation with weak nonlocality. AIMS Mathematics, 2025, 10(3): 7489-7508. doi: 10.3934/math.2025344 |
[3] | Chun Huang, Zhao Li . New soliton solutions of the conformal time derivative generalized q-deformed sinh-Gordon equation. AIMS Mathematics, 2024, 9(2): 4194-4204. doi: 10.3934/math.2024206 |
[4] | Tianyong Han, Ying Liang, Wenjie Fan . Dynamics and soliton solutions of the perturbed Schrödinger-Hirota equation with cubic-quintic-septic nonlinearity in dispersive media. AIMS Mathematics, 2025, 10(1): 754-776. doi: 10.3934/math.2025035 |
[5] | Emad H. M. Zahran, Omar Abu Arqub, Ahmet Bekir, Marwan Abukhaled . New diverse types of soliton solutions to the Radhakrishnan-Kundu-Lakshmanan equation. AIMS Mathematics, 2023, 8(4): 8985-9008. doi: 10.3934/math.2023450 |
[6] | Kun Zhang, Tianyong Han, Zhao Li . New single traveling wave solution of the Fokas system via complete discrimination system for polynomial method. AIMS Mathematics, 2023, 8(1): 1925-1936. doi: 10.3934/math.2023099 |
[7] | Da Shi, Zhao Li, Dan Chen . New traveling wave solutions, phase portrait and chaotic patterns for the dispersive concatenation model with spatio-temporal dispersion having multiplicative white noise. AIMS Mathematics, 2024, 9(9): 25732-25751. doi: 10.3934/math.20241257 |
[8] | Mahmoud Soliman, Hamdy M. Ahmed, Niveen Badra, Taher A. Nofal, Islam Samir . Highly dispersive gap solitons for conformable fractional model in optical fibers with dispersive reflectivity solutions using the modified extended direct algebraic method. AIMS Mathematics, 2024, 9(9): 25205-25222. doi: 10.3934/math.20241229 |
[9] | Hamood Ur Rehman, Aziz Ullah Awan, Sayed M. Eldin, Ifrah Iqbal . Study of optical stochastic solitons of Biswas-Arshed equation with multiplicative noise. AIMS Mathematics, 2023, 8(9): 21606-21621. doi: 10.3934/math.20231101 |
[10] | Dumitru Baleanu, Kamyar Hosseini, Soheil Salahshour, Khadijeh Sadri, Mohammad Mirzazadeh, Choonkil Park, Ali Ahmadian . The (2+1)-dimensional hyperbolic nonlinear Schrödinger equation and its optical solitons. AIMS Mathematics, 2021, 6(9): 9568-9581. doi: 10.3934/math.2021556 |
In this paper, we represented the optimal control and dynamics of a stochastic SEIR epidemic model with nonlinear incidence and treatment rate. By using the Lyapunov function method, the existence and uniqueness of the global positive solution of the model were proved. The dynamic analysis of the stochastic model was studied and we found that the model has an ergodic stationary distribution when Rs0>1. The disease was extinct when Re0<1. The optimal solution of the disease was obtained by using the stochastic control theory. The numerical simulation of our conclusion was carried out. The results showed that the disease decreased with the increase of the control variables.
Hydraulic jumps are frequently observed in lab studies, river flows, and coastal environments. The shallow water equations, commonly known as Saint-Venant equations, have important applications in oceanography and hydraulics. These equations are used to represent fluid flow when the depth of the fluid is minor in comparison to the horizontal scale of the flow field fluctuations [1]. They have been used to simulate flow at the atmospheric and ocean scales, and they have been used to forecast tsunamis, storm surges, and flow around constructions, among other things [2,3]. Shallow water is a thin layer of constant density fluid in hydrostatic equilibrium that is bordered on the bottom by a rigid surface and on the top by a free surface [4].
Nonlinear waves have recently acquired importance due to their capacity to highlight several complicated phenomena in nonlinear research with spirited applications [5,6,7,8]. Teshukov in [9] developed the equation system that describes multi-dimensional shear shallow water (SSW) flows. Some novel solitary wave solutions for the ill-posed Boussinesq dynamic wave model under shallow water beneath gravity were constructed [10]. The effects of vertical shear, which are disregarded in the traditional shallow water model, are included in this system to approximate shallow water flows. It is a non-linear hyperbolic partial differential equations (PDE) system with non-conservative products. Shocks, rarefactions, shear, and contact waves are all allowed in this model. The SSW model can capture the oscillatory character of turbulent hydraulic leaps, which corrects the conventional non-linear shallow water equations' inability to represent such occurrences [11]. Analytical and numerical techniques for solving such problems have recently advanced; for details, see [12,13,14,15,16,17] and the references therein.
The SSW model consists of six non-conservative hyperbolic equations, which makes its numerical solution challenging since the concept of a weak solution necessitates selecting a path that is often unknown. The physical regularization mechanism determines the suitable path, and even if one knows the correct path, it is challenging to construct a numerical scheme that converges to the weak solution since the solution is susceptible to numerical viscosity [18].
In the present work, we constructed the generalized Rusanov (G. Rusanov) scheme to solve the SSW model in 1D of space. This technique is divided into steps for predictors and correctors [19,20,21,22,23]. The first one includes a numerical diffusion control parameter. In the second stage, the balance conservation equation is retrieved. Riemann solutions were employed to determine the numerical flow in the majority of the typical schemes. Unlike previous schemes, the interesting feature of the G. Rusanov scheme is that it can evaluate the numerical flow even when the Riemann solution is not present, which is a very fascinating advantage. In actuality, this approach may be applied as a box solver for a variety of non-conservative law models.
The remainder of the article is structured as follows. Section 2 offers the non-conservative shear shallow water model. Section 3 presents the structure of the G. Rusanov scheme to solve the 1D SSW model. Section 4 shows that the G. Rusanov satisfies the C-property. Section 5 provides several test cases to show the validation of the G. Rusanov scheme versus Rusanov, Lax-Friedrichs, and reference solutions. Section 6 summarizes the work and offers some conclusions.
The one-dimensional SSW model without a source term is
∂W∂t+∂F(W)∂x+K(W)∂h∂x=S(W), | (2.1) |
W=(hhv1hv2E11E12E22),F(W)=(hv1R11+hv21+gh22R12+hv1v2(E11+R11)v1E12v1+12(R11v2+R12v1)E22v1+R12v2), |
K(W)=(000ghv112ghv20),S(W)=(0−gh∂B∂x−Cf|v|v1−Cf|v|v2−ghv1∂B∂x+12D11−Cf|v|v21−12ghv2∂B∂x+12D12−Cf|v|v1v212D22−Cf|v|v22), |
while R11 and R12 are defined by the following tensor Rij=hpij, and also, E11, E12 and E22 are defined by the following tensor Eij=12Rij+12hvivj with i≥1,j≤2. We write the previous system 2.1 in nonconservative form with non-dissipative (Cf=0,D11=D12=D22=0), see [24], which can be written as follows
∂W∂t+(∇F(W)+C(W))∂W∂x=S1(W). | (2.2) |
Also, we can write the previous system as the following
∂W∂t+A(W)∂W∂x=S1(W) | (2.3) |
with
A(W)=[010000gh00200000020−3E11v1h+2v31+ghv23E11h−3v2103v100−2E12v1h−E11v2h+2v21v2+ghv222E12h−2v1v2E11h−v21v22v10−E22v1h−2E12v2h+v22v1E22h+v222E12h−2v2v102v2v1] |
and
S1(W)=(0−gh∂B∂x0−ghv1∂B∂x−12ghv2∂B∂x0). |
System (2.3) is a hyperbolic system and has the following eigenvalues
λ1=λ2=v1,λ3=v1−√2E11h−v21,λ4=v1+√2E11h−v21, |
λ5=v1−√gh+3(v21−2E11h),λ6=v1+√gh+3(v21−2E11h). |
In order deduce the G. Rusanov scheme, we rewrite the system (2.1) as follows
∂W∂t+∂F(W)∂x=−K(W)∂h∂x+S1(W)=Q(W). | (3.1) |
Integrating Eq (3.1) over the domain [tn,tn+1]×[xi−12,xi+12] gives the finite-volume scheme
Wn+1i=Wni−ΔtΔx(F(Wni+1/2)−F(Wni−1/2))+ΔtQin | (3.2) |
in the interval [xi−1/2,xi+1/2] at time tn. Wni represents the average value of the solution W as follows
Wni=1Δx∫xi+1/2xi−1/2W(tn,x)dx, |
and F(Wni±1/2) represent the numerical flux at time tn and space x=xi±1/2. It is necessary to solve the Riemann problem at xi+1/2 interfaces due to the organization of the numerical fluxes. Assume that for the first scenario given below, there is a self-similar Riemann problem solution associated with Eq (3.1)
W(x,0)={WL,ifx<0,WR,ifx>0 | (3.3) |
is supplied by
W(t,x)=Rs(xt,WL,WR). | (3.4) |
The difficulties with discretization of the source term in (3.2) could arise from singular values of the Riemann solution at the interfaces. In order to overcome these challenges and reconstruct a Wni+1/2 approximation, we created a finite-volume scheme in [19,20,21,22,23,25,26,27] for numerical solutions of conservation laws including source terms and without source terms. The principal objective here is building the intermediate states Wni±1/2 to be utilized in the corrector stage (3.2). The following is obtained by integrating Eq (3.1) through a control volume [tn,tn+θni+1/2]×[x−,x+], that includes the point (tn,xi+1/2), with the objective to accomplish this:
∫x+x−W(tn+θni+1/2,x)dx=Δx−Wni+Δx+Wni+1−θni+1/2(F(Wni+1)−F(Wni))+θni+1/2(Δx−−Δx+)Qni+12 | (3.5) |
with Wni±1/2 denoting the approximation of Riemann solution Rs across the control volume [x−,x+] at time tn+θni+1/2. While calculating distances Δx− and Δx+ as
Δx−=|x−−xi+1/2|,Δx+=|x+−xi+1/2|, |
Qni+12 closely resembles the average source term Q and is given by the following
Qni+12=1θni+12(Δx−+Δx+)∫tn+θni+12tn∫x+x−Q(W)dtdx. | (3.6) |
Selecting x−=xi, x+=xi+1 causes the Eq (3.5) to be reduced to the intermediate state, which given as follows
Wni+1/2=12(Wni+Wni+1)−θni+1/2Δx(F(Wni+1)−F(Wni))+θni+12Qni+12, | (3.7) |
where the approximate average value of the solution W in the control domain [tn,tn+θni+1/2]×[xi,xi+1] is expressed by Wni+1/2 as the following
Wni+1/2=1Δx∫xi+1xiW(x,tn+θni+1/2)dx | (3.8) |
by selecting θni+1/2 as follows
θni+1/2=αni+1/2ˉθi+1/2,ˉθi+1/2=Δx2Sni+1/2. | (3.9) |
This choice is contingent upon the results of the stability analysis[19]. The following represents the local Rusanov velocity
Sni+1/2=maxk=1,...,K(max(∣λnk,i∣,∣λnk,i+1∣)), | (3.10) |
where αni+1/2 is a positive parameter, and λnk,i represents the kth eigenvalues in (2.3) evaluated at the solution state Wni. Here, in our case, k=6 for the shear shallow water model. One can use the Lax-Wendroff technique again for αni+1/2=ΔtΔxSni+1/2. Choosing the slope αni+1/2=˜αni+1/2, the proposed scheme became a first-order scheme, where
˜αni+1/2=Sni+1/2sni+1/2, | (3.11) |
and
sni+1/2=mink=1,...,K(max(∣λnk,i∣,∣λnk,i+1∣)). | (3.12) |
In this case, the control parameter can be written as follows:
αni+1/2=˜αni+1/2+σni+1/2Φ(rmi+1/2), | (3.13) |
where ˜αni+1/2 is given by (3.11), and Φi+1/2=Φ(ri+1/2) is a function that limits the slope. While for
ri+1/2=Wi+1−q−Wi−qWi+1−Wi,q=sgn[F′(Xn+1,Wni+1/2)] |
and
σni+1/2=ΔtΔxSni+1/2−Sni+1/2sni+1/2. |
One may use any slope limiter function, including minmod, superbee, and Van leer [28] and [29]. At the end, the G. Rusanov scheme for Eq (2.3) can be written as follows
{Wni+12=12(Wni+Wni+1)−αni+122Snj+12[F(Wni+1)−F(Wni)]+αni+122Snj+12Qni+12,Wn+1i=Wni−rn[F(Wni+12)−F(Wni−12)]+ΔtnQin. | (3.14) |
With a definition of the C-property [30], the source term in (2.1) is discretized in the G. Rusanov method in a way that is well-balanced with the discretization of the flux gradients. According to [30,31], if the following formulas hold, a numerical scheme is said to achieve the C-property for the system (3.1).
(hp11)n= constant,hn+B= constantandvn1=vn2=0. | (4.1) |
When we set v1=v2=0 in the stationary flow at rest, we get system (3.1), which can be expressed as follows.
∂∂t(h0012hp1112hp1212hp22)+∂∂x(0hp11+12gh2hp12000)=(0−gh∂B∂x0000)=Q(x,t). | (4.2) |
We can express the predictor stage (3.14) as follows after applying the G. Rusanov scheme to the previous system
Wni+12=(12(hni+hni+1)−αni+124Sni+12(hni+hni+1)[(hni+1+Bi+1)−(hni+Bi)]−αni+122Sni+12((hp11)ni+1−(hp11)ni−αni+122Sni+12((hp12)ni+1−(hp12)ni)14((hp11)ni+1+(hp11)ni)14((hp12)ni+1+(hp12)ni)14((hp22)ni+1+(hp22)ni)). | (4.3) |
Also, we can write the previous equations as follows
Wni+12=(hni+120−αni+122Sni+12((hp12)ni+1−(hp12)ni)12(hp11)ni+1212(hp12)ni+1212(hp22)ni+12), | (4.4) |
and the stage of the corrector updates the solution to take on the desired form.
((h)n+1i(hv1)n+1i(hv2)n+1i(E11)n+1i(E12)n+1i(E22)n+1i)=((h)ni(hv1)ni(hv2)ni(E11)ni(E12)ni(E22)ni)−ΔtnΔx(0g2((hni+12)2−(hni−12)2)(hp12)ni+12−(hp12)ni−12000) | (4.5) |
+(0ΔtnQni0000). |
The solution is stationary when Wn+1i=Wni and this leads us to rewrite the previous system (4.5) as follows
Δtn2Δxg((hni+12)2−(hni−12)2)−ΔtnQni. |
This lead to the following
Qni=−g8Δx(hni+1+2hni+hni−1)(Bi+1−Bi−1). | (4.6) |
Afterward, when the source term in the corrector stage is discretized in the earlier equations, this leads to the G. Rusanov satisfying the C-property.
In order to simulate the SSW system numerically, we provide five test cases without a source term using the G. Rusanov, Rusanov, and Lax-Friedrichs schemes to illustrate the effectiveness and precision of the suggested G. Rusanov scheme. The computational domain for all cases is L=[0,1] divided into 300 gridpoints, and the final time is t=0.5s, except for test case 2, where the final time is t=10s. We evaluate the three schemes with the reference solution on the extremely fine mesh of 30000 cells that was produced by the traditional Rusanov scheme. Also, we provide one test case with a source term using the G. Rusanov scheme in the domain [0,1] divided into 200 gridpoints and final time t=0.5. We select the stability condition [19] in the following sense
Δt=CFLΔxmaxi(|αni+12Sni+12|), | (5.1) |
where a constant CFL=0.5.
This test case was studied in [24] and [32], and the initial condition is given by
(h,v1,v2,p11,p22,p12)={(0.01,0.1,0.2,0.04,0.04,1×10−8)ifx≤L2,(0.02,0.1,−0.2,0.04,0.04,1×10−8)ifx>L2. | (5.2) |
The solution include five waves, which are represented by 1− shock and 6− rarefaction. We compare the numerical results with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells. Figures 1–3 show the numerical results and variation of parameter of control. We note that all waves are captured by this scheme and the numerical solution agrees with the reference solution. Table 1 shows the CPU time of computation for the three schemes by using a Dell i5 laptop, CPU 2.5 GHZ.
Scheme | G. Rusanov | Rusanov | Lax-Friedrichs |
CPU time | 0.068005 | 0.056805 | 0.0496803 |
This test case (the shear waves problem) was studied in [24] and [32], and the initial condition is given by
(h,v1,v2,p11,p22,p12)={(0.01,0,0.2,1×10−4,1×10−4,0)ifx≤L2,(0.01,0,−0.2,1×10−4,1×10−4,0)ifx>L2. | (5.3) |
The solution is represented by two shear waves with only transverse velocity discontinuities, the p12 and p22 elements of a stress tensor. We compare the numerical results by G. Rusanov, Rusanov, and Lax-Friedrichs with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells and a final time t=10s, see Figures 4 and 5. We observe that Figure 5 displays a spurious rise in the central of the domain of computation, which has also been seen in the literature using different methods, see [24,32].
This test case was studied in [24] with the following initial condition
(h,v1,v2,p11,p22,p12)={(0.02,0,0,4×10−2,4×10−2,0)ifx≤L2,(0.01,0,0,4×10−2,4×10−2,0)ifx>L2. | (5.4) |
The solution for h, v1, and p11 consists of two shear rarefaction waves, one moving to the right and the other moving to the left, and a shock wave to the right. See Figures 6 and 7 which show the behavior of the height of the water, velocity, pressure, and variation of the parameter of control.
We take the same example, but with another variant of the modified test case, where p12=1×10−8 is set to a small non-zero value. We notice that the behavior of h, v1, and p11 is the same, but there is a change in the behavior of p12 such that it displays all of the five waves (four rarefaction and one shock) of the shear shallow water model, see Figures 8 and 9.
This test case was studied in [24] with the following initial condition
(h,v1,v2,p11,p22,p12)={(0.02,0,0,1×10−4,1×10−4,0)ifx≤L2,(0.03,−0.221698,0.0166167,1×10−4,1×10−4,0)ifx>L2. | (5.5) |
The solution for this test case consists of a single shock wave. We compare the numerical results with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells and a final time t=0.5s. Figures 10 and 11 show the numerical results, and we note that all waves are captured by this scheme and the numerical solution agrees with the reference solution, but the solution of p11 does not agree with the exact solution [24]. There is an oscillation with Lax-Friedrichs scheme in the water height h and the pressure p11.
This test case (the shear waves problem) was studied in [24] and [32] with the following initial condition
(h,v1,v2,p11,p22,p12)={(0.02,0,0,1×10−4,1×10−4,0)ifx≤L2,(0.01,0,0,1×10−4,1×10−4,0)ifx>L2. | (5.6) |
The solution of this test case has a single shock wave and a single rarefaction wave, separated by a contact discontinuity. We compare the numerical results with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells and a final time t=0.5s. Figures 12 and 13 show the numerical results, and we note that all waves are captured by this scheme and the numerical solution agrees with the reference solution, but the solution of p11 does not agree with the reference solution [24].
In this test case, we take the previous test case 3 and we added the discontinuous fond topography with the following initial condition
(h,v1,v2,p11,p22,p12,B)={(0.02,0,0,4×10−2,4×10−2,1×10−8,0)ifx≤L2,(0.01,0,0,4×10−2,4×10−2,1×10−8,0.01)ifx>L2. | (5.7) |
We simulate this test case with the G. Rusanov scheme with 200 gridpoints on the domain L=[0,1] at the final time t=0.5s, and we note that the behavior of the solution for h consists of one shear rarefaction wave moving to the left, a contact discontinuity after that two shear rarefaction waves, one moving to the right and the other moving to the left (see the left side of Figure 14. The right side of the Figure 14 shows the behavior of the velocity v1, which it consists of a rarefaction wave, a contact discontinuity after that, and two shear rarefactions. The left side of Figure 14 displays the behavior of the velocity v1. It is composed of two shear rarefactions followed by a contact discontinuity and three rarefaction waves moving to the right. The the right side of Figure 14 displays the behavior of the velocity v2. It is composed of two shear rarefactions moving to the left followed by a contact discontinuity after that, and two rarefaction waves moving to the right.
In summary, we have reported five numerical examples to solve the SSW model. Namely, we implemented the G. Rusanov, Rusanov, and Lax-Friedrichs schemes. We also compared the numerical solutions with the reference solution obtained by the classical Rusanov scheme on the very fine mesh of 30000 gridpoints. The three schemes were capable of capturing shock waves and rarefaction. Also, we have given the last numerical test with a source term. Finally, we discovered that the G. Rusanov scheme was more precise than the Rusanov and Lax-Friedrichs schemes.
The current study is concerned with the SSW model, which is a higher order variant of the traditional shallow water model since it adds vertical shear effects. The model features a non-conservative structure, which makes numerical solutions problematic. The 1D SSW model was solved using the G. Rusanov technique. We clarify that this scheme satisfied the C-property. Several numerical examples were given to solve the SSW model using the G. Rusanov, Rusanov, Lax-Friedrichs, and reference solution techniques. The simulations given verified the G. Rusanov technique's high resolution and validated its capabilities and efficacy in dealing with such models. This approach may be expanded in a two-dimensional space.
H. S. Alayachi: Conceptualization, Data curation, Formal analysis, Writing - original draft; Mahmoud A. E. Abdelrahman: Conceptualization, Data curation, Formal analysis, Writing - original draft; K. Mohamed: Conceptualization, Software, Formal analysis, Writing - original draft.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 445-9-753.
The authors declare that they have no competing interests.
[1] |
Z. Hu, W. Ma, S. Ruan, Analysis of SIR epidemic models with nonlinear incidencerate and treatment, Math. Biosci., 238 (2012), 12–20. https://doi.org/10.1016/j.mbs.2012.03.010 doi: 10.1016/j.mbs.2012.03.010
![]() |
[2] |
M. Khan, Q. Badshah, S. Islam, I. Khan, S. Shafie, S. Khan, Global dynamics of SEIRS epidemic model with non-linear generalized incidences and preventive vaccination, Adv. Differ. Equ., 2015 (2015), 88. https://doi.org/10.1186/s13662-015-0429-3 doi: 10.1186/s13662-015-0429-3
![]() |
[3] |
F. Rihan, H. Alsakaji, Dynamics of a stochastic delay differential model for COVID-19 infection with asymptomatic infected and interacting people: case study in the UAE, Results Phys., 28 (2021), 104658. https://doi.org/10.1016/j.rinp.2021.104658 doi: 10.1016/j.rinp.2021.104658
![]() |
[4] |
M. Abdoon, A. Alzahrani, Comparative analysis of influenza modeling using novel fractional operators with real data, Symmetry, 16 (2024), 1126. https://doi.org/10.3390/sym16091126 doi: 10.3390/sym16091126
![]() |
[5] |
C. Qin, J. Du, Y. Hui, Dynamical behavior of a stochastic predator-prey model with Holling-type Ⅲ functional response and infectious predator, AIMS Mathematics, 7 (2022), 7403–7418. https://doi.org/10.3934/math.2022413 doi: 10.3934/math.2022413
![]() |
[6] |
T. Kar, S. Jana, Application of three controls optimally in a vector-borne disease-mathematical study, Commun. Nonlinear Sci., 18 (2013), 2868–2884. https://doi.org/10.1016/j.cnsns.2013.01.022 doi: 10.1016/j.cnsns.2013.01.022
![]() |
[7] | K. Hattaf, M. Rachik, S. Saadi, Y. Tabit, N. Yousfi, Optimal control of tuberculosis with exogenous reinfection, Applied Mathematical Sciences, 3 (2009), 231–240. |
[8] |
F. Gumaa, M. Abdoon, A. Qazza, R. Saadeh, M. Arishi, A. Degoot, Analyzing the impact of control strategies on VisceralLeishmaniasis: a mathematical modeling perspective, Eur. J. Pure Appl. Math., 17 (2024), 1213–1227. https://doi.org/10.29020/nybg.ejpam.v17i2.5121 doi: 10.29020/nybg.ejpam.v17i2.5121
![]() |
[9] |
J. Tchuenche, S. Khamis, F. Agusto, S. Mpeshe, Optimal control and sensitivity analysis of an influenza model with treatment and vaccination, Acta Biotheor., 59 (2011), 1–28. https://doi.org/10.1007/s10441-010-9095-8 doi: 10.1007/s10441-010-9095-8
![]() |
[10] |
K. Okosun, R. Ouifki, N. Marcus, Optimal control analysis of a malaria disease transmission model that includes treatment and vaccination with waning immunity, Biosystems, 106 (2011), 136–145. https://doi.org/10.1016/j.biosystems.2011.07.006 doi: 10.1016/j.biosystems.2011.07.006
![]() |
[11] |
H. Alsakaji, Y. El-Khatib, F. Rihan, A. Hashish, A stochastic epidemic model with time delays and unreported cases based on Markovian switching, Journal of Biosafety and Biosecurity, 6 (2024), 234–243. https://doi.org/10.1016/j.jobb.2024.08.002 doi: 10.1016/j.jobb.2024.08.002
![]() |
[12] |
X. Zhang, X. Liu, Backward bifurcation of an epidemic model with saturated treatment, J. Math. Anal. Appl., 348 (2008), 433–443. https://doi.org/10.1016/j.jmaa.2008.07.042 doi: 10.1016/j.jmaa.2008.07.042
![]() |
[13] |
S. Jana, S. Nandi, T. Kar, Complex dynamics of an SIR epidemic model with saturated incidence rate and treatment, Acta Biotheor., 64 (2016), 65–84. https://doi.org/10.1007/s10441-015-9273-9 doi: 10.1007/s10441-015-9273-9
![]() |
[14] |
M. Ali, F. Guma, A. Qazza, R. Saadeh, N. Alsubaie, M. Althubyani, et al., Stochastic modeling of influenza transmission: insights into disease dynamics and epidemic management, Partial Differential Equations in Applied Mathematics, 11 (2024), 100886. https://doi.org/10.1016/j.padiff.2024.100886 doi: 10.1016/j.padiff.2024.100886
![]() |
[15] |
R. Upadhyay, A. Pal, S. Kumari, P. Roy, Dynamics of an SEIR epidemic model with nonlinear incidence and treatment rates, Nonlinear Dyn., 96 (2019), 2351–2368. https://doi.org/10.1007/s11071-019-04926-6 doi: 10.1007/s11071-019-04926-6
![]() |
[16] |
M. Ali, S. Alzahrani, R. Saadeh, M. Abdoon, A. Qazza, N. Al-kuleab, et al., Modeling COVID-19 spread and non-pharmaceutical interventions in South Africa: a stochastic approach, Scientific African, 24 (2024), e02155. https://doi.org/10.1016/j.sciaf.2024.e02155 doi: 10.1016/j.sciaf.2024.e02155
![]() |
[17] |
F. Rihan, H. Alsakaji, C. Rajivganthi, Stochastic SIRC epidemic model with time-delay for COVID-19, Adv. Differ. Equ., 2020 (2020), 502. https://doi.org/10.1186/s13662-020-02964-8 doi: 10.1186/s13662-020-02964-8
![]() |
[18] |
Q. Yang, D. Jiang, N. Shi, C. Ji, The ergodicity and extinction of stochastically perturbed SIR and SEIR epidemic models with saturated incidence, J. Math. Anal. Appl., 388 (2012), 248–271. https://doi.org/10.1016/j.jmaa.2011.11.072 doi: 10.1016/j.jmaa.2011.11.072
![]() |
[19] | S. Gani, S. Halawar, Optimal control analysis of deterministic and stochastic SIS epidemic model with vaccination, Int. J. Solids Struct., 12 (2017), 251–263. |
[20] |
X. Mao, G. Marion, E. Renshaw, Environmental Brownian noise suppresses explosions in population dynamics, Stoch. Proc. Appl., 97 (2002), 95–110. https://doi.org/10.1016/S0304-4149(01)00126-0 doi: 10.1016/S0304-4149(01)00126-0
![]() |
[21] | X. Mao, Stochastic differential equations and applications, Chichester: Horwood Publishing, 2008. |
[22] |
L. Wang, D. Zhou, Z. Liu, D. Xu, X. Zhang, Media alert in an SIS epidemic model with logistic growth, J. Biol. Dynam., 11 (2017), 120–137. https://doi.org/10.1080/17513758.2016.1181212 doi: 10.1080/17513758.2016.1181212
![]() |
[23] | R. Khasminskii, Stochastic stability of differential equations, 2 Eds., Heidelberg: Spinger-Verlag, 2011. https://doi.org/10.1007/978-3-642-23280-0 |
[24] |
V. Rico-Ramirez, U. Diwekar, Stochastic maximum principle for optimal control under uncertainty, Comput. Chem. Eng., 28 (2004), 2845–2849. https://doi.org/10.1016/j.compchemeng.2004.08.001 doi: 10.1016/j.compchemeng.2004.08.001
![]() |
[25] |
S. Khare, K. Mathur, K. Das, Optimal control of deterministic and stochastic Eco-epidemic food adulteration model, Results in Control and Optimization, 14 (2024), 100336. https://doi.org/10.1016/j.rico.2023.100336 doi: 10.1016/j.rico.2023.100336
![]() |
[26] | B ∅ksendal, Stochastic differential equations: an introduction with applications, Berlin: Springer, 2003. https://doi.org/10.1007/978-3-642-14394-6 |
[27] |
D. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Rev., 43 (2001), 525–546. https://doi.org/10.1137/S0036144500378302 doi: 10.1137/S0036144500378302
![]() |
1. | Georges Chamoun, Finite Volume Analysis of the Two Competing-species Chemotaxis Models with General Diffusive Functions, 2025, 22, 2224-2902, 232, 10.37394/23208.2025.22.24 |
Scheme | G. Rusanov | Rusanov | Lax-Friedrichs |
CPU time | 0.068005 | 0.056805 | 0.0496803 |