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Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2021 Jan 20;2021(1):57. doi: 10.1186/s13662-021-03213-2

Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe

Abdon Atangana 1,2,, Seda İğret Araz 3
PMCID: PMC7816167  PMID: 33495699

Abstract

Using the existing collected data from European and African countries, we present a statistical analysis of forecast of the future number of daily deaths and infections up to 10 September 2020. We presented numerous statistical analyses of collected data from both continents using numerous existing statistical theories. Our predictions show the possibility of the second wave of spread in Europe in the worse scenario and an exponential growth in the number of infections in Africa. The projection of statistical analysis leads us to introducing an extended version of the well-blancmange function to further capture the spread with fractal properties. A mathematical model depicting the spread with nine sub-classes is considered, first converted to a stochastic system, where the existence and uniqueness are presented. Then the model is extended to the concept of nonlocal operators; due to nonlinearity, a modified numerical scheme is suggested and used to present numerical simulations. The suggested mathematical model is able to predict two to three waves of the spread in the near future.

Keywords: Statistical analysis, Extended blancmange function, Stochastic model, COVID-19 spread with waves, Modified numerical scheme

Introduction

Interdisciplinary research is the way forward for mankind to be in control of its environment. Of course they will not be able to have total control since the nature within which they live is full of uncertainties, many complex phenomena that have not been yet understood with the current collections of knowledge and technology. For example, we cannot explicitly and confidently explain what is happening at the Bermuda Triangle, although many studies have been done around this place, some believe it is a devil’s triangle. There are many other natural occurrences that could not be explained so far with our knowledge. But it has been proven that putting together several concepts from different academic fields could provide better results. COVID-19 is an invisible enemy that left humans with no choice than to put all their efforts from all backgrounds with the aim to protect the survival of their kind. Many souls have been taken, many humans have been infected and some recovered, but still the spread has not yet reached its peak in many countries. While in some countries the curve of daily new infected has nearly reached zero, in others the spread is increasing exponentially. For some statistical analysis, we investigated daily cases of infections and deaths due to the COVID-19 spread that occurred in 54 countries in the European continent and 47 countries in the African continent from the beginning of the outbreak to 15 June 2020. To do this, we used the available data on the website of the World Health Organization (WHO) [1, 2]. Although mathematicians cannot provide vaccine or cure the disease in an infected person, they can use their mathematical tools to foresee what could possibly happen in the near future with some limitations [314]. With the new trend of spread, it is possible that the world will face a second wave of COVID-19 spread, this will be the aim of our work.

The paper is organized as follows. In Sect. 2, we present the definitions of differential and integral operators where singular and nonsingular kernels are used. In Sect. 3, the parameter estimations are presented for the infected and deaths in Africa and Europe using the Box–Jenkins model. In Sect. 4, the simulations for smoothing method for the infected and deaths in Africa and Europe are presented. In Sect. 5, the predictions about the cases of infections and deaths in Africa and Europe are provided. In Sect. 6, we give an analysis of COVID-19 spread based on fractal interpolation and fractal dimension. In Sect. 7, existence and uniqueness for a mathematical model with stochastic component are investigated. Also the numerical simulations for such a model are depicted. In Sect. 8, we present a modified scheme based on the Newton polynomial. In Sect. 9, we provide numerical solutions for the suggested COVID-19 model with different differential operators.

Differential and integral operators

In this section, we present some definitions of differential and integral operators with singular and nonsingular kernels. The fractional derivatives with power-law, exponential decay, and Mittag-Leffler kernel are given as follows:

Definition 1

Dtα0Cf(t)=1Γ(1α)0tddτf(τ)(tτ)αdτ,Dtα0CFf(t)=M(α)1α0tddτf(τ)exp[α1α(tτ)]dτ,Dtα0ABCf(t)=AB(α)1α0tddτf(τ)Eα[α1α(tτ)α]dτ. 1

The fractional integrals with power-law, exponential decay, and Mittag-Leffler kernel are given as follows:

Jtα0Cf(t)=1Γ(α)0t(tτ)α1f(τ)dτ,Jtα,β0CFf(t)=1αM(α)f(t)+αM(α)0tf(τ)dτ,Jtα,β0ABf(t)=1αAB(α)f(t)+αAB(α)Γ(α)0t(tτ)α1f(τ)dτ. 2

The fractal-fractional derivatives with power-law kernel, exponential decay, and Mittag-Leffler kernel are given as follows:

Dtα,β0FFPf(t)=1Γ(1α)ddtβ0tf(τ)(tτ)αdτ,Dtα,β0FFEf(t)=M(α)1αddtβ0tf(τ)exp[α1α(tτ)]dτ,Dtα,β0FFMf(t)=AB(α)1αddtβ0tf(τ)Eα[α1α(tτ)α]dτ, 3

where

df(t)dtβ=limtt1f(t)f(t1)t2βt12β(2β). 4

The fractal-fractional integrals with power-law, exponential decay, and Mittag-Leffler kernel are as follows:

Jtα,β0FFPf(t)=1Γ(α)0t(tτ)α1τ1βf(τ)dτ,Jtα,β0FFEf(t)=1αM(α)t1βf(t)+αM(α)0tτ1βf(τ)dτ,Jtα,β0FFMf(t)=1αAB(α)t1βf(t)+αAB(α)Γ(α)0t(tτ)α1τ1βf(τ)dτ. 5

Box–Jenkin’s model development

Autoregressive integrated moving average (ARIMA) approach suggested by Box and Jenkins is one of the most powerful techniques used in time series analysis. The ARIMA model is composed of three parts. First, the autoregressive part is a linear regression which has a relation between past values and future values of data series; second, the integrated part expresses how many times the data series has to be differenced to obtain a stationary series; and the last one is the moving average part which has a relation between past forecast errors and future values of data series [14]. These processes can be presented by the models AR(p), MA(q), ARMA(p,q), and ARIMA(p,d,q). We should decide which model we will choose for our data series. To do this, partial autocorrelation (PACF) and the autocorrelation (ACF) are helpful to obtain parameters for the AR model and the MA model, respectively.

Figures 1 and 2 depict graphs of autocorrelation functions for the infected and deaths in Africa and Europe.

Figure 1.

Figure 1

Autocorrelation function for the infected and deaths in Africa

Figure 2.

Figure 2

Autocorrelation function for the infected and deaths in Europe

Now we introduce these models. Let Yt be the value of the time series at time t. Time series as a p-order autoregressive process is as follows:

Yt=δ+φ1Yt1+φ2Yt2++φpYtp+εt, 6

which is shown as AR(p). Here, δ and εt describe constant and error terms, respectively. Time series as a qth degree of moving average process is given by

Yt=μ+εt+θ1εt1+θ2εt2++θqεtq, 7

which is shown as MA(q). The ARMA(p,q) expression is obtained as a combination of AR(p) and MA(q) equations:

Yt=δ+φ1Yt1+φ2Yt2++φpYtp+εt+θ1εt1+θ2εt2++θqεtq. 8

When the time series is not stationary, we take the difference d times to make it stationary. The ARIMA(p,q) model is given by

(1φ1lφ2l2φplp)ΔdYt=δ+εt+θ1εt1+θ2εt2++θqεtq. 9

In the ARIMA technique, the model performance can be measured by using some criteria, for instance, Akaike information criteria(AIC), Bayesian information criteria(BIC). Here, we benefit from the Akaike information criteria given as follows:

AIC=2log(l)+2k,BIC=2log(l)+klnn, 10

where l states likelihood of the data, n is the number of data points, and k also defines the intercept of the ARIMA model. The numerical simulation are depicted in Figs. 3, 4, 5 and 6.

Figure 3.

Figure 3

ARIMA model for the infected in Africa

Figure 4.

Figure 4

AR model for deaths in Africa

Figure 5.

Figure 5

ARIMA model for the infected in Europe

Figure 6.

Figure 6

AR model for deaths in Europe

According to data series for the infected in Africa, we use the ARIMA(2,1,0) model which is given by

(1φ1lφ2l2)(1l)Yt=c+εt. 11

Here,

AIC=1670.1734,BIC=1680.9388. 12

In Table 1, we give parameter estimation for infections in Africa.

Table 1.

Model estimation for infections in Africa

Parameter Value Standard error TStatistic
Constant 89.2032 56.6511 1.5746
AR{1} −0.44796 0.099221 −4.5147
AR{2} −0.17789 0.068294 −2.6047
Variance 168,446.2911 12,738.3089 13.2236

According to data series for deaths in Africa, we use the AR(1) model which is given by

(1φ1l)Yt=c+εt. 13

Here,

AIC=1056.6482,BIC=1064.7768. 14

In Table 2, we give parameter estimation for deaths in Africa.

Table 2.

Model estimation for deaths in Africa

Parameter Value Standard error TStatistic
Constant 12.581 6.0023 2.096
AR{1} 0.75094 0.082701 9.0802
Variance 694.3043 92.168 7.533

According to data series for the infected in Europe, we use the ARIMA(2,1,1) model which is given by

(1φ1lφ2l2)(1l)Yt=c+(1+θ1l)εt. 15

Here,

AIC=2690.5358,BIC=2705.2796. 16

In Table 3, we give parameter estimation for the infected in Europe.

Table 3.

Model estimation for the infected in Europe

Parameter Value Standard error TStatistic
Constant 83.7826 118.7108 0.70577
AR{1} 0.3216 0.59303 0.5423
AR{2} 0.035772 0.16277 0.21977
MA{1} −0.53222 0.58359 −0.91197
Variance 7,214,609.9182 569,786.6944 12.6619

According to data series for deaths in Europe, we use the AR(1) model which is given by

(1φ1l)Yt=c+εt. 17

Here,

AIC=1670.1734,BIC=1680.9388. 18

In Table 4, we give parameter estimation for deaths in Europe.

Table 4.

Model estimation for deaths in Europe

Parameter Value Standard error TStatistic
Constant 151.4852 163.967 0.92388
AR{1} 0.8865 0.041096 21.5714
Variance 460,062.22 27,485.093 16.7386

Brown’s exponential smoothing method

Brown’s linear exponential smoothing is one type of double exponential smoothing based on two different smoothed series. The formula is composed of an extrapolation of a line through the two centers. The Brown exponential smoothing method is helpful to model the time series having trend but no seasonality.

For non-adaptive Brown exponential smoothing, the procedure can be described as follows.

Firstly, we start with the following initialization:

  1. S0=u0,

  2. T0=u0,

  3. a0=2S0T0,

  4. F1=a0+b0.

Then we have the following calculations:

  1. St=αut+(1α)St1,

  2. Tt=αSt+(1α)Tt1,

  3. at=2St+Tt63,

  4. α(StTt)=(1α)bt,

  5. Ft+1=at+bt,

where 0<α<1 is the smoothing factor. St and Tt are the simply smoothed value and doubly smoothed value for the (t+1)th time period, respectively. Also at and bt describe the intercept and the slope, respectively.

In Figs. 7, 8, 9, and 10, we present the simulation for smoothing method for the infected and deaths in Africa and Europe where the smoothing factor was chosen as α=0.99.

Figure 7.

Figure 7

Exponential smoothing for the infected in Africa

Figure 8.

Figure 8

Exponential smoothing for deaths in Africa

Figure 9.

Figure 9

Exponential smoothing for the infected in Europe

Figure 10.

Figure 10

Exponential smoothing for deaths in Europe

Future prediction of daily new numbers of the infected and deaths: Africa and Europe

With the collected data using some statistical formula, it is possible to predict what will possibly happen in the near future. Having in mind what could possibly happen, several measures could be taken to avoid the worst case scenario. In this section, with the data collected for 101 countries from Africa (47) and Europe (54), we aim at presenting possible scenarios or events that could be observed in the near future, the daily numbers of deaths and infections. Numerical simulation are presented in Figs. 11, 12, 13 and 14.

Figure 11.

Figure 11

Prediction for the infected in Africa using Forecast Sheet

Figure 12.

Figure 12

Prediction for deaths in Africa using Forecast Sheet

Figure 13.

Figure 13

Prediction for the infected in Europe using Forecast Sheet

Figure 14.

Figure 14

Prediction for deaths in Europe using Forecast Sheet

In Figs. 15, 16, 17, and 18, we present fitting with smoothing spline for the infected and deaths in Africa and Europe.

Figure 15.

Figure 15

Fitting for the infected in Africa

Figure 16.

Figure 16

Fitting for deaths in Africa

Figure 17.

Figure 17

Fitting for the infected in Europe

Figure 18.

Figure 18

Fitting for deaths in Europe

An analysis of COVID-19 spread based on fractal interpolation and fractal dimension

In this section, we present some information about fractal dimension, interpolation, and blancmange curve.

Fractal dimension

Fractal dimensions enable us to compare fractals. Fractal dimensions are important because they can be defined in connection with real-world data, and they can be measured approximately by means of experiments. These numbers allow us to compare sets in the real world with the laboratory fractals.

Theorem

(The box counting theorem)

Let Nn(A) be the number of boxes of side length (1/2n). Then the fractal dimension D of A is given as [15]

D=limn{ln[Nn(A)]ln(2n)}. 19

Fractal interpolation

Euclidean geometry and calculus enable us to model using some lines and curves, the shapes that we encounter in the nature [15, 16]. In this section, we present an interpolation function which interpolates the data.

Definition 2

An interpolation function f:[x0,xN]R corresponding to the set of data {(xi,Fi)R2:i=0,1,2,,N} [15]

f(xi)=Fifor i=1,2,,N, 20

where x0<x1<x2<xN.

Let f:[x0,xN]R denote the unique continuous function which is called a piecewise linear interpolation function. Also this function is linear on each of the subintervals [xi1,xi], and it is represented by

f(x)=Fi1+(xxi1)(xixi1)(FiFi1)for x[xi1,xi],i=1,2,,N. 21

We have the following transformation, which is iterated:

fn(xy)=(tn0unyn)(xy)+(vnwn). 22

When solving this system for tn, un, vn, and wn in terms of the data and yn, we obtain the following:

tn=xnxn1xNx0,un=FnFn1xNx0ynFnF0xNx0,vn=xNxn1x0xnxNx0,wn=xNFn1x0FnxNx0ynxNF0x0FnxNx0, 23

where 0yn<1 is called the scaling factor [15].

Blancmange curve

The blancmange function can be given as an example of fractal interpolation function, and this function is defined by

n=0S(2nx)2n,x[0,1], 24

where S(x)=minmZ|xm|, xR.

However, many problems cannot be depicted when c=2 [16]. Then we discuss the limitations of this blancmange; for example, t can only go from 0 to 1, the periodic parameter is 2. Therefore, we change 2 to c, where c is a real number from 1 to a. Therefore, in this section, we extend the blancmange function to a large interval also with any given periodic parameter. So, we have the following formula:

n=0S(cnx)cn,x[0,a], 25

where c is the real number. We now present the extended blancmange function for different periodic parameters and different w.

The simulation are presented in Figs. 19, 20, 21, and 18.

Figure 19.

Figure 19

Blancmange function c=2

Figure 20.

Figure 20

Blancmange function c=3.7

Figure 21.

Figure 21

Blancmange function c=1.3

Mathematical model for COVID-19 outbreak

We consider the following mathematical model of COVID-19 spread:

S=Λ{δ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)}S,I=δ(t)(αI+w(βID+γIA+δ1IR+δ2IT))S(ε+ξ+λ+μ1)I,IA=ξI(θ+μ+χ+μ1)IA,ID=εI(η+φ+μ1)ID,IR=ηID+θIA(v+ξ+μ1)IR,IT=μIA+vIR(σ+τ+μ1)IT,R=λI+φID+χIA+ξIR+σIT(Φ+μ1)R,D=τIT,V=γ1S+ΦRμ1V. 26

The above model was suggested by Atangana and Seda, the model has a deterministic character. In this section, we convert the model to a stochastic one by introducing the effect of environmental white noise. To achieve this, we reformulate the model by adding the nonlinear perturbation into each equation of the system. The perturbation may depend on square of the classes S, I, IA, ID, IR, IT, R, D, and V respectively. Here, we perturb only the rate of each class. However, for the vaccine class, it will be perturbed by a natural death rate.

For the class S(t):γ1γ1+(Π11S+Π12)B1(t),For the class I(t):λλ+(Π21I+Π22)B2(t),For the class IA(t):θθ+(Π31IA+Π32)B3(t),For the class ID(t):ηη+(Π41ID+Π42)B4(t),For the class IR(t):vv+(Π51IR+Π52)B5(t),For the class IT(t):σσ+(Π61IT+Π62)B6(t),For the class R(t):ΦΦ+(Π71R+Π72)B7(t),For the class D(t):ττ no change,For the class V(t):μ1μ1+(Π81V+Π82)B8(t).

Therefore, the associated stochastic model is given as follows:

dS=[Λ{δ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)}S]dt+(Π11S+Π12)SdB1(t),dI=[δ(t)(αI+w(βID+γIA+δ1IR+δ2IT))S(ε+ξ+λ+μ1)I]dt+(Π21I+Π22)IdB2(t),dIA=[ξI(θ+μ+χ+μ1)IA]dt+(Π31IA+Π32)IAdB3(t),dID=[εI(η+φ+μ1)ID]dt+(Π41ID+Π42)IDdB4(t),dIR=[ηID+θIA(v+ξ+μ1)IR]dt+(Π51IR+Π52)IDdB5(t),dIT=[μIA+vIR(σ+τ+μ1)IT]dt+(Π61IT+Π62)ITdB6(t),dR=[λI+φID+χIA+ξIR+σIT(Φ+μ1)R]dt+(Π71R+Π72)RdB7(t),dV=[γ1S+ΦRμ1V]dt++(Π71V+Π72)VdB8(t). 27

In this conversion, the function Bi(t) represents the standard Brownian motions valid within the set of probability (Ω,A,{At}t0,P), where {At}t0 is filtration valid under the condition described in [17]. Here, Πi,j[1,2,3,4,5,6,7,8] are positive and are the intensities of the environmental random disturbance.

Existence and uniqueness

In this subsection, we present the existence and uniqueness of the system solutions of the stochastic model. To achieve the existence and uniqueness, we convert the system into Volterra type. But first we do the following for simplicity:

dS=F1(t,S,I,IA,ID,IR,IT,R,V)dt+G1(t,S)dB1(t),dI=F2(t,S,I,IA,ID,IR,IT,R,V)dt+G2(t,I)dB2(t),dIA=F3(t,I,IA)dt+G3(t,IA)dB3(t),dID=F4(t,I,ID,)dt+G4(t,ID)dB4(t),dIR=F5(t,IA,ID,IR)dt+G5(t,IR)dB5(t),dIT=F6(t,IA,IR,IT)dt+G6(t,IT)dB6(t),dR=F7(t,I,IA,ID,IR,IT,R)dt+G7(t,R)dB7(t),dV=F8(t,S,R,V)dt+G8(t,V)dB8(t). 28

Therefore, converting to Volterra, we get

S(t)=S(0)+0tF1(τ,S,I,IA,ID,IR,IT,R,V)dτ+0tG1(τ,S)dB1(τ),I(t)=I(0)+0tF2(τ,S,I,IA,ID,IR,IT,R,V)dτ+0tG2(τ,I)dB2(τ),IA(t)=IA(0)+0tF3(τ,I,IA)dτ+0tG3(τ,IA)dB3(τ),ID(t)=ID(0)+0tF4(τ,I,ID)dτ+0tG4(τ,ID)dB4(τ),IR(t)=IR(0)+0tF5(τ,IA,ID,IR)dτ+0tG5(τ,IR)dB5(τ),IT(t)=IT(0)+0tF6(τ,IA,IR,IT)dτ+0tG6(τ,IT)dB6(τ),R(t)=R(0)+0tF7(τ,I,IA,ID,IR,IT,R)dτ+0tG7(τ,R)dB7(τ),V(t)=V(0)+0tF8(τ,S,R,V)dτ+0tG8(τ,S)dB8(τ). 29

We present the existence and uniqueness of the stochastic system of COVID-19 model. This will be achieved via the following theorem.

Theorem

Assume that there exist positive constants Ki, Ki such that

  • (i)
    |Fi(x,t)Fi(xi,t)|2<Ki|xxi|2,|Gi(x,t)Gi(xi,t)|2<Ki|xxi|2 30
  • (ii)
    (x,t)R8×[0,T]
    |Fi(x,t)|2,|Gi(x,t)|2<K(1+|x|2). 31

Then there exists a unique solution X(t)R8 for our model and it belongs to M2([0,T],R8).

The proof can be found in [17], but we have to verify (i) and (ii) for our system. Without loss of generality, we start our investigation with functions F1(t,S,I,IA,ID,IR,IT,R,V) and G1(t,S). For the function F, the proof will be performed for (t,S). Thus

|F1(t,S)F1(t,S1)|2=|δ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)(SS1)|2. 32

We define the following norm:

φ=supt[0,T]|φ|2, 33

then

|F1(S,t)F1(S1,t)|2supt[0,T]|δ(t)(αI+w(βID+γIA+δ1IR+δ2IT))(SS1)|2δ(t)(αI+w(βID+γIA+δ1IR+δ2IT))2|SS1|2K1|SS1|2 34

and

|G1(S,t)G1(S1,t)|2=|(Π11S+Π12)S(Π11S1+Π12)S1|2=|Π11(S2S12)Π12(SS1)|2=(Π11(S+S1)+Π12)2|SS1|2=(Π112(S+S1)2+2Π11Π12(S+S1)+Π122)|SS1|2=(Π112(S2+2SS1+S12)+2Π11Π12(S+S1)+Π122)|SS1|2{Π112(supt[0,T]|S2(t)|+2supt[0,T]|S(t)|supt[0,T]|S1(t)|+supt[0,T]|S12(t)|)+2Π11Π12(supt[0,T]|S(t)|+supt[0,T]|S1(t)|)+Π122}×|SS1|2{Π112(S2+2SS1+S12)+2Π11Π12SS1+Π122}|SS1|2K1|SS1|2, 35

where

K1=Π112(S2+2SS1+S12)+2Π11Π12SS1+Π122=Π112(S+S1)2+2Π11Π12SS1+Π122. 36

Similarly,

K2=Π212(I+I1)2+2Π21Π22II1+Π222,K3=Π312(IA+IA1)2+2Π31Π32IAIA1+Π322,K4=Π412(ID+ID1)2+2Π41Π42IDID1+Π422,K5=Π512(IR+IR1)2+2Π51Π52IRIR1+Π522,K6=Π612(IT+IT1)2+2Π61Π62ITIT1+Π622,K7=Π712(R+R1)2+2Π71Π72RR1+Π722,K8=Π812(V+V1)2+2Π81Π82VV1+Π822. 37

Also

|F2(I,t)F2(I1,t)|2=|δ(t)α(II1)(ε+ξ+λ+μ1)(II1)|2=|(δ(t)α(ε+ξ+λ+μ1))(II1)|2supt[0,T]|(δ(t)α(ε+ξ+λ+μ1))|2|II1|2δ(t)|α(ε+ξ+λ+μ1)|2|II1|2K2|II1|2, 38

where

K2=δ(t)|α(ε+ξ+λ+μ1)|2. 39

Also

|F3(IA,t)F3(IA1,t)|2=|(θ+μ+χ+μ1)(IAIA1)|22|(θ+μ+χ+μ1)|2|IAIA1|2K3|IAIA1|2, 40

where

K3=2|(θ+μ+χ+μ1)|2. 41

Similarly, we evaluate

|F4(ID,t)F4(ID1,t)|2=|η+φ+μ1|2|IDID1|2K4|IDID1|2,|F5(IR,t)F5(IR1,t)|2=|v+ξ+μ1|2|IRIR1|2K5|IRIR1|2,|F6(IT,t)F6(IT1,t)|2=|σ+τ+μ1|2|ITIT1|2K6|ITIT1|,|F7(R,t)F7(R1,t)|2=|Φ+μ1|2|RR1|2K7|RR1|,|F8(V,t)F8(V1,t)|2=|μ1|2|VV1|2K8|VV1|2. 42

For both classes Gi and Fi, we have verified condition (i). Now we verify the second condition.

|F1(S,t)|2=|Λδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)S|2|ΛSδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)S|2|S|2|Λδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)|2<(|S|2+1)|Λδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)|2<(|S|2+1)|Λδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)|2<(|S|2+1)supt[0,T]|Λδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)|2<K1(|S|2+1), 43

where

K1=supt[0,T]|Λδ(t)(αI+w(βID+γIA+δ1IR+δ2IT)+γ1+μ1)|2. 44

Then

|G1(S,t)G1(S1,t)|2=|(Π11S+Π12)S|2|Π11S2+Π12S2|2(Π11+Π12)2|S2|2(Π11+Π12)2supt[0,T]|S2||S|2(Π11+Π12)2S2(|S|2+1)K1(|S|2+1), 45

where

K1=(Π11+Π12)2S2. 46

Similarly,

K2=(Π21+Π22)2I2,K3=(Π31+Π32)2IA2,K4=(Π41+Π42)2ID2,K5=(Π51+Π52)2IR2,K6=(Π61+Π62)2IT2,K7=(Π71+Π72)2R2,K8=(Π81+Π82)2V2. 47

Also, we have

|F2(I,t)|2=|δ(t)(w(βID+γIA+δ1IR+δ2IT))S+δ(t)αIS(ε+ξ+λ+μ1)I|2|δ(t)(w(βID+γIA+δ1IR+δ2IT))S+δ(t)αS(ε+ξ+λ+μ1)||I|2<(|S|2+1)supt[0,T]|δ(t)(w(βID+γIA+δ1IR+δ2IT))S+δ(t)αS(ε+ξ+λ+μ1)|2<K2(|I|2+1),|F3(IA,t)|2=|ξI(θ+μ+χ+μ1)IA|2|ξI(θ+μ+χ+μ1)|2|IA|2(|IA|2+1)supt[0,T]|ξI(θ+μ+χ+μ1)|2K3(|IA|2+1),|F4(IA,t)|2=|εI(η+φ+μ1)ID|2(|ID|2+1)supt[0,T]|εI(η+φ+μ1)|2K4(|ID|2+1),|F5(IR,t)|2(|IR|2+1)supt[0,T]|ηID+θIA(v+ξ+μ1)|2K5(|IR|2+1),|F6(IT,t)|2(|IT|2+1)supt[0,T]|μIA+vIR(σ+τ+μ1)|2K6(|IT|2+1),|F6(IT,t)|2(|IT|2+1)supt[0,T]|μIA+vIR(σ+τ+μ1)|2K6(|IT|2+1),|F7(R,t)|2(|R|2+1)supt[0,T]|λI+φID+χIA+ξIR+σIT(Φ+μ1)|2K7(|R|2+1). 48

Finally, we have

|F8(V,t)|2(|V|2+1)supt[0,T]|γ1S+ΦRμ1|2K8(|V|2+1).

Both Gi and Fi verify the second condition. Therefore, according to the above theorem, the system has a unique system solution.

Numerical simulation for the stochastic model

Numerical solutions of the suggested stochastic model are presented in Figs. 2225. The numerical solution depicts the future stochastic behavior of the susceptible class, five sub-classes of the infected population, the recovered class, the death class, and the vaccination class. These are depicted in figures below.

Figure 23.

Figure 23

Stochastic behavior of IA(t) and ID(t) classes

Figure 24.

Figure 24

Stochastic behavior of IR(t) and IT(t) classes

Figure 22.

Figure 22

Stochastic behavior of S(t) and I(t) classes

Figure 25.

Figure 25

Stochastic behavior of R(t) and V(t) classes

Atangana–Seda modified scheme

The mathematical model considered in this work has the ability to depict two to three waves of COVID-19 spread. The model is subjected to a system of initial conditions. Additionally, the model is nonlinear, thus it is impossible to obtain exact solutions to the system, thus numerical schemes are needed. We present a numerical scheme based on the Newton polynomial [18]. However, one needs the initial condition and two additional components for the scheme to be implemented. In this section, we present a modified version that will not need the two additional components, and then the scheme will be used later to provide numerical solutions for the suggested COVID-19 model with different differential operators. We start with the classical case, the following is considered:

dy(t)dt=f(t,y(t)). 49

Then

yn+1=yn+{512f(tn2,yn2)43f(tn1,yn1)+512f(tn,yn)}Δt. 50

To reduce these requirements, we proceed as follows:

ynyn1Δt=f(tn,yn)yn1=ynf(tn,yn)Δt. 51

On the other hand,

yn1yn2Δt=f(tn1,yn1) 52

or

yn2=yn1f(tn1,yn1)Δt=ynΔtf(tn,yn)Δtf(tn1,ynf(tn,yn)Δt). 53

Replacing yn2 and yn1 with their values, we obtain

yn+1=yn+512Δtf(tn2,ynΔtf(tn,yn)Δtf(tn1,ynf(tn,yn)Δt))43f(tn1,ynf(tn,yn)Δt)+2312f(tn,yn)Δt. 54

The above does not need y1 and y2, only the initial condition. With the Caputo–Fabrizio derivative, we consider the following:

Dtα0CFy(t)=f(t,y(t)). 55

From the definition of the Caputo–Fabrizio integral, we can reformulate the above equation as follows:

y(t)y(0)=1αM(α)f(t,y(t))+αM(α)0tf(τ,y(τ))dτ. 56

We have, at the point tn+1=(n+1)Δt,

y(tn+1)y(0)=1αM(α)f(tn+1,y(tn+1))+αM(α)0tn+1f(τ,y(τ))dτ, 57

and at the point tn=nΔt,

y(tn)y(0)=1αM(α)f(tn,y(tn))+αM(α)0tnf(τ,y(τ))dτ. 58

Taking the difference of these equations, we can write the following:

y(tn+1)y(tn)=1αM(α)[f(tn+1,y(tn+1))f(tn,y(tn))]+αM(α)tntn+1f(τ,y(τ))dτ=1αM(α)[f(tn+1,y(tn)Δtf(tn,y(tn)))f(tn,y(tn))]+αM(α){512f(tn2,ynΔtf(tn,yn)Δtf(tn1,ynf(tn,yn)Δt))Δt43f(tn1,ynf(tn,yn)Δt)Δt+2312f(tn,yn)Δt}. 59

With the Caputo derivative, we write

{Dtα0Cy(t)=f(t,y(t)),y(0)=y0. 60

We convert the above into

y(t)y(0)=1Γ(α)0tf(τ,y(τ))(tτ)α1dτ. 61

At the point tn+1=(n+1)Δt, we have the following:

y(tn+1)y(0)=1Γ(α)0tn+1f(τ,y(τ))(tn+1τ)α1dτ,

and we write

y(tn+1)=y(0)+1Γ(α)j=2ntjtj+1f(τ,y(τ))(tn+1τ)α1dτ.

After putting the Newton polynomial into the above equation, the above equation can be written as follows:

yn+1=y0+(Δt)αΓ(α+1)j=2nf(tj2,yj2)[(nj+1)α(nj)α]+(Δt)αΓ(α+2)j=2n[f(tj1,yj1)f(tj2,yj2)]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+α(Δt)α2Γ(α+3)j=2n[f(tj,yj)2f(tj1,yj1)+f(tj2,yj2)]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]], 62

where

f(tj1,yj1)=f(tj1,yjf(tj,yj)Δt),f(tj2,yj2)=f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt)). 63

With Atangana–Baleanu, we have

{Dtα0ABCy(t)=f(t,y(t)),y(0)=y0. 64

We transform the above equation into

y(t)y(0)=1αAB(α)f(t,y(t))+αAB(α)Γ(α)0tf(τ,y(τ))(tτ)α1dτ. 65

At the point tn+1=(n+1)Δt, we have the following:

y(tn+1)y(0)=1αAB(α)f(t,y(t))+αAB(α)Γ(α)0tn+1f(τ,y(τ))(tn+1τ)α1dτ, 66

and we write

y(tn+1)=y(0)+1αAB(α)f(tn+1,yn+1)+αAB(α)Γ(α)j=2ntjtj+1f(τ,y(τ))(tn+1τ)α1dτ. 67

After putting the Newton polynomial into the above equation, the above equation can be written as follows:

yn+1=y0+1αAB(α)f(tn+1,y(tn)Δtf(tn,y(tn)))+α(Δt)αAB(α)Γ(α+1)j=2nf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))×[(nj+1)α(nj)α]+α(Δt)αAB(α)Γ(α+2)j=2n[f(tj1,yjf(tj,yj)Δt)f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+α(Δt)α2AB(α)Γ(α+3)j=2n[f(tj,yj)2f(tj1,yj1)+f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 68

With the Caputo–Fabrizio fractal-fractional derivative, we consider

Dtα,β0FFEy(t)=f(t,y(t)),y(0)=y0. 69

Applying the associated integral operator with exponential kernel, we can reformulate equation (69) as follows:

y(t)=1αM(α)t1βf(t,y(t))+αM(α)0tf(τ,y(τ))τ1βdτ. 70

At the point tn+1=(n+1)Δt,

y(tn+1)=1αM(α)tn+11βf(tn+1,y(tn+1))+αM(α)0tn+1f(τ,y(τ))τ1βdτ, 71

and at the point tn=nΔt, we have

y(tn)=1αM(α)tn1βf(tn,y(tn))+αM(α)0tnf(τ,y(τ))τ1βdτ. 72

If we take the difference of these equations, we obtain the following equation:

y(tn+1)y(tn)=1αM(α)[tn+11βf(tn+1,y(tn+1))tn1βf(tn,y(tn))]+αM(α)tntn+1f(τ,y(τ))τ1βdτ. 73

For brevity, we consider

y(tn+1)y(tn)=1αM(α)[F(tn+1,y(tn+1))F(tn,y(tn))]+αM(α)tntn+1F(τ,y(τ))dτ, 74

where

F(t,y(t))=f(t,y(t))t1β. 75

We can rearrange the above scheme as follows:

yn+1yn=1αM(α)[F(tn+1,y(tn)Δtf(tn,y(tn)))F(tn,y(tn))]+αM(α){512F(tn2,ynΔtf(tn,yn)ΔtF(tn1,ynf(tn,yn)Δt))Δt43F(tn1,ynf(tn,yn)Δt)Δt+2312F(tn,yn)Δt}. 76

If we replace F(t,y(t)) with its value, we can solve our equation numerically with the following scheme:

yn+1yn=1αM(α)[tn+11βf(tn+1,y(tn)Δtf(tn,y(tn)))tn1βf(tn,y(tn))]+αM(α){tn21β512F(tn2,ynΔtf(tn,yn)Δtf(tn1,ynf(tn,yn)Δt))Δt43tn11βf(tn1,ynf(tn,yn)Δt)Δt+2312tn1βf(tn,yn)Δt}. 77

With the Atangana–Baleanu fractal-fractional derivative, we write

Dtα,β0FFMy(t)=f(t,y(t)),y(0)=y0. 78

Applying the new fractional integral with Mittag-Leffler kernel, we transform the above equation into

y(t)=y(0)+1αAB(α)t1βf(t,y(t))+αAB(α)Γ(α)0tf(τ,y(τ))(tτ)α1τ1βdτ. 79

At the point tn+1=(n+1)Δt, we obtain the following:

y(tn+1)=y(0)+1αAB(α)tn+11βf(tn+1,y(tn+1))+αAB(α)Γ(α)0tn+1f(τ,y(τ))(tn+1τ)α1τ1βdτ. 80

For simplicity, we shall take

F(t,y(t))=f(t,y(t))t1β. 81

We also have

y(tn+1)=y(0)+1αAB(α)F(tn+1,y(tn+1))+αAB(α)Γ(α)j=2ntjtj+1F(τ,y(τ))(tn+1τ)α1dτ. 82

Replacing them into the above equation and substituting F(t,y(t))=f(t,y(t))t1β, we can get the following numerical scheme:

yn+1=1αAB(α)tn+11βf(tn+1,y(tn+1))+α(Δt)αAB(α)Γ(α+1)j=2ntj21βf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))×[(nj+1)α(nj)α]+α(Δt)αAB(α)Γ(α+2)j=2n[tj11βf(tj1,yjf(tj,yj)Δt)tj21βf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+α(Δt)α2AB(α)Γ(α+3)j=2n[tj1βg(tj,yj)2tj11βf(tj1,yjf(tj,yj)Δt)+tj21βf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 83

With the Caputo fractal-fractional derivative, we consider the following:

Dtα,β0FFPy(t)=f(t,y(t)),y(0)=y0. 84

Applying the new fractional integral with power-law kernel, we transform the above equation into

y(t)=y(0)+1Γ(α)0tf(τ,y(τ))(tτ)α1τ1βdτ. 85

At the point tn+1=(n+1)Δt, we obtain the following:

y(tn+1)=y(0)+1Γ(α)0tn+1f(τ,y(τ))(tn+1τ)α1τ1βdτ. 86

For simplicity, we shall take

F(t,y(t))=f(t,y(t))t1β. 87

We also have

y(tn+1)=y(0)+1Γ(α)j=2ntjtj+1F(τ,y(τ))(tn+1τ)α1dτ. 88

Replacing them into the above equation and substituting F(t,y(t))=f(t,y(t))t1β, we can get the following numerical scheme:

yn+1=(Δt)αΓ(α+1)j=2ntj21βf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))×[(nj+1)α(nj)α]+(Δt)αΓ(α+2)j=2n[tj11βf(tj1,yjf(tj,yj)Δt)tj21βf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+(Δt)α2Γ(α+3)j=2n[tj1βg(tj,yj)2tj11βf(tj1,yjf(tj,yj)Δt)+tj21βf(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 89

Finally, we present the numerical scheme with fractal-fractional derivative with variable order. We start with the Caputo–Fabrizio case:

Dtα,β(t)0FFEy(t)=f(t,y(t)),y(0)=y0. 90

The above equation can be reformulated as follows:

y(t)=1αM(α)t2β(t)[β(t)ln(t)+2β(t)t]f(t,y(t))+αM(α)0tf(τ,y(τ))[β(τ)ln(τ)+2β(τ)τ]τ2β(τ)dτ. 91

We write the above equation as follows:

y(tn+1)y(tn)=1αM(α)[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)f(tn+1,y(tn+1))tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)f(tn,y(tn))]+αM(α)tntn+1f(τ,y(τ))[β(τ)ln(τ)+2β(τ)τ]τ2β(τ)dτ. 92

For simplicity, we take

F(t,y(t))=f(t,y(t))[β(t)ln(t)+2β(t)t]t2β(t), 93

and we have

y(tn+1)y(tn)=1αM(α)[F(tn+1,y(tn+1))F(tn,y(tn))]+αM(α)tntn+1F(τ,y(τ))dτ. 94

If we do the same routine and replace F(t,y(t)) with its value, we have the following numerical approximation:

yn+1=yn+1αM(α)[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×f(tn+1,y(tn)Δtf(tn,y(tn)))tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×f(tn,y(tn))]+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×2312f(tn,yn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×f(tn1,ynf(tn,yn)Δt)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×512f(tn2,ynΔtf(tn,yn)Δtf(tn1,ynf(tn,yn)Δt))Δt}. 95

We deal with our problem involving the new constant fractional order and variable fractal dimension

Dtα,β(t)0FFMy(t)=f(t,y(t)),y(0)=y0, 96

where the kernel is the Mittag-Leffler kernel. If we integrate the above equation with the new integral operator including the Mittag-Leffler kernel, the above equation can be converted to

y(t)=1αAB(α)t2β(t)[β(t)ln(t)+2β(t)t]f(t,y(t))+αAB(α)Γ(α)0tf(τ,y(τ))(tτ)α1×[β(τ)ln(τ)+2β(τ)τ]τ2β(τ)dτ. 97

At the point tn+1=(n+1)Δt, we have the following:

y(tn+1)=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×f(tn+1,y(tn+1))+αAB(α)Γ(α)0tn+1f(τ,y(τ))(tn+1s)α1×[β(τ)ln(τ)+2β(τ)τ]τ2β(τ)dτ. 98

For brevity, we consider

F(τ,y(τ))=f(τ,y(τ))[β(τ)ln(τ)+2β(τ)τ]τ2β(τ), 99

and we can write the following:

y(tn+1)=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×f(tn+1,y(tn+1))+αAB(α)Γ(α)j=2ntjtj+1F(τ,y(τ))(tn+1τ)α1dτ. 100

One can replace the Newton polynomial in the above equation as follows. Thus, we have the following scheme:

yn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×f(tn+1,y(tn+1))+α(Δt)αAB(α)Γ(α+1)j=2nF(tj2,yj2)[(nj+1)α(nj)α]+α(Δt)αAB(α)Γ(α+2)j=2n[F(tj1,yj1)F(tj2,yj2)]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+α(Δt)α2AB(α)Γ(α+3)j=2n[F(tj,yj)2F(tj1,yj1)+F(tj2,yj2)]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 101

Replacing the function G(t,y(t)) with its value, we can present the following scheme for numerical solution of our equation:

yn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×f(tn+1,yn+f(tn,yn)Δt)+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)[β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2]×f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))[(nj+1)α(nj)α]+α(Δt)αAB(α)Γ(α+2)j=2n[tj12β(tj1)[β(tj)β(tj1)Δtlntj1+2β(tj1)tj1]×f(tj1,yjf(tj,yj)Δt)tj22β(tj2)[β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2]×f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+α(Δt)α2AB(α)Γ(α+3)j=2n[tj2β(tj)[β(tj+1)β(tj)Δtlntj+2β(tj)tj]×f(tj,yj)Δt2tj12β(tj1)[β(tj)β(tj1)Δtlntj1+2β(tj1)tj1]×f(tj1,yjf(tj,yj)Δt)+tj22β(tj2)[β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2]×f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 102

We deal with our problem involving the new constant fractional order and variable fractal dimension

Dtα,β(t)0FFPy(t)=f(t,y(t)),y(0)=y0, 103

where the kernel is the power-law kernel. If we integrate equation (103) with the new integral operator including the power-law kernel, the above equation can be converted to

y(t)=1Γ(α)0tf(τ,y(τ))(tτ)α1[β(τ)ln(τ)+2β(τ)τ]τ2β(τ)dτ. 104

At the point tn+1=(n+1)Δt, we have the following:

y(tn+1)=1Γ(α)0tn+1f(τ,y(τ))(tn+1s)α1×[β(τ)ln(τ)+2β(τ)τ]τ2β(τ)dτ. 105

For brevity, we consider

F(τ,y(τ))=f(τ,y(τ))[β(τ)ln(τ)+2β(τ)τ]τ2β(τ), 106

and we can write the following:

y(tn+1)=1Γ(α)j=2ntjtj+1F(τ,y(τ))(tn+1τ)α1dτ. 107

Thus, we have the following scheme:

yn+1=(Δt)αΓ(α+1)j=2nF(tj2,yj2)[(nj+1)α(nj)α]+(Δt)αΓ(α+2)j=2n[F(tj1,yj1)F(tj2,yj2)]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+(Δt)α2Γ(α+3)j=2n[F(tj,yj)2F(tj1,yj1)+F(tj2,yj2)]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 108

Replacing the function G(t,y(t)) with its value, we can present the following scheme for numerical solution of our equation:

yn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)[β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2]×f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))[(nj+1)α(nj)α]+(Δt)αΓ(α+2)j=2n[tj12β(tj1)[β(tj)β(tj1)Δtlntj1+2β(tj1)tj1]×f(tj1,yjf(tj,yj)Δt)tj22β(tj2)[β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2]×f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)]+(Δt)α2Γ(α+3)j=2n[tj2β(tj)[β(tj+1)β(tj)Δtlntj+2β(tj)tj]×f(tj,yj)Δt2tj12β(tj1)[β(tj)β(tj1)Δtlntj1+2β(tj1)tj1]×f(tj1,yjf(tj,yj)Δt)+tj22β(tj2)[β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2]×f(tj2,yjΔtf(tj,yj)Δtf(tj1,yjf(tj,yj)Δt))]×[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]]. 109

Application to COVID-19 model

In this section, using the suggested numerical scheme, we present its application to solve the mathematical model of COVID-19 with possibility of waves. The numerical scheme will be applied for all cases where the differential operators are with classical differential operators, modern fractional differential operators, and variable orders, although only few examples will be used for numerical simulations. Firstly, we shall use the Caputo–Fabrizio fractional derivative

Dtα0CFS=Λ(δ(t)(αI+wβID+γwIA+wδ1IR+wδ2IT)+γ1+μ1)S,Dtα0CFI=(δ(t)(αI+wβID+γwIA+wδ1IR+wδ2IT))S(ε+ξ+λ+μ1)I,Dtα0CFIA=ξI(θ+μ+χ+μ1)IA,Dtα0CFID=εI(η+φ+μ1)ID,Dtα0CFIR=ηID+θIA(v+ξ+μ1)IR,Dtα0CFIT=μIA+vIR(σ+τ+μ1)IT,Dtα0CFR=λI+φID+χIA+ξIR+σIT(Φ+μ1)R,Dtα0CFD=τIT,Dtα0CFV=γ1S+ΦRμ1V. 110

For simplicity, we rearrange the above equation as follows:

Dtα0CFS=S(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFI=I(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFIA=IA(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFID=ID(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFIR=IR(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFIT=IT(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFR=R(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFD=D(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0CFV=V(t,S,I,IA,ID,IR,IT,R,D,V). 111

Thus, we can have the following scheme for our model:

Sn+1=Sn+1αM(α)[S(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)S(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 112
Sn+1=+αM(α){2312S(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43S(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512S(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},In+1=Sn+1αM(α)[I(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)I(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 113
In+1=+αM(α){2312I(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43I(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512I(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IAn+1=IAn+1αM(α)[IA(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IA(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 114
IAn+1=+αM(α){2312IA(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43IA(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512IA(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IDn+1=IDn+1αM(α)[ID(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)ID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 115
IDn+1=+αM(α){2312ID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43ID(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512ID(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IRn+1=IRn+1αM(α)[IR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 116
IRn+1=+αM(α){2312IR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43IR(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512IR(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},ITn+1=ITn+1αM(α)[IT(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IT(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 117
ITn+1=+αM(α){2312IT(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43IT(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512IT(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},Rn+1=Rn+1αM(α)[R(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)R(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 118
Rn+1=+αM(α){2312R(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43R(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512R(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},Dn+1=Dn+1αM(α)[D(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)D(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 119
Dn+1=+αM(α){2312D(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43D(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512D(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},Vn+1=Vn+1αM(α)[V(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)V(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]Vn+1=+αM(α){2312V(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43V(tn,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512V(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}. 120

With the Atangana–Baleanu fractional derivative, we can solve numerically our model as follows:

Sn+1=1αAB(α)S(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)+α(Δt)αAB(α)Γ(α+1)×j=2nS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+α(Δt)αAB(α)Γ(α+2)×j=2n[S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+α(Δt)α2AB(α)Γ(α+3)×j=2n[S(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,In+1=1αAB(α)I(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)+α(Δt)αAB(α)Γ(α+1)×j=2nI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+α(Δt)αAB(α)Γ(α+2)×j=2n[I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+α(Δt)α2AB(α)Γ(α+3)×j=2n[I(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IAn+1=1αAB(α)IA(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IAn+1=+α(Δt)αAB(α)Γ(α+1)IAn+1=×j=2nIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIAn+1=+α(Δt)αAB(α)Γ(α+2)IAn+1=×j=2n[IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIAn+1=+α(Δt)α2AB(α)Γ(α+3)IAn+1=×j=2n[IA(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IDn+1=1αAB(α)ID(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IDn+1=+α(Δt)αAB(α)Γ(α+1)IDn+1=×j=2nID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIDn+1=+α(Δt)αAB(α)Γ(α+2)IDn+1=×j=2n[ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIDn+1=+α(Δt)α2AB(α)Γ(α+3)IDn+1=×j=2n[ID(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IRn+1=1αAB(α)IR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IRn+1=+α(Δt)αAB(α)Γ(α+1)IRn+1=×j=2nIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIRn+1=+α(Δt)αAB(α)Γ(α+2)IRn+1=×j=2n[IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIRn+1=+α(Δt)α2AB(α)Γ(α+3)IRn+1=×j=2n[IR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,ITn+1=1αAB(α)IT(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)ITn+1=+α(Δt)αAB(α)Γ(α+1)ITn+1=×j=2nIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠITn+1=+α(Δt)αAB(α)Γ(α+2)ITn+1=×j=2n[IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣITn+1=+α(Δt)α2AB(α)Γ(α+3)ITn+1=×j=2n[IT(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Rn+1=1αAB(α)R(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Rn+1=+α(Δt)αAB(α)Γ(α+1)Rn+1=×j=2nR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠRn+1=+α(Δt)αAB(α)Γ(α+2)Rn+1=×j=2n[R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣRn+1=+α(Δt)α2AB(α)Γ(α+3)Rn+1=×j=2n[R(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Dn+1=1αAB(α)D(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Dn+1=+α(Δt)αAB(α)Γ(α+1)Dn+1=×j=2nD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠDn+1=+α(Δt)αAB(α)Γ(α+2)Dn+1=×j=2n[D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣDn+1=+α(Δt)α2AB(α)Γ(α+3)Dn+1=×j=2n[D(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Vn+1=1αAB(α)V(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Vn+1=+α(Δt)αAB(α)Γ(α+1)Vn+1=×j=2nV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠVn+1=+α(Δt)αAB(α)Γ(α+2)Vn+1=×j=2n[V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣVn+1=+α(Δt)α2AB(α)Γ(α+3)Vn+1=×j=2n[V(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ, 121

where

Δ=[(nj+1)α[2(nj)2+(3α+10)(nj)+2α2+9α+12](nj)α[2(nj)2+(5α+10)(nj)+6α2+18α+12]],Σ=[(nj+1)α(nj+3+2α)(nj)α(nj+3+3α)],Π=[(nj+1)α(nj)α]. 122

With the Caputo fractional derivative, we can obtain the following:

Sn+1=(Δt)αΓ(α+1)j=2nS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+(Δt)αΓ(α+2)j=2n[S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+(Δt)α2Γ(α+3)j=2n[S(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,In+1=(Δt)αΓ(α+1)j=2nI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIn+1=+(Δt)αΓ(α+2)j=2n[I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIn+1=+(Δt)α2Γ(α+3)j=2n[I(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]In+1=×Δ,IAn+1=(Δt)αΓ(α+1)j=2nIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+(Δt)αΓ(α+2)j=2n[IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+(Δt)α2Γ(α+3)j=2n[IA(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IDn+1=(Δt)αΓ(α+1)j=2nID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIDn+1=+(Δt)αΓ(α+2)IDn+1=×j=2n[ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIDn+1=+(Δt)α2Γ(α+3)IDn+1=×j=2n[ID(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IDn+1=×Δ,IRn+1=(Δt)αΓ(α+1)j=2nIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIRn+1=+(Δt)αΓ(α+2)j=2n[IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIRn+1=+(Δt)α2Γ(α+3)j=2n[IR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IRn+1=×Δ,ITn+1=(Δt)αΓ(α+1)j=2nIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠITn+1=+α(Δt)αΓ(α+2)j=2n[IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣITn+1=+(Δt)α2Γ(α+3)j=2n[IT(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]ITn+1=×Δ,Rn+1=(Δt)αΓ(α+1)j=2nR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠRn+1=+(Δt)αΓ(α+2)j=2n[R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Rn+1=×ΣRn+1=+(Δt)α2Γ(α+3)j=2n[R(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Rn+1=×Δ,Dn+1=(Δt)αΓ(α+1)j=2nD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠDn+1=+(Δt)αΓ(α+2)j=2n[D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Dn+1=×ΣDn+1=+(Δt)α2Γ(α+3)j=2n[D(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Dn+1=×Δ,Vn+1=(Δt)αΓ(α+1)j=2nV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+(Δt)αΓ(α+2)j=2n[V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+(Δt)α2Γ(α+3)j=2n[V(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ. 123

We now do the same routine for fractal-fractional derivatives. We start with the Caputo–Fabrizio fractal-fractional derivative

Dtα0FFES=S(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFEI=I(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFEIA=IA(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFEID=ID(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFEIR=IR(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFEIT=IT(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFER=R(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFED=D(t,S,I,IA,ID,IR,IT,R,D,V),Dtα0FFEV=V(t,S,I,IA,ID,IR,IT,R,D,V). 124

After applying the fractional integral with exponential kernel and putting the Newton polynomial into these equations, we can solve our model as follows:

Sn+1=Sn+1αM(α)[tn+11βS(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βS(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βS(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βS(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βS(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}, 125
In+1=Sn+1αM(α) 126
In+1=[tn+11βI(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βI(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]In+1=+αM(α)In+1=×{2312tn1βI(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βI(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βI(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IAn+1=IAn+1αM(α)[tn+11βIA(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βIA(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βIA(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βIA(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βIA(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}, 127
IDn+1=IDn+1αM(α)[tn+11βID(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βID(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βID(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}, 128
IRn+1=IRn+1αM(α)[tn+11βIR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βIR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 129
IRn+1=+αM(α)IRn+1=×{2312tn1βIR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βIR(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βIR(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},ITn+1=ITn+1αM(α)[tn+11βIT(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βIT(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βIT(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βIT(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βIT(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}, 130
Rn+1=Rn+1αM(α)[tn+11βR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βR(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βR(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}, 131
Dn+1=Dn+1αM(α)[tn+11βD(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βD(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βD(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βD(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βD(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}, 132
Vn+1=Vn+1αM(α)[tn+11βV(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn1βV(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α)×{2312tn1βV(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn11βV(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn21βV(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}. 133

For the Atangana–Baleanu fractal-fractional derivative, we can have the following numerical scheme:

Sn+1=1αAB(α)tn+11βS(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Sn+1=+α(Δt)αAB(α)Γ(α+1)Sn+1=×j=2ntj21βS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠSn+1=+α(Δt)αAB(α)Γ(α+2)Sn+1=×j=2n[tj11βS(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣSn+1=+α(Δt)α2AB(α)Γ(α+3)Sn+1=×j=2n[tj1βS(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βS(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,In+1=1αAB(α)tn+11βI(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)In+1=+α(Δt)αAB(α)Γ(α+1)In+1=×j=2ntj21βI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIn+1=+α(Δt)αAB(α)Γ(α+2)In+1=×j=2n[tj11βI(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIn+1=+α(Δt)α2AB(α)Γ(α+3)In+1=×j=2n[tj1βI(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βI(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IAn+1=1αAB(α)tn+11βIA(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IAn+1=+α(Δt)αAB(α)Γ(α+1)IAn+1=×j=2ntj21βIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIAn+1=+α(Δt)αAB(α)Γ(α+2)IAn+1=×j=2n[tj11βIA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIAn+1=+α(Δt)α2AB(α)Γ(α+3)IAn+1=×j=2n[tj1βIA(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βIA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IDn+1=1αAB(α)tn+11βID(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IDn+1=+α(Δt)αAB(α)Γ(α+1)IDn+1=×j=2ntj21βID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIDn+1=+α(Δt)αAB(α)Γ(α+2)IDn+1=×j=2n[tj11βID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIDn+1=+α(Δt)α2AB(α)Γ(α+3)IDn+1=×j=2n[tj1βID(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IRn+1=1αAB(α)tn+11βIR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)+α(Δt)αAB(α)Γ(α+1)×j=2ntj21βIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+α(Δt)αAB(α)Γ(α+2)×j=2n[tj11βIR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+α(Δt)α2AB(α)Γ(α+3)×j=2n[tj1βIR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βIR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,ITn+1=1αAB(α)tn+11βIT(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAnIDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)ITn+1=+α(Δt)αAB(α)Γ(α+1)ITn+1=×j=2ntj21βIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠITn+1=+α(Δt)αAB(α)Γ(α+2)ITn+1=×j=2n[tj11βIT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣITn+1=+α(Δt)α2AB(α)Γ(α+3)ITn+1=×j=2n[tj1βIT(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj121βIT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Rn+1=1αAB(α)tn+11βR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Rn+1=+α(Δt)αAB(α)Γ(α+1)Rn+1=×j=2ntj21βR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠRn+1=+α(Δt)αAB(α)Γ(α+2)Rn+1=×j=2n[tj11βR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣRn+1=+α(Δt)α2AB(α)Γ(α+3)Rn+1=×j=2n[tj1βR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Dn+1=1αAB(α)tn+11βD(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Dn+1=+α(Δt)αAB(α)Γ(α+1)Dn+1=×j=2ntj21βD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠDn+1=+α(Δt)αAB(α)Γ(α+2)Dn+1=×j=2n[tj11βD(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣDn+1=+α(Δt)α2AB(α)Γ(α+3)Dn+1=×j=2n[tj1βD(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βD(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Vn+1=1αAB(α)tn+11βV(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Vn+1=+α(Δt)αAB(α)Γ(α+1)Vn+1=×j=2ntj21βV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠVn+1=+α(Δt)αAB(α)Γ(α+2)Vn+1=×j=2n[tj11βV(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣVn+1=+α(Δt)α2AB(α)Γ(α+3)Vn+1=×j=2n[tj1βV(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βV(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ. 134

For the power-law kernel, we can have the following:

Sn+1=(Δt)αΓ(α+1)×j=2ntj21βS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+(Δt)αΓ(α+2)×j=2n[tj11βS(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+(Δt)α2Γ(α+3)×j=2n[tj1βS(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βS(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βS(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,In+1=(Δt)αΓ(α+1)j=2ntj21βI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIn+1=+(Δt)αΓ(α+2)In+1=×j=2n[tj11βI(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIn+1=+(Δt)α2Γ(α+3)In+1=×j=2n[tj1βI(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βI(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βI(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IAn+1=(Δt)αΓ(α+1)j=2ntj21βIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIAn+1=+(Δt)αΓ(α+2)IAn+1=×j=2n[tj11βIA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIAn+1=+(Δt)α2Γ(α+3)IAn+1=×j=2n[tj1βIA(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βIA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βIA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IDn+1=(Δt)αΓ(α+1)j=2ntj21βID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIDn+1=+(Δt)αΓ(α+2)IDn+1=×j=2n[tj11βID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIDn+1=+(Δt)α2Γ(α+3)IDn+1=×j=2n[tj1βID(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IRn+1=(Δt)αΓ(α+1)j=2ntj21βIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+(Δt)αΓ(α+2)×j=2n[tj121βIR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+(Δt)α2Γ(α+3)×j=2n[tj1βIR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βIR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βIR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,ITn+1=(Δt)αΓ(α+1)j=2ntj21βIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠITn+1=+α(Δt)αΓ(α+2)ITn+1=×j=2n[tj11βIT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣITn+1=+(Δt)α2Γ(α+3)ITn+1=×j=2n[tj1βIT(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βIT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βIT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Rn+1=(Δt)αΓ(α+1)j=2ntj21βR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠRn+1=+(Δt)αΓ(α+2)Rn+1=×j=2n[tj11βR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣRn+1=+(Δt)α2Γ(α+3)Rn+1=×j=2n[tj1βR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Dn+1=(Δt)αΓ(α+1)j=2ntj21βD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×Π+(Δt)αΓ(α+2)×j=2n[tj11βD(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Σ+(Δt)α2Γ(α+3)×j=2n[tj1βD(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βD(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βD(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Vn+1=(Δt)αΓ(α+1)j=2ntj21βV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠVn+1=+(Δt)αΓ(α+2)Vn+1=×j=2n[tj11βV(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj21βV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣVn+1=+(Δt)α2Γ(α+3)Vn+1=×j=2n[tj1βV(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj11βV(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj21βV(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ. 135

Now we apply

Dtα,β(t)0FFES=S(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFEI=I(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFEIA=IA(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFEID=ID(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFEIR=IR(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFEIT=IT(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFER=R(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFED=D(t,S,I,IA,ID,IR,IT,R,D,V),Dtα,β(t)0FFEV=V(t,S,I,IA,ID,IR,IT,R,D,V). 136

After applying the fractional integral with exponential kernel and putting the Newton polynomial into these equations, we can solve our model as follows:

Sn+1=Sn+1αM(α)[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×S(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)S(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)] 137
Sn+1=+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×S(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×S(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×S(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},In+1=In+1αM(α)×[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×I(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)I(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×I(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×I(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×I(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IAn+1=IAn+1αM(α)[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×IA(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×IA(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]IAn+1=+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×IA(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×IA(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×IA(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IDn+1=IDn+1αM(α)×[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×ID(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×ID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×ID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×ID(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×ID(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},IRn+1=IRn+1αM(α)[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×IR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×IR(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]IRn+1=+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×ID(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×IR(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×IR(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},ITn+1=ITn+1αM(α)×[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×IT(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×IT(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×IT(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×IT(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×IT(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},Rn+1=Rn+1αM(α)Rn+1=×[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×R(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)R(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]Rn+1=+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×R(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×R(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×R(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},Dn+1=Dn+1αM(α)×[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×D(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×D(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×D(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×D(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×D(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt},Vn+1=Vn+1αM(α)Vn+1=×[tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)×V(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×V(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)]Vn+1=+αM(α){2312tn2β(tn)(β(tn+1)β(tn)Δtlntn+2β(tn)tn)×R(tn,Sn,In,IAn,IDn,IRn,ITn,Rn,Dn,Vn)Δt43tn12β(tn1)(β(tn)β(tn1)Δtlntn1+2β(tn1)tn1)×V(tn1,SnΔtSn,InΔtIn,IAnΔtIAn,IDnΔtIDn,IRnΔtIRn,ITnΔtITn,RnΔtRn,DnΔtDn,VnΔtVn)Δt+512tn22β(tn2)(β(tn1)β(tn2)Δtlntn2+2β(tn2)tn2)×V(tn2,SnΔtSnΔtS(n1),InΔtInΔtI(n1),IAnΔtIAnΔtIA(n1),IDnΔtIDnΔtID(n1),IRnΔtIRnΔtIR(n1),ITnΔtITnΔtIT(n1),RnΔtRnΔtR(n1),DnΔtDnΔtD(n1),VnΔtVnΔtV(n1))Δt}. 138

For the Atangana–Baleanu fractal-fractional derivative, we can have the following numerical scheme:

Sn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)Sn+1=×S(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Sn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Sn+1=×S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠSn+1=+α(Δt)αAB(α)Γ(α+2)Sn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣSn+1=+α(Δt)α2AB(α)Γ(α+3)Sn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×S(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,In+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)In+1=×I(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)In+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)In+1=×I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIn+1=+α(Δt)αAB(α)Γ(α+2)In+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIn+1=+α(Δt)α2AB(α)Γ(α+3)In+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×I(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IAn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)IAn+1=×IA(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IAn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)IAn+1=×IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIAn+1=+α(Δt)αAB(α)Γ(α+2)IAn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIAn+1=+α(Δt)α2AB(α)Γ(α+3)IAn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×IA(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IDn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)IDn+1=×ID(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IDn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)IDn+1=×ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIDn+1=+α(Δt)αAB(α)Γ(α+2)IDn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIDn+1=+α(Δt)α2AB(α)Γ(α+3)IDn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×ID(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,IRn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)IRn+1=×IR(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)IRn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)IRn+1=×IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIRn+1=+α(Δt)αAB(α)Γ(α+2)IRn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣIRn+1=+α(Δt)α2AB(α)Γ(α+3)IRn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×IR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,ITn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)ITn+1=×IT(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)ITn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)ITn+1=×IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠITn+1=+α(Δt)αAB(α)Γ(α+2)ITn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣITn+1=+α(Δt)α2AB(α)Γ(α+3)ITn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×IT(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Rn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)Rn+1=×R(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Rn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Rn+1=×R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠRn+1=+α(Δt)αAB(α)Γ(α+2)Rn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣRn+1=+α(Δt)α2AB(α)Γ(α+3)Rn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×R(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Dn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)Dn+1=×D(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Dn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Dn+1=×D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠDn+1=+α(Δt)αAB(α)Γ(α+2)Dn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣDn+1=+α(Δt)α2AB(α)Γ(α+3)Dn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×D(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ,Vn+1=1αAB(α)tn+12β(tn+1)(β(tn+2)β(tn+1)Δtlntn+1+2β(tn+1)tn+1)Vn+1=×V(tn+1,Sn+ΔtSn,In+ΔtIn,IAn+ΔtIAn,IDn+ΔtIDn,IRn+ΔtIRn,ITn+ΔtITn,Rn+ΔtRn,Dn+ΔtDn,Vn+ΔtVn)Vn+1=+α(Δt)αAB(α)Γ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Vn+1=×V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠVn+1=+α(Δt)αAB(α)Γ(α+2)Vn+1=×j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×ΣVn+1=+α(Δt)α2AB(α)Γ(α+3)Vn+1=×j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×V(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]×Δ. 139

For the power-law kernel, we can have the following:

Sn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Sn+1=×S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠSn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Sn+1=×ΣSn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×S(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×S(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×S(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Sn+1=×Δ,In+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)In+1=×I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]In+1=×ΣIn+1=+α(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×I(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×I(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×I(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]In+1=×Δ,IAn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)IAn+1=×IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIAn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IAn+1=×ΣIAn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×IA(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IA(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IA(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IAn+1=×Δ,IDn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)IDn+1=×ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIDn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IDn+1=×ΣIDn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×ID(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×ID(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×ID(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IDn+1=×Δ,IRn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)IRn+1=×IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠIRn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IRn+1=×ΣIRn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×IR(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IR(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IR(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]IRn+1=×Δ,ITn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)ITn+1=×IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠITn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]ITn+1=×ΣITn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×IT(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×IT(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×IT(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]ITn+1=×Δ,Rn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Rn+1=×R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠRn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Rn+1=×ΣRn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×R(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×R(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×R(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Rn+1=×Δ,Dn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Dn+1=×D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠDn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Dn+1=×ΣDn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×D(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×D(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×D(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Dn+1=×Δ,Vn+1=(Δt)αΓ(α+1)j=2ntj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)Vn+1=×V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))×ΠVn+1=+(Δt)αΓ(α+2)j=2n[tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Vn+1=×ΣVn+1=+(Δt)α2Γ(α+3)j=2n[tj2β(tj)(β(tj+1)β(tj)Δtlntj+2β(tj)tj)×V(tj,Sj,Ij,IAj,IDj,IRj,ITj,Rj,Dj,Vj)2tj12β(tj1)(β(tj)β(tj1)Δtlntj1+2β(tj1)tj1)×V(tj1,SjΔtSj,IjΔtIj,IAjΔtIAj,IDjΔtIDj,IRjΔtIRj,ITjΔtITj,RjΔtRj,DjΔtDj,VjΔtVj)+tj22β(tj2)(β(tj1)β(tj2)Δtlntj2+2β(tj2)tj2)×V(tj2,SjΔtSjΔtS(j1),IjΔtIjΔtI(j1),IAjΔtIAjΔtIA(j1),IDjΔtIDjΔtID(j1),IRjΔtIRjΔtIR(j1),ITjΔtITjΔtIT(j1),RjΔtRjΔtR(j1),DjΔtDjΔtD(j1),VjΔtVjΔtV(j1))]Vn+1=×Δ. 140

Numerical simulation

In this section, using the numerical solutions obtained in the previous section, we present a numerical method for all cases. The numerical simulations are depicted for different values of fractional order and fractal dimension as presented in Figs. 2637.

Dtα,β0FFMS=Λ(δ(t)(αI+wβID+γwIA+wδ1IR+wδ2IT)+γ1+μ1)S,Dtα,β0FFMI=(δ(t)(αI+wβID+γwIA+wδ1IR+wδ2IT))S(ε+ξ+λ+μ1)I,Dtα,β0FFMIA=ξI(θ+μ+χ+μ1)IA,Dtα,β0FFMID=εI(η+φ+μ1)ID,Dtα,β0FFMIR=ηID+θIA(v+ξ+μ1)IR,Dtα,τ0FFMIT=μIA+vIR(σ+τ+μ1)IT,Dtα,τ0FFMR=λI+φID+χIA+ξIR+σIT(Φ+μ1)R,Dtα,τ0FFMD=τIT,Dtα,τ0FFMV=γ1S+ΦRμ1V, 141

where

δ(t)={d0(1an)cos(btt0T),0<t<t0d0,t0<t<t1d1(1ar)cos(btt1T),t1<t<t2d1,t2<t<t3d2(1at)cos(btt2T),t>t3}. 142

Also, the initial conditions are

S(0)=800,000,I(0)=3,IA(0)=0,ID(0)=0,IR(0)=0,IT(0)=0,R(0)=0,D(0)=0,V(0)=0. 143

Also, the parameters are chosen as follows:

Λ=810,000,η=0.12,χ=0.15,v=0.4,γ=0.09,β=0.75,γ1=0.4,μ1=0.3,ε=0.161,τ=0.0199,Φ=0.015,λ=0.0345,φ=0.0345,δ1=0.5,ξ=0.015,σ=0.015,δ0=0.99,Δt=900,t0=30,δ2=0.4,w=0.4,b=0.2,an=0.1,ar=0.2,at=0.3,d0=0.02,d1=0.2,d2=0.15. 144

Figure 27.

Figure 27

Numerical visualization of COVID-19 model for α=0.85

Figure 28.

Figure 28

Numerical visualization of COVID-19 model for α=0.91

Figure 29.

Figure 29

Numerical visualization of COVID-19 model for α=0.76

Figure 30.

Figure 30

Numerical visualization of COVID-19 model for α=0.90, β=0.85

Figure 31.

Figure 31

Numerical visualization of COVID-19 model for α=0.95, β=0.95

Figure 32.

Figure 32

Numerical visualization of COVID-19 model for α=0.72

Figure 33.

Figure 33

Numerical visualization of COVID-19 model for α=0.82

Figure 34.

Figure 34

Numerical visualization of COVID-19 model for α=0.90

Figure 35.

Figure 35

Numerical visualization of COVID-19 model for α=1

Figure 36.

Figure 36

Numerical visualization of COVID-19 model for α=0.89, β=0.85

Figure 26.

Figure 26

Numerical visualization of COVID-19 model for α=0.76

Figure 37.

Figure 37

Numerical visualization of COVID-19 model for α=0.95, β=0.97

Likelihood with hyper-Poisson distribution

Using the suggested numerical model, we obtain the approximate solution (S(t),I(t),IA(t),ID(t),IR(t),IT(t),R(t),D(t),V(t)). We are more interested in I(t), R(t), and D(t) and the approximate solution I, R, D because we have the collected data zIt, zRt, zDt which represent the number of infections, recovered, and deaths daily. We assume that such follow hyper-Poisson distribution with parameters. The hyper-Poisson distribution is given as follows:

P(X=k)=Γ(β)Γ(k+β)Φ(1,β,λ),λ>0,k=0,1,2,,n, 145

where

Φ(1,β,λ)=k=0(1)kλk(β)kk!,(β)k=β(β+1)(β+k) 146

Ω with parameters k1, k2, k3

k1=Ω1I(t),k2=Ω2R(t),k3=Ω3D(t) 147

and

zItHP(k1=Ω1I(t)),zRtHP(k2=Ω2R(t)),zDtHP(k3=Ω3D(t)). 148

Here, the parameters Ω1, Ω2, and Ω3 are a combination of collection accuracy and detectability of infected, recovered, and dead. Thus the likelihood function is defined as follows:

L(k1)=t=0ng(zIt/k1),L(k2)=t=0ng(zRt/k2),L(k3)=t=0ng(zDt/k3). 149

Thus

L(k1)=t=0nΓ(β)λzItΓ(zIt+β)Φ(1,β,λ),L(k2)=t=0nΓ(β)λzRtΓ(zRt+β)Φ(1,β,λ),L(k3)=t=0nΓ(β)λzDtΓ(zDt+β)Φ(1,β,λ). 150

Without loss of generality, we consider L(k1):

logL(k1)=t=0nlogΓ(β)λzItΓ(zIt+β)Φ(1,β,λ)=t=0n[logΓ(β)+zItlog(Ω1I)logΓ(zIt+β)logΦ(1,β,Ω1I)] 151

and

logL(k1)zIt=t=0nlog(Ω1)+t=0nlog(I)t=0n(Γ(zIt+β))Γ(zIt+β)=n[log(Ω1)+log(I)]t=0n(Γ(zIt+β))Γ(zIt+β)=nlog(Ω1I)t=0n(Γ(zIt+β))Γ(zIt+β), 152
logL(k1)I=nzItIIt=0nΦ(1,β,Ω1I)Φ(1,β,Ω1I)=nzItIInΦ(1,β,Ω1I)Φ(1,β,Ω1I), 153
logL(k1)Ω1=nzItΩ1Ω1nΦ(1,β,Ω1I)Φ(1,β,Ω1I)=nΦ(1,β,Ω1I)Φ(1,β,Ω1I), 154
L(k2)=t=0nlogΓ(β)λzRtΓ(zRt+β)Φ(1,β,λ)=t=0n[logΓ(β)+zRtlogλlogΓ(zRt+β)logΦ(1,β,λ)], 155
logL(k2)zRt=t=0nlog(Ω2)+t=0nlog(R)t=0n(Γ(zRt+β))Γ(zRt+β)=n[log(Ω2)+log(R)]t=0n(Γ(zRt+β))Γ(zRt+β)=nlog(Ω2R)t=0n(Γ(zRt+β))Γ(zRt+β), 156
logL(k2)R=nzRtRRt=0nΦ(1,β,Ω2R)Φ(1,β,Ω2R)=nzRtRRnΦ(1,β,Ω2R)Φ(1,β,Ω2R), 157
logL(k2)Ω2=nzRtΩ2Ω2nΦ(1,β,Ω2R)Φ(1,β,Ω2R)=nΦ(1,β,Ω2R)Φ(1,β,Ω2R), 158
L(k3)=t=0nlogΓ(β)λzDtΓ(zDt+β)Φ(1,β,λ)=t=0n[logΓ(β)+zDtlogλlogΓ(zDt+β)logΦ(1,β,λ)], 159
logL(k3)zDt=t=0nlog(Ω3)+t=0nlog(D)t=0n(Γ(zDt+β))Γ(zDt+β)=n[log(Ω3)+log(D)]t=0n(Γ(zDt+β))Γ(zDt+β)=nlog(Ω3D)t=0n(Γ(zDt+β))Γ(zDt+β), 160
logL(k3)R=nzDtDDt=0nΦ(1,β,Ω3D)Φ(1,β,Ω3D)=nzDtDDnΦ(1,β,Ω3D)Φ(1,β,Ω3D), 161
logL(k3)Ω3=nzDtΩ3Ω3nΦ(1,β,Ω3D)Φ(1,β,Ω3D)=nΦ(1,β,Ω3D)Φ(1,β,Ω3D). 162

Likelihood with Weibull distribution

We will do the same routine for the Weibull distribution known as

P(X=k)=kα(λα)k1exp(λ/α)k,λ,α>0,k=0,1,2,,n, 163

Ω with parameters k1, k2, k3

k1=Ω1I(t),k2=Ω2R(t),k3=Ω3D(t) 164

and

εItW(k1=Ω1I(t)),εRtW(k2=Ω2R(t)),εDtW(k3=Ω3D(t)). 165

Thus the likelihood function is given by

L(k1)=t=0nW(εIt/k1),L(k2)=t=0nW(εRt/k2),L(k3)=t=0nW(εDt/k3). 166

Thus

L(k1)=t=0nεItα(λα)εIt1exp(λ/α)εIt,L(k2)=t=0nεRtα(λα)εRt1exp(λ/α)εRt,L(k3)=t=0nεDtα(λα)εDt1exp(λ/α)εDt. 167

Without loss of generality, we consider L(k1):

logL(k1)=t=0nlog[εItα(Ω1Iα)εIt1exp(Ω1I/α)εIt]=[logεItlogα+(εIt1)[log(Ω1I)logα]εIt(Ω1I/α)] 168

and

logL(k1)εIt=t=0nεItεIt+t=0n[log(Ω1I)logα]t=0n(Ω1I/α)=nεItεIt+n[log(Ω1I)logα]+n(Ω1I/α), 169
logL(k1)I=n(εIt1)IIt=0n(Ω1I/α)(Ω1I/α)=n(εIt1)IIn(Ω1I/α)(Ω1I/α), 170
logL(k1)Ω1=n(εIt1)Ω1Ω1n(Ω1I/α)(Ω1I/α)=n(Ω1I/α)(Ω1I/α), 171
logL(k2)=t=0nlog[εRtα(Ω2Iα)εRt1exp(Ω2R/α)εIt]=[logεRtlogα+(εRt1)[log(Ω2R)logα]εRt(Ω2R/α)] 172

and

logL(k2)εRt=t=0nεRtεRt+t=0n[log(Ω2R)logα]t=0n(Ω2R/α)=nεRtεRt+n[log(Ω2R)logα]+n(Ω2R/α), 173
logL(k2)R=n(εRt1)RRt=0n(Ω2R/α)(Ω2R/α)=n(εRt1)RRn(Ω2R/α)(Ω2R/α), 174
logL(k2)Ω2=n(εRt1)Ω2Ω2n(Ω2R/α)(Ω2R/α)=n(Ω2R/α)(Ω2R/α), 175
logL(k3)=t=0nlog[εDtα(Ω3Dα)εDt1exp(Ω3D/α)εDt]=[logεDtlogα+(εDt1)[log(Ω3D)logα]εDt(Ω3D/α)] 176

and

logL(k3)εDt=t=0nεDtεDt+t=0n[log(Ω3D)logα]t=0n(Ω3D/α)=nεDtεDt+n[log(Ω3R)logα]+n(Ω3D/α), 177
logL(k3)D=n(εDt1)DDt=0n(Ω3D/α)(Ω3D/α)=n(εIt1)DDn(Ω3D/α)(Ω3D/α), 178
logL(k1)Ω1=n(εDt1)Ω3Ω3n(Ω3D/α)(Ω3D/α)=n(Ω3D/α)(Ω3D/α). 179

Likelihood with Mittag-Leffler distribution

Finally, we shall use the Mittag-Leffler distribution for similar processes. The Mittag-Leffler distribution is defined by

P(X=k)=λkΓ(αk+β)Eα,β(λ),λ>0,k=0,1,2,,n, 180

where

Eα,β(λ)=k=0λkΓ(αk+β). 181

Ωi, i=1,2,3 with parameters k1, k2, k3

k1=Ω1I(t),k2=Ω2R(t),k3=Ω3D(t) 182

and

εItML(k1=Ω1I(t)),εRtML(k2=Ω2R(t)),εDtML(k3=Ω3D(t)). 183

Thus the likelihood function is written as

L(k1)=t=0nML(εIt/k1),L(k2)=t=0nML(εRt/k2),L(k3)=t=0nML(εDt/k3). 184

Thus

L(k1)=t=0nλεItΓ(αεIt+β)Eα,β(λ),L(k2)=t=0nλεRtΓ(αεRt+β)Eα,β(λ),L(k3)=t=0nλεDtΓ(αεDt+β)Eα,β(λ). 185

We write L(k1):

logL(k1)=logλεItΓ(αεIt+β)Eα,β(λ)=t=0n[εItlog(Ω1I)logΓ(αεIt+β)logEα,β(Ω1I)] 186

and

logL(k1)εIt=t=0nlog(Ω1)+t=0nlog(I)t=0n(Γ(αεIt+β))Γ(αεIt+β)=n[log(Ω1)+log(I)]t=0n(Γ(αεIt+β))Γ(αεIt+β)=nlog(Ω1I)t=0n(Γ(αεIt+β))Γ(αεIt+β), 187
logL(k1)I=nεItIIt=0nEα,β(Ω1I)Eα,β(Ω1I)=nεItIInEα,β(Ω1I)Eα,β(Ω1I), 188
logL(k1)Ω1=nεItΩ1Ω1nEα,β(Ω1I)Eα,β(Ω1I)=nEα,β(Ω1I)Eα,β(Ω1I). 189

With the same routine,

logL(k2)=t=0nlogλεRtΓ(αεIt+β)Eα,β(λ)=t=0n[εRtlog(Ω1R)logΓ(αεRt+β)logEα,β(Ω2R)] 190

and

logL(k2)εRt=t=0nlog(Ω2)+t=0nlog(R)t=0n(Γ(αεRt+β))Γ(αεRt+β)=n[log(Ω2)+log(R)]t=0n(Γ(αεRt+β))Γ(αεRt+β)=nlog(Ω2R)t=0n(Γ(αεRt+β))Γ(αεRt+β), 191
logL(k2)R=nεRtRRt=0nEα,β(Ω2R)Eα,β(Ω2R)=nεItRRnEα,β(Ω2R)Eα,β(Ω2R), 192
logL(k2)Ω2=nεRtΩ2Ω2nEα,β(Ω2R)Eα,β(Ω2R)=nEα,β(Ω2R)Eα,β(Ω2R) 193

and

logL(k3)=t=0nlogλεDtΓ(αεDt+β)Eα,β(λ)=t=0n[εDtlog(Ω3D)logΓ(αεDt+β)logEα,β(Ω3D)] 194

and

logL(k1)εIt=t=0nlog(Ω3)+t=0nlog(D)t=0n(Γ(αεDt+β))Γ(αεDt+β)=n[log(Ω3)+log(D)]t=0n(Γ(αεDt+β))Γ(αεDt+β)=nlog(Ω3D)t=0n(Γ(αεDt+β))Γ(αεDt+β), 195
logL(k3)D=nεDtDDt=0nEα,β(Ω3D)Eα,β(Ω3D)=nεDtDDnEα,β(Ω3D)Eα,β(Ω3D), 196
logL(k3)Ω3=nεDtΩ1Ω1nEα,β(Ω3D)Eα,β(Ω3D)=nEα,β(Ω3D)Eα,β(Ω3D). 197

Conclusion

Up to date humans have relied on forecasting with the aim to better control their world, or at least to have an asymptotic idea of their future. They have many ways to achieve this, one way is to use the deterministic approach and another is stochastic one. In this work, we presented a comprehensive analysis ranging from stochastic, fractal to differentiation with the aim to predict the future behavior of COVID-19 with cases studied in Africa and Europe. With stochastic approach, we were able to detect a possibility of the second wave of COVID-19 spread in Europe and in Africa, a continuous exponential growth could be possible. We presented an extension of the blancmange function to capture more fractal behaviors, and some examples were presented resembling the COVID-19 spread in various countries in Africa and Europe. A complex and nonlinear mathematical model with wave function was considered and solved numerically with a modified scheme.

Acknowledgements

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

Authors’ contributions

All authors have contributed equally in this work. All authors read and approved the final manuscript.

Funding

There is no funding for this paper.

Availability of data and materials

There are no data for this paper.

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Abdon Atangana, Email: AtanganaA@ufs.ac.za.

Seda İğret Araz, Email: sedaaraz@siirt.edu.tr.

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