Startseite Mathematik Positivity preserving operator splitting nonstandard finite difference methods for SEIR reaction diffusion model
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Positivity preserving operator splitting nonstandard finite difference methods for SEIR reaction diffusion model

  • Nauman Ahmed EMAIL logo , Tahira S.S. , M. Rafiq , M.A. Rehman , Mubasher Ali und M.O. Ahmad
Veröffentlicht/Copyright: 29. April 2019

Abstract

In this work, we will introduce two novel positivity preserving operator splitting nonstandard finite difference (NSFD) schemes for the numerical solution of SEIR reaction diffusion epidemic model. In epidemic model of infection diseases, positivity is an important property of the continuous system because negative value of a subpopulation is meaningless. The proposed operator splitting NSFD schemes are dynamically consistent with the solution of the continuous model. First scheme is conditionally stable while second operator splitting scheme is unconditionally stable. The stability of the diffusive SEIR model is also verified numerically with the help of Routh-Hurwitz stability condition. Bifurcation value of transmission coefficient is also carried out with and without diffusion. The proposed operator splitting NSFD schemes are compared with the well-known operator splitting finite difference (FD) schemes.

1 Introduction

In childhood epidemic diseases, Measles is considered as highly infectious disease, spread due to respiratory infection by a traveling virus. In 19th century, Arthur Ransom observed the unevenly recurring nature of Measles. Since 1896, age structures, contact rates and school seasons are known as critical agents for the transmission of Measles in a population. Hamer [1] in 1906 presented a model for transmission dynamics of Measles. Later, the principle of “mass action” was introduced which became a fundamental statute in present theory of infectious disease modeling [2, 3, 4, 5].

Infectious disease dynamics is an important application of dynamical systems. Fixed points, steady states or the equilibrium points of a continuous dynamical systems are the values of variables that do not change over time. If a system starts at a nearby state and converges to the equilibrium point, then this equilibrium point is attractive. Many dynamical systems in different fields of science and engineering show the chaotic behavior. If a numerical method produces chaos and it is not the feature of continuous system, then this is called contrived chaos. Many existing numerical techniques show the contrived chaos. If a continuous dynamical system demonstrates a property P, then it is necessary for the numerical method to preserve that property P. This is called dynamical consistency, in the literature, Twizzell et al and various authors [6, 7, 8, 9, 10, 11, 12, 13] discussed about the contrived chaos and loss of the dynamical consistency in the finite difference scheme. Similarly, Fernández [14] discussed about the same drawbacks exhibited in different series solution techniques. The aim of this work is to develop and investigate such numerical schemes whose equilibrium points coincide with the equilibrium points of the continuous system and preserve all the essential properties demonstrated by the continuous system.

In this work, we consider SEIR epidemic model with diffusion presented by Al-Shawaikh and Twizell [15]. This model describes the transmission dynamics of measles. In this model, the total population is taken as ‘N’ and it is categorized in four classes i.e. susceptible population, exposed population, infected population and recovered population denoted by S, E, I and R respectively. It is assumed that the total population ‘N’ is constant i.e. birth and death rates in the population are equal. Mathematical model of SEIR epidemic model with diffusion is

St=μNμSβSI+dS2Sx2, (1)
Et=βSIμ+σE+dE2Ex2, (2)
It=σEμ+γI+dI2Ix2, (3)
Rt=γIμR+dR2Rx2. (4)

Since R is not present in first three equations so we can write the system as

St=μNμSβSI+dS2Sx2, (5)
Et=βSIμ+σE+dE2Ex2, (6)
It=σEμ+γI+dI2Ix2, (7)

with the initial conditions

S(x,0)=g1(x),E(x,0)=g2(x),I(x,0)=g3(x),0xL (8)

and homogeneous Neumann boundary conditions are

Sx0,t=SxL,t=0, (9)
Ex0,t=ExL,t=0, (10)
Ix0,t=IxL,t=0. (11)

As discussed above, the purpose of this work is to construct positivity preserving operator splitting nonstandard finite difference schemes for the system (5)-(7). Since SEIR reaction diffusion system (5)-(7) is a population model therefore negative values of susceptible, exposed and infected population is meaningless. Nonstandard finite difference method is a structural preserving numerical method which is developed by Mickens [16]. NSFD method is an important tool to solve ordinary and partial differential equations [9, 10, 11, 17, 18] as it preserves the properties possessed by continuous system.

2 Equilibrium Points

There are two steady states [15] of the system (5)-(7), disease-free and endemic steady state. Disease-free state is

(S0,E0,I0)=(N,0,0) (12)

and the endemic steady state is

(S,E,I)=NR0,μNμ+σ11R0,μβR01, (13)

where

R0=σβN(μ+σ)(μ+γ),when,dS=dE=dI=0

R0 is the reproductive and if R0 < 1 then the system possesses disease free state and if R0 > 1 then system possesses endemic state.

3 Stability of Equilibrium of the System

The system (5)-(7) is linearized about the equilibrium point (S*, E*, I*) to have small perturbations S(x, t), E(x, t) and I(x, t) as describe in [19]

St=a11S1+a12E1+a13I1+dS2Sx2, (14)
Et=a21S1+a22E1+a23I1+dE2Ex2, (15)
It=a31S1+a32E1+a33I1+dI2Ix2. (16)

Suppose that the system (14)-(16) possesses Fourier series solution as mentioned in [19], then

S1(x,t)=kSkeλtcos(kx), (17)
E1(x,t)=kEkeλtcos(kx), (18)
I1(x,t)=kIkeλtcos(kx), (19)

where k represents the wave number for the node n and k = /2, (n = 1, 2, 3, …). Replacing the values of S1(x, t), E1(x, t) and I1(x, t) in the system, it is transformed into

k(a11dSk2λ)Sk+ka12Ek+ka13Ik=0, (20)
ka21Sk+k(a22dEk2λ)Ek+ka23Ik=0, (21)
ka31Sk+ka32Ek+k(a33dIk2λ)Ik=0. (22)

The variational matrix 𝓥 is for the equation (20)-(22) is

V=a11dSk2a12a13a21a22dEk2a23a31a32a33dIk2, (23)

where

a11=μβI,a12=0,a13=βS,a21=βI,a22=μσ,a23=βS,a31=0,a32=σ,a33=μγ.

The characteristics equation for matrix 𝓥 is

λ3+η1λ2+η2λ+η3=0,
η1=a11+a22+a33dSk2dEk2dIk2,
η2=(a12a21a11a22a11a33+a13a31a22a33+a23a32+a11dEk2+a11dIk2+a22dSk2+a33dSk2+a33dEk2dSdEk4dSdIk4dEdIk4),
η3=a11a22a33+a11a23a32+a12a21a33a12a23a31a13a21a32+a13a22a31+a11a22dEk2a12a21dIk2+a11a33dEk2a13a31dEk2+a22a33dSk2a23a32dSk2a11dEdIk4a22dSdIk4a33dSdEk4+dSdEdIk6.

The Routh-Hurwitz stability criterion gives

η1>0,η3>0,

and

η1η2η3>0.

4 Bifurcation Value of Transmission Coefficient β

The values of S*, E* and I* are substituted in a11, a22, ⋅ ⋅ ⋅, to obtain

a11=13688.874223β,a12=0,a13=73.052026,a21=13688.87422β0.02,a22=45.62,a23=73.052026,a31=0,a32=45.6,a33=73.02.

The Routh-Hurwitz criterion for stability gives

η1=13688.874223β+118.790511=f1(β),
η3=45626204.885735β66.387278=f2(β),
η1η2η3=22236475224.061045β2+147566036.674313β+2032.880064=f3(β).

The equation f2(β) = 0 provides the bifurcation value of β, which shifts the stability of the equilibrium point (i.e. from stable to unstable). The solution for f2(β) gives 0.000001455. The point of equilibrium is unstable for any value of β less than 0.000001455. Similarly, the bifurcation value of β for system without diffusion is 0.000001462. The equilibrium point of the system without diffusion is not stable for any value of β less than 0.000001462. It is concluded that bifurcation value of β with diffusion is less than bifurcation value of β without diffusion. In order to find the bifurcation value of β we consider case 1 in Table 1 and dS = 0.05, dE = 0.01, dI = 0.001.

Table 1

Values of parameters.

cases β σ γ μ
1 0.3×10–5 45.6 73 0.02
2 0.5×10–4 50 80 0.02
3 0.8×10–3 58 85 0.02
4 0.9×10–3 65 90 0.02
5 0.3×10–2 77 99 0.02

Al-Showaikh et al. and Jansen et al. [15, 20]

Table 2

Stability of Equilibrium.

cases Point of Equilibrium n η1 η2 η1η2η3 Stability
1 (2.435 × 107, 1.125 × 104, 7022.67) 1 1.1883 × 102 7.0491× 10 2.4758× 103 Stable
2 (1.6010 × 106, 1.9352 × 104, 1.2092× 104) 1 130.8151 2.4215× 103 1.0579× 104 Stable
3 (1.0631 × 105, 1.7199 × 104, 1.1733× 104) 1 152.5968 4.6323× 104 1.6207× 105 Stable
4 (1.0005 × 105, 1.5349 × 104, 1.1083× 104) 1 165.1852 5.8407× 104 2.0116× 105 Stable
5 (3.3015 × 104, 1.2975 × 104, 1.0090× 104) 1 206.4796 2.3093× 105 8.7524× 105 Stable

5 Numerical Methods

In order to handle the complexity and nonlinearity of the reaction-diffusion system (5)-(7), we implement two classical and two proposed nonstandard operator splitting finite difference methods. Operator splitting FD schemes are used by various authors to solve complex problems containing differential equations [19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]. Generally, the SEIR reaction-diffusion equations are split into two systems of equations. The nonlinear reaction steps

12St=μNμSβIS, (24)
12Et=βSI(μ+σ)E, (25)
12It=σE(μ+γ)I, (26)

which are used for the first half step for time and the linear diffusion equation

12St=dS2Sx2, (27)
12Et=dE2Ex2, (28)
12It=dI2Ix2, (29)

which are implemented for the second half step of time. Firstly, we present forward Euler FD and Backward Euler schemes to solve the equations.

S¯im+12=Sim+τμNμSimβSimIim, (30)
E¯im+12=Eim+τβSimIim(μ+σ)Eim, (31)
I¯im+12=Iim+τσEim(μ+γ)Iim, (32)

where Sim,Eim,Iim are the finite difference approximation values of S, E and I at 0 + ih, i = 0, 1, … and time , m = 0, 1, … and S¯im+12,E¯im+12 and I¯im+12 reflect the representative values at the half time step. The procedure at first half time step is same for both forward Euler and Backward Euler FD schemes. At second half-time step, the procedure is different for both schemes. For forward Euler FD scheme, we use

Sim+1=S¯im+12+λ1S¯i1m+122S¯im+12+S¯i+1m+12, (33)
Eim+1=E¯im+12+λ2E¯i1m+122E¯im+12+E¯i+1m+12, (34)
Iim+1=I¯im+12+λ3I¯i1m+122I¯im+12+I¯i+1m+12. (35)

The procedure for the backward Euler FD scheme is

λ1Si1m+1+1+2λ1Sim+1λ1Si1m+1=S¯im+12, (36)
λ2Ei1m+1+1+2λ2Eim+1λ2Ei1m+1=E¯im+12, (37)
λ3Ii1m+1+1+2λ3Iim+1λ3Ii1m+1=I¯im+12. (38)

Nowwe construct nonstandard FD schemes. By applying rules defined by Mickens [16], both nonstandard FD schemes at first half time step are

S¯im+12=Sim+τμN1+τμ+τβIim, (39)
E¯im+12=Eim+τβSimIim1+τ(μ+σ), (40)
I¯im+12=Iim+τσEim1+τ(μ+γ). (41)

Positivity of the solution requires that if

Sim0,Eim0,Iim0,thenS¯im+120,E¯im+120,I¯im+120. (42)

The procedure is different for both NSFD operator splitting schemes at second half-time step. For explicit NSFD operator splitting scheme, we use

Sim+1=12λ1S¯im+12+λ1S¯i1m+12+S¯i+1m+12, (43)
Eim+1=12λ2E¯im+12+λ2E¯i1m+12+E¯i+1m+12, (44)
Iim+1=12λ3I¯im+12+λ3I¯i1m+12+I¯i+1m+12. (45)

The procedure for the implicit NSFD operator splitting scheme at second half time step is

λ1Si1m+1+1+2λ1Sim+1λ1Si1m+1=S¯im+12, (46)
λ2Ei1m+1+1+2λ2Eim+1λ2Ei1m+1=E¯im+12, (47)
λ3Ii1m+1+1+2λ3Iim+1λ3Ii1m+1=I¯im+12, (48)

where

λ1=dSτh2,λ2=dEτh2,λ3=dIτh2.

5.1 Accuracy and Stability of the Numerical Methods

In operator splitting methods, the stability and consistency depends on their split solutions [21, 22]. In all the above splitting methods, the reaction step is solved exactly and time derivative has O(τ) accuracy [21, 22]. Similarly, the diffusion step has O(h2) accuracy and the cumulative accuracy of all numerical schemes is first order in time and second order in space [21, 22]. For the stability, the reaction step is unconditionally stable in all cases as it is solved exactly [21, 22]. In forward Euler method operator splitting FD method and explicit NSFD operator splitting method (32)-(34), the stability region of diffusion step is

λi12,(i=1,2,3). (49)

In the backward Euler method operator splitting FD method and implicit NSFD operator splitting method, the diffusion step is unconditionally stable.

5.2 Positivity of NSFD methods

It can be observed that NSFD methods in reaction step (39)-(41) preserve the positivity property of the solution. For the diffusion step, explicit NSFD method (43)-(45) gives the positive solution if

12λi0,i=1,2,3.

This implies that

λi12,(i=1,2,3),

which is the stability condition (49) of NSFD method (39)-(41). This proves that the first NSFD method preserves the positivity property in its stability region. In order to prove the positivity of unconditionally stable implicit NSFD method (46)-(48), we use M-matrix theory [33].

5.2.1 Theorem [31, 32]

For any h > 0 and τ > 0, the system (46)-(48) is positive, i.e. Sm > 0, Em > 0 and Im > 0 for all m = 0, 1, 2...

Proof

The system (46)-(48) can be written as

ASm+1=Sm, (50)
BEm+1=Em, (51)
CIm+1=Im, (52)

where A, B and C are the square matrices

A=a3a100a2a3a20a2a3a2a2a3a20a2a3a200a1a3, (53)
B=b3b100b2b3b20b2b3b2b2b3b20b2b3b200b1b3, (54)

and

C=c3c100c2c3c20c2c3c2c2c3c20c2c3c200c1c3. (55)

The off-diagonal entries of A are a1 = −2λ1, a2 = −λ1 and a3 = 1 + 2λ1. The off-diagonal entries of B are b1 = −2λ1, b2 = −λ1and diagonal entries are b3 = 1 + 2λ2. The off-diagonal entries of C are c1 = −2λ3, c2 = −λ3and diagonal entries are c3 = 1 + 2λ3. Thus A, B and C are M-matrices. Hence equation (50), (51) and (52) become

Sm+1=A1Sm, (56)
Em+1=B1Em, (57)
Im+1=C1Im. (58)

If we consider that Sm > 0, Em > 0 and Im > 0, then from the property of M-matrix and (56)-(58) we get Sm+1 > 0, Em+1 > 0 and Im+1 > 0 . So by the induction, the theorem is proved.

It follows from Theorem 5.2.1 that implicit NSFD operator splitting scheme preserve positivity property unconditionally.

6 Numerical Experiment

In this section, we perform a numerical experiment for all the methods at both equilibrium points. For the numerical test, the following values of parameters are used for disease-free equilibrium [15, 20]

N=5×107,μ=0.02,σ=45.6,γ=73,β=0.1×105. (59)

For endemic equilibrium points, the following parametric values are used [15, 20]

N=5×107,μ=0.02,σ=45.6,γ=73,β=0.3×105. (60)

In this experiment we take dS = 0.05, dE = 0.01, dI = 0.001. The initial conditions for the system (5)-(7) are

S(x,0)=12500000xif0x<0.5125000001xif0.5x1, (61)
E(x,0)=50000xif0x<0.5500001xif0.5x1, (62)
I(x,0)=30000xif0x<0.5300001xif0.5x1. (63)

Now the graphs of all the schemes at disease free equilibrium point is presented. First of all we show the graphs of Forward Euler operator splitting FD scheme.

The figures 1-3 indicate the graphs of disease free equilibrium using forward Euler operator splitting FD scheme at h = 0.1 and λ1 = 0.09375, λ2 = 0.01875 and λ3 = 0.001875. Figure 2 and figure 3 show that forward Euler operator splitting FD scheme fails to preserve the positivity property.

Figure 1 
Mesh graph of Susceptible Individuals for DFE
Figure 1

Mesh graph of Susceptible Individuals for DFE

Figure 2 
Mesh graph of Exposed Individuals for DFE
Figure 2

Mesh graph of Exposed Individuals for DFE

Figure 3 
Mesh graph of Infected Individuals for DFE
Figure 3

Mesh graph of Infected Individuals for DFE

The figures 4-6 represent the mesh graphs of susceptible, exposed and infected individuals using forward Euler explicit operator splitting FD scheme at h = 0.1 and λ1 = 0.25, λ2 = 0.05 and λ3 = 0.005. It can be seen that all the graphs in figures 4-6 show overflow and divergence. Remember that these are the graphs at endemic equilibrium point. Forward Euler explicit operator splitting FD scheme fails to converge to the endemic equilibrium point. Secondly we present the simulation of backward Euler implicit operator splitting FD scheme.

Figure 4 
Mesh graph of Susceptible Individuals for EE
Figure 4

Mesh graph of Susceptible Individuals for EE

Figure 5 
Mesh graph of Exposed Individuals for EE
Figure 5

Mesh graph of Exposed Individuals for EE

Figure 6 
Mesh graph of Infected Individuals for EE
Figure 6

Mesh graph of Infected Individuals for EE

The figures 7-9 reflect the graphs of disease free equilibrium using backward Euler implicit operator splitting FD scheme at h = 0.1 and λ1 = 0.09375, λ2 = 0.01875 and λ3 = 0.001875. Figure 8 and figure 9 show that backward Euler implicit operator splitting FD scheme also fails to preserve the positivity property.

Figure 7 
Mesh graph of Susceptible Individuals for DFE
Figure 7

Mesh graph of Susceptible Individuals for DFE

Figure 8 
Mesh graph of Exposed Individuals for DFE
Figure 8

Mesh graph of Exposed Individuals for DFE

Figure 9 
Mesh graph of Infected Individuals for DFE
Figure 9

Mesh graph of Infected Individuals for DFE

The figures 10-12 represent the mesh graphs of susceptible, exposed and infected individuals using backward Euler implicit operator splitting FD scheme at h = 0.1 and λ1 = 0.25, λ2 = 0.05 and λ3 = 0.005. It can be seen that all the graphs in figures 10-12 show overflow and divergence. Remember that these are the graphs at endemic equilibrium point. Backward Euler implicit operator splitting FD scheme fails to converge to the endemic equilibrium point. Thirdly, the graphs of susceptible, exposed and infected population at disease free and endemic equilibrium point using nonstandard operator splitting explicit FD method are presented.

Figure 10 
Mesh graph of Susceptible Individuals for EE
Figure 10

Mesh graph of Susceptible Individuals for EE

Figure 11 
Mesh graph of Exposed Individuals for EE
Figure 11

Mesh graph of Exposed Individuals for EE

Figure 12 
Mesh graph of Infected Individuals for EE
Figure 12

Mesh graph of Infected Individuals for EE

The figures 13-15 reflect the graphs of disease free equilibrium using NSFD explicit operator splitting scheme at h = 0.1 and λ1 = 0.09375, λ2 = 0.01875 and λ3 = 0.001875. Graphs clearly show that NSFD operator splitting explicit scheme preserves the positivity property and converges to the disease free equilibrium point (S0, E0, I0).

Figure 13 
Mesh graph of Susceptible Individuals for DFE
Figure 13

Mesh graph of Susceptible Individuals for DFE

Figure 14 
Mesh graph of Exposed Individuals for DFE
Figure 14

Mesh graph of Exposed Individuals for DFE

Figure 15 
Mesh graph of Infected Individuals for DFE
Figure 15

Mesh graph of Infected Individuals for DFE

The figures 16-18 represent the graphs of endemic equilibrium point for susceptible, exposed and infected individuals using NSFD operator splitting explicit scheme at h = 0.1 and λ1 = 0.25, λ2 = 0.05 and λ3 = 0.005. Graphs show that NSFD operator splitting explicit scheme preserves the positivity property and converges to the endemic equilibrium point (S*, E*, I*). At the end, we present the simulations using NSFD operator splitting implicit method.

Figure 16 
Mesh graph of Susceptible Individuals for EE
Figure 16

Mesh graph of Susceptible Individuals for EE

Figure 17 
Mesh graph of Exposed Individuals for EE
Figure 17

Mesh graph of Exposed Individuals for EE

Figure 18 
Mesh graph of Infected Individuals for EE
Figure 18

Mesh graph of Infected Individuals for EE

Figures 19-21 reflect the graphs of disease free equilibrium using NSFD operator splitting implicit scheme at h = 0.1 and λ1 = 0.09375, λ2 = 0.01875 and λ3 = 0.001875. Graphs clearly show that NSFD operator splitting implicit scheme preserves the positivity property and converges to the disease free equilibrium point (S0, E0, I0).

Figure 19 
Mesh graph of Susceptible Individuals for DFE
Figure 19

Mesh graph of Susceptible Individuals for DFE

Figure 20 
Mesh graph of Exposed Individuals for DFE
Figure 20

Mesh graph of Exposed Individuals for DFE

Figure 21 
Mesh graph of Infected Individuals for DFE
Figure 21

Mesh graph of Infected Individuals for DFE

The figures 22-24 represent the graphs of endemic equilibrium point for susceptible, exposed and infected individuals using NSFD operator splitting implicit scheme at h = 0.1 and λ1 = 0.25, λ2 = 0.05 and λ3 = 0.005. Graphs show that NSFD operator splitting implicit method preserves the positivity property and converges to the endemic equilibrium point (S*, E*, I*).

Figure 22 
Mesh graph of Susceptible Individuals for EE
Figure 22

Mesh graph of Susceptible Individuals for EE

Figure 23 
Mesh graph of Exposed Individuals for EE
Figure 23

Mesh graph of Exposed Individuals for EE

Figure 24 
Mesh graph of Infected Individuals for EE
Figure 24

Mesh graph of Infected Individuals for EE

7 Conclusion

In this work, we developed two positivity preserving nonstandard finite difference schemes which did not bring contrived chaos. The proposed NSFD schemes are operator splitting FD schemes. We showed that classical operator splitting FD schemes bring the contrived chaos which lead to the numerical instabilities. We also give the stability analysis of SEIR reaction-diffusion system and find out the bifurcation value of transmission coefficient with the help of Routh-Hurwitz criteria. The proposed NSFD operator splitting schemes should prove to be of value in the solution of nonlinear continuous dynamical systems. Our future plane includes the construction of NSFD operator splitting techniques for hidden attractors in continuous dynamical systems [34, 35, 36, 37].

Acknowledgement

We are thankful to the reviewer for spending his time and giving his valuable suggestions to improve this manuscript.

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Received: 2018-07-15
Accepted: 2019-02-14
Published Online: 2019-04-29

© 2019 Ahmed et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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