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Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2021 May 12;148:111030. doi: 10.1016/j.chaos.2021.111030

A fractional order Covid-19 epidemic model with Mittag-Leffler kernel

Hasib Khan a, Muhammad Ibrahim a, Abdel-Haleem Abdel-Aty b,c,, M Motawi Khashan d, Farhat Ali Khan f, Aziz Khan e
PMCID: PMC8114791  PMID: 34002105

Abstract

In this article, we are studying fractional-order COVID-19 model for the analytical and computational aspects. The model consists of five compartments including; ``Sc which denotes susceptible class, ``Ec represents exposed population, ``Ic is the class for infected people who have been developed with COVID-19 and can cause spread in the population. The recovered class is denoted by ``Rc and ``Vc is the concentration of COVID-19 virus in the area. The computational study shows us that the spread will be continued for long time and the recovery reduces the infection rate. The numerical scheme is based on the Lagrange’s interpolation polynomial and the numerical results for the suggested model are similar to the integer order which gives us the applicability of the numerical scheme and effectiveness of the fractional order derivative.

MSC: Primary 26A33, Secondary 34A08, 35R11

1. Introduction

In December 2019, Chinese wellbeing specialists have reported a group of pneumonia case in Wuhan city in Hubei area, China. The microbe that is liable for the viral pneumonia in influenced patients is the recently recognized Covid (SARS-Cov-2) [1]. On the 3rd March 2020, 80151 cases have been identified and affirmed in China. Kong et al. [2] All around the world, above 10,566 further cases were identified in 72 nations. Organization [3] Among the patients, the symptoms of the Covid infection include as fever, hack, breathing challenges. To reduce the risk of spread of the Covid among a population, several control arrangements and vital activities are being done at various stages. On January 23, Wuhan city was closed for transportation and other gatherings, celebrations [4]. Individuals were to wear masks in open regions. Schools, colleges, universities were also closed and most of the educational systems were transferred on the online systems to reduce the risk of infection [5], [6], [7]. Thousands of people have lost their lives and more than 81600 new cases were reported in the different areas of the China10. This has now caused a pandemic situation round the world and researchers are working on the reduction of the spread of the infection.

Mathematical modeling is the most influential instrument for its analyzing, guessing the spreading forces and formulation of control strategies for Covid-19. In order to recognize the means to control infections in the people have been studied in various articles [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24]. Recently, Ullah et al. [10] examined a tuberculosis model for the existence and stability as the numerical results were also formulated. Okuonghae [11] studied a Covid-19 model. Based on these results, we study a fractional order Covid-19 model in ABC-sense. For the detail, the readers can get benefit from Khan et al. [12], Owolabi and Atangana [13].

0ABRDtκ1*Sc(t)=cβEcScEcβIcScIcβVcScVcμcSc0ABRDtκ2*Ec(t)=βEcScEc+βIcScIcβVcScVc(ζc*+μc)Ec0ABRDtκ3*Ic(t)=ζc*Ec(ωc+γc+μc)Ic0ABRDtκ4*Rc(t)=γcIcμcRc0ABRDtκ5*Vc(t)=ψ1cEc+ψ2cIcτcVc. (1)

The total population is divided in five classes including; the susceptible class denoted with “Sc”, “Ec” is the exposed portion of the population, the infected individuals are given in “Ic”, The recovered people are “Rc”. “Vc” is the concentration of the COVID-19 virus in the area. In the parameters “c” recruitment rate, “μc” is the rate of natural death. The quarantine period of the patients is ``(ζc)1”, “γc is the recovery rate and “ψ1c”, “ψ2c” are rates of the spread of the corona virus, “ωc” is the death rate due to Covid-19 and “τc” removal of the COVID-19 virus from the area, the human to human transmission of exposed into susceptible is given by “βEc”. The rate of transmission from infected into susceptible is “βIc”. The transmission of the disease due to the environmental contact of humanis “βVc”. All the “βEc”, “βIc” and “βVc” are non-negative and non-increasing. Our aims include the analytical and computational study of the COVID-19 model fractional order (1) for the existence results and numerical simulations. The model is analyzed for the fractional orders κi*=κ=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6. A computational scheme is derived for the numerical simulation based on the Lagrange’s interpolation polynomial and is tested for the available data given in [14].

Definition 1.1

For ψ0H*(a,b),b>a, for κ1*[0,1], the ABC-fractional derivative is

ABCaDτκ*1ψ0(τ)=B(κ*1)1κ*1aτψ0(s)Eκ*1[κ*1(τs)κ1*1κ*1]ds, (2)

where B(κ*1) is satisfying B(0)=B(1)=1.

Definition 1.2

Let ψ0H*(a,b),b>a,κ*1[0,1], the ABR-fractional derivative is:

ABRaDτκ*1ψ0(τ)=B(κ*1)1κ*1ddτaτψ0(s)Eκ*1[κ*1(τs)1κ*1κ*1]ds. (3)

Definition 1.3

The AB-integral of ψH*(a,b),b>a,0<κ*1<1 is given by

ABaIτκ*1ψ(τ)=1κ*1B(κ*1)ψ(τ)+κ*1B(κ*1)Γ(κ*1)aτψ(s)(τs)κ*11ds. (4)

Lemma 1.4

Forψ0H(a,b), the following Newton-Leibniz formula is satisfied:

ABaIτκ*1(ABCaDτκ*1ψ0(τ))=ψ0(τ)ψ0(a). (5)

2. Existence criteria

By the help of Lemma 1.4 and (1), we have

Sc(t)Sc(0)=1κ*1B(κ*1)[cβEcScEcβIcScIcβVcScVcμcSc,]+κ*1B(κ*1)Γκ*10t(ts)κ*11[cβEcScEcβIcScIcβVcScVcμcSc,]dsEc(t)Ec(0)=1κ*2B(κ*2)[βEcScEc+βIcScIcβVcScVc(ζc*+μc)Ec]+κ*1B(κ*2)Γκ*20t(ts)κ*21[βEcScEc+βIcScIcβVcScVc(ζc*+μc)Ec]dsIc(t)Ic(0)=1κ*3B(κ*3)[ζc*Ec(ωc+ζc+μc)Ic]+κ*1B(κ*3)Γκ*30t(ts)κ*31[ζc*Ec(ωc+ζc+μc)Ic]dsRc(t)Rc(0)=1κ*4B(κ*4)[ζcIcμcRc]+κ*4B(κ*4)(Γκ*4)0t(ts)κ*41[ζcIcμcRc]dsVc(t)Vc(0)=1κ*5B(κ*5)[ψ1cEc+ψ2cIcτcVc]+κ*5B(κ*5)(Γκ*5)0t(ts)κ*51[ψ1cEc+ψ2cIcτcVc]ds (6)

Assume the functions Yi for i=1,2,3,4,5 given below:

Y1(t,Sc)=cβEcScEcβIcScIcβVcScVcμcSc, (7)
Y2(t,Ec)=βEcScEc+βIcScIcβVcScVc(ζc*+μc)Ec, (8)
Y3(t,Ic)=ζc*Ec(ωc+ζc+μc)Ic, (9)
Y4(t,Rc)=ζcIcμcRc, (10)
Y5(t,Vc)=ψ1cEc+ψ2cIcτcVc. (11)
{ψ1=βEcc1+βIcc2+βVcc3+μcψ2=βEcb1+(ζc+μc),ψ3=ωc+ζc+μc,ψ4=μcψ5=τc. (12)
  • (B) Let, for Sc(t),Sc*(t),Ec(t),Ec*(t),Ic(t),Ic*(t),Rc(t),Rc*(t),Vc(t),Vc*(t).L[0,1] there exists constants κi>0, for i=1,2,3,4, such that Scκ1,Ecκ2,Icκ3,Rcκ4,Vcκ5, and ξ1,ξ2>0, and
    Sc+Ic+Vcξ1, (13)
    Vc+Ec(t)ξ2. (14)

Theorem 2.1

TheYi, foriN15satisfy Lipschitz condition provided thatB)is obeyed.

Consider for Y1, below

Y1(Sc)Y1(Sc*)=cβEcScEcβIcScIcβVcScVcμcSc,(cβEcScEcβIcScIcβVcSc*VcμcSc*),βEcc1+βIcc2+βVcc3+μcScSc*[βEcc1+βIcc2+βVcc3+μc]ScSc*=ψ1ScSc*. (15)

For the Y2(t,Ec), we have

Y2(Ec)Y2(Ec*)=(βEcScEc+βIcScIcβVcScVc(ζc*+μc)Ec)(βEc*ScEc*+βIcScIcβVcScVc(ζc*+μc)Ec*)[βEcb1+(ζc+μc)]EcEc*=ψ2EcEc*. (16)

The Y3(t,Ic*) implies

Y3(Ic)Y3(Ic*)=(ζc*Ec(ωc+ζc+μc)Ic)(ζc*Ec(ωc+ζc+μc)Ic*)[(ωc+ζc+μc)]IcIc*=ψ3IcIc*. (17)

for Y4(t,I), we have

Y4(Rc)Y4(Rc*)=(ζcIcμcRc)(ζcIcμcRc*)μcRcRc*[μc]RcRc*=ψ4RcRc*. (18)

for Y5(t,R1), we have

Y5(Vc)Y5(Vc*)=(ψ1cEc+ψ2cIcτcVc)(ψ1cEc+ψ2cIcτcVc)τcVcVc*=ψ5VcVc*. (19)

With the help of (15) to (19), the Yi satisfy Lipschitz condition for i=1,2,3,4,5.

Let Sc(0)=Ec(0)=Ic(0)=Rc(0)=0=Vc(0)=0, then we have

Sc(t)=1κ*1B(κ*1)Y1(t,Sc(t))+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc(s))ds. (20)
Ec(t)=1κ*2B(κ*2)Y2(t,Ec(t))+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ec(s))ds, (21)
Ic(t)=1κ*3B(κ*3)Y3(t,Ic(t))+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Ic(s))ds, (22)
Rc(t)=1κ*4B(κ*4)Y4(t,Rc(t))+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rc(s))ds. (23)
Vc(t)=1κ*5B(κ*5)Y5(t,Vc(t))+κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vc(s))ds. (24)

We define the following iterative relations for (1):

Scn(t)=1κ*1B(κ*1)Y1(t,Scn1(t))+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Scn1(s))ds,
Ecn(t)=1κ*2B(κ*2)Y2(t,Ecn1(t))+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ecn1(s))ds,
Icn(t)=1κ*3B(κ*3)Y3(t,Icn1(t))+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Icn1(s))ds.
Rcn(t)=1κ*4B(κ*4)Y4(t,Rcn1(t))+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rcn1(s))ds,
Vcn(t)=1κ*5B(κ*5)Y5(t,Vcn1(t))+κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vcn1(s))ds,

Theorem 2.2

With the assumptionB, the model(1)has a solution subjected to:

Δ=max{Ψi}<1,iforiN15. (25)

We define the function

K1n(t)=Scn+1(t)Sc(t),K2n(t)=Ecn+1(t)Ec(t),K3n(t)=Icn+1(t)Ic(t),K4n(t)=Rcn+1(t)Rc(t),K5n(t)=Vcn+1(t)Vc(t). (26)

Then using equations (3) to (26), we find that

K1n1κ*1B(κ*1)Y1(Scn)Y1(Scn1)+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Scn(s))Y1(s,Scn1(t))ds[1κ*1B(κ*1)+1B(κ*1)Γ(κ*1)]ψ1ScnSc[1κ*1B(κ*1)+1B(κ*1)Γ(κ*1)]nΔnSc1Sc. (27)

And

K2n1κ*2B(κ*2)Y2(EcnY2(Ecn1)+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ecn(s))Y2(s,Ecn1(t))ds[1κ*2B(κ*2)+1B(κ*2)Γ(κ*2)]ψ2EcnEc[1κ*2B(κ*2)+1B(κ*2)Γ(κ*2)]nΔnEc1Ec. (28)

Similarly,

K3n1κ*3B(κ*3)Y3(Icn)Y3(Icn1)+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Icn(s))Y3(s,Icn1(t))ds[1κ*3B(κ*3)+1B(κ*1)Γ(κ*3)]ψ3IcnIc[1κ*3B(κ*3)+1B(κ*1)Γ(κ*3)]nΔnIc1Ic, (29)
K4n1κ*4B(κ*4)Y4(Rcn)Y4(Rcn1)+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rcn(s))Y4(s,Rcn1(t))ds[1κ*4B(κ*4)+1B(κ*4)Γ(κ*4)]ψ4RcnRc[1κ*4B(κ*4)+1B(κ*4)Γ(κ*4)]nΔnRc1Rc, (30)
K5n1κ*5B(κ*5)Y5(Vcn(t))Y5(Vvn1(t))+κ*5B(κ*1)Γ(κ*5)0t(ts)κ*51Y5(s,Vcn(s))Y5(s,Vcn1)ds[1κ*5B(κ*5)+1B(κ*5)Γ(κ*5)]ψ5VcnVc[1κ*5B(κ*5)+1B(κ*5)Γ(κ*5)]nΔnVc1Vc. (31)

Thus, we have K(t)n0,iN15, as n for Δ<1, which is the required proof.

3. Uniqueness solution

Theorem 3.1

With the assumption B, the fractional order model (1) has unique solution provided that

[1φiB(φi)+1B(φi)Γ(φi)]ψi1,iN15. (32)

Let us assume that {Sc¯(t),Ec¯(t),Ic¯(t),Rc¯(t),Vc¯(t)} another solution of (1), such that

Sc¯(t)=1κ*1B(κ*1)Y1(t,Sc¯(t))+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc¯(s))ds, (33)
Ec¯(t)=1κ*2B(κ*2)Y2(t,Ec¯(t))+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ec¯(s))ds, (34)
Ic¯(t)=1κ*3B(κ*3)Y3(t,Ic¯(t))+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Ic¯(s))ds, (35)
Rc¯(t)=1κ*4B(κ*4)Y4(t,Rc¯(t))+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rc¯(s))ds, (36)
Vc¯(t)=1κ*5B(κ*5)Y5(t,Vc¯(t))+κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vc¯(s))ds, (37)

Then,

ScSc¯1κ*1B(κ*1)Y1(t,Sc(t))Y1(t,Sc¯(t))+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc(s))Y1(t,Sc¯(t))ds[1κ*1B(κ*1)+1B(κ*1)Γ(κ*1)]ψ1ScSc¯,

which implies

[1κ*1B(κ*1)ψ1+ψ1B(κ*1)Γ(κ*1)1]ScSc¯0. (38)

By (32), the (38) is valid for ScSc¯=0, which means Sc(t)=Sc¯(t). Similarly, we have

EcEc¯1κ*2B(κ*2)Y2(t,Ec(t))Y2(t,Ec¯(t))+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ec(s))Y2(t,Ec¯(t))ds[1κ*2B(κ*2)+1B(κ*2)Γ(κ*2)]ψ2EcEc¯,
[1κ*2B(κ*2)ψ2+ψ2B(κ*2)Γ(κ*2)1]EcEc¯0. (39)

which implies By (32), the (39) is valid for EcEc¯=0, which means Ec(t)=Ec¯(t). Now, for Lf, we have

IcIc¯1κ*3B(κ*3)Y3(t,Ic(t))Y3(t,Ic¯(t))+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Ic(s))Y3(t,Ic¯(t))ds[1κ*3B(κ*3)+1B(κ*3)Γ(κ*3)]ψ3IcIc¯,

which implies

[1κ*3B(κ*3)ψ3+ψ3B(κ*3)Γ(κ*3)1]IcIc¯0. (40)

which implies By (32), the (40) is valid for IcIc¯=0, which means Ic(t)=Ic¯(t).

RcRc¯1κ*4B(κ*4)Y4(t,Rc(t))Y4(t,Rc¯(t))+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rc(s))Y4(t,Rc¯(t))ds[1κ*4B(κ*4)+1B(κ*4)Γ(κ*4)]ψ4RcRc¯,

this implies

[1κ*4B(κ*4)ψ4+ψ4B(κ*4)Γ(κ*4)1]RcRc¯0. (41)

By (32), the (41) is valid for RcRc¯=0, which means Rc(t)=Rc¯(t). Now, for R1, we have

VcVc¯1κ*5B(κ*5)Y5(t,Vc(t))Y5(t,Vc¯(t))+κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vc(s))Y5(t,Vc¯(t))ds[1κ*5B(κ*5)+1B(κ*5)Γ(κ*5)]ψ5VcVc¯,

which implies

[1κ*5B(κ*5)ψ5+ψ5B(κ*5)Γ(κ*5)1]VcVc¯0. (42)

which implies By (32), the (42) is true ifVcVc¯=0, which implies Vc(t)=Vc¯(t). Thus the (1) has unique solution.

4. Hyers Ulams stability

Definition 4.1

The system of integral Eqs. (20)(24) is Hyers-Ulam-stable if for δi>0,iN15 and ζi>0,iN15, with

|Sc(t)1κ*1B(κ*1)Y1(t,Sc(t))κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc(s))ds|ζ1, (43)
|Ec(t)1κ*2B(κ*2)Y2(t,Ec(t))κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ec(s))ds|ζ2, (44)
|Ic(t)1κ*3B(κ*3)Y3(t,Ic(t))κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Ic(s))ds|ζ3, (45)
|Rc(t)1κ*4B(κ*4)Y4(t,Rc(t))κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rc(s))ds|ζ4, (46)
|Vc(t)1κ*5B(κ*5)Y5(t,Vc(t))κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vc(s))ds|ζ5, (47)

We have Sc˙(t),Ec˙(t),Ic˙(t),Rc˙(t),Vc˙(t) which implies

Sc˙(t)=1κ*1B(κ*1)Y1(t,Sc˙(t))+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc˙(s))ds, (48)
Ec˙(t)=1κ*2B(κ*2)Y2(t,Ec˙(t))+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ec˙(s))ds, (49)
Ic˙(t)=1κ*3B(κ*3)Y3(t,Ic˙(t))+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Ic˙(s))ds, (50)
Rc˙(t)=1κ*4B(κ*4)Y4(t,Rc˙(t))+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rc˙(s))ds, (51)
Vc˙(t)=1κ*5B(κ*5)Y5(t,Vc˙(t))+κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vc˙(s))ds, (52)

Such that

|Sc(t)Sc˙(t)|δ1ζ1,|Ec(t)Ec˙(t)|δ2ζ2,|Ic(t)Ic˙(t)|δ3ζ3,|Rc(t)Rc˙(t)|δ4ζ4,|Vc(t)Vc˙(t)|δ5ζ5.

Theorem 4.2

Assume that (B) , is satisfied then the Covid-19 model of fractional order (1) is Hyers-Ulam-Stable.

Proof

With the help of Theorem 3.1, the Covid-19 model of fractional order (1) has a unique solution, say, Sc(t),Ec(t),Ic(t),Rc(t),Vc(t) We assume (Sc˙(t),Ec˙(t),Ic˙(t),Rc˙(t),Vc˙(t) be an another solution for the model (1) satisfying (20)(24). Then

Sc(t)Sc˙(t)1κ*1B(κ*1)Y1(t,Sc(t))Y1(t,Sc˙(t))+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc(s))Y1(t,Sc˙(t))ds[1κ*1B(κ*1)+1B(κ*1)Γ(κ*1)]ψ1ScSc˙. (53)

Taking ζ1=ψ1, Δ=1κ*1B(κ*1)+κ*1B(κ*1)Γ(κ*1) this implies

Sc(t)Sc˙(t)ζ1Δ1. (54)

Similarly, for Ec(t),Ec˙(t),Ic(t),Ic˙(t),Rc(t),Rc˙(t),Vc(t),Vc˙(t) we have

{Ec(t)Ec˙(t)ζ2Δ2,Ic(t)Ic˙(t)ζ3Δ3,Rc(t)Rc˙(t)ζ4Δ4,Vc(t)Vc˙(t)ζ5Δ5. (55)

Thus, the solution of Covid-19 model of fractional order (1) is stable. □

5. Numerical scheme

For the numerical scheme of (15)-(19), consider:

{0ABCDtκ*1Sc(t)=Y1(t,Sc),0ABCDtκ*2Ec(t)=Y2(t.Ec),0ABCDtκ*3Ic(t)=Y3(t,Ic),0ABCDtκ*4Rc(t)=Y4(t,Rc),0ABCDtκ*5Vc(t)=Y5(t.Vc). (56)

By the Lemma 1.4, (56) becomes

Sc(t)Sc(0)=1κ*1B(κ*1)Y1(t,Sc)+κ*1B(κ*1)Γ(κ*1)0t(ts)κ*11Y1(s,Sc)ds, (57)
Ec(t)Ec(0)=1κ*2B(κ*2)Y2(t,Ec)+κ*2B(κ*2)Γ(κ*2)0t(ts)κ*21Y2(s,Ec)ds, (58)
Ic(t)Ic(0)=1κ*3B(κ*3)Y2(t,Ic)+κ*3B(κ*3)Γ(κ*3)0t(ts)κ*31Y3(s,Ic)ds, (59)
Rc(t)Rc(0)=1κ*4B(κ*4)Y4(t,Rc)+κ*4B(κ*4)Γ(κ*4)0t(ts)κ*41Y4(s,Rc)ds, (60)
Vc(t)Vc(0)=1κ*5B(κ*5)Y5(t,Vc)+κ*5B(κ*5)Γ(κ*5)0t(ts)κ*51Y5(s,Vc)ds, (61)

By splitting [0,t] with the help of tm+1, for m=0,1,2,n, we have

Sc(tm+1)=1κ*1B(κ*1)Y1(tm,Sc)+κ*1B(κ*1)Γ(κ*1)k=0ntktk+1(tm+1s)κ*11Y1(s,Sc)ds, (62)
Ec(tm+1)=1κ*2B(κ*2)Y2(tm,Ec)+κ*2B(κ*2)Γ(κ*2)k=0ntktk+1(tm+1s)κ*21Y2(s,Ec)ds, (63)
Ic(tm+1)=1κ*3B(κ*3)Y3(tm,Ic)+κ*3B(κ*3)Γ(κ*3)k=0ntktk+1(tm+1s)κ*31Y3(s,Ic)ds, (64)
Rc(tm+1)=1κ*4B(κ*4)Y4(tm,Rc)+κ*4B(κ*4)Γ(κ*4)k=0ntktk+1(tm+1s)κ*41Y4(s,Rc)ds, (65)
Vc(tm+1)=1κ*5B(κ*5)Y5(tm,Vc)+κ*5B(κ*5)Γ(κ*5)k=0ntktk+1(tm+1s)κ*51Y5(s,Vc)ds, (66)

Now, using Lagrange’s interpolation, we have

Sc(tm+1)=1κ*1B(κ*1)Y1(tk,Sc)+κ*1B(κ*1)×k=0n[hκ*1Y1(tk,Sc)Γ(κ*1+2)((m+1k)κ*1(mk+2+κ*1)(mk)κ*1(mk+2+2κ*1))hκ*1Y1(tk1,Sc)Γ(κ*1+2)×((m+1k)κ*1(mk)κ*1(m+1k+κ*1))],
Ec(tm+1)=1κ*2B(κ*2)Y2(tk,Ec)+κ*2B(κ*2)×k=0n[hκ*2Y2(tk,Ec)Γ(κ*2+2)((m+1k)κ*2(mk+2+κ*2)(mk)κ*2(mk+2+2κ*2))hκ*2Y2(tk1,Ec)Γ(κ*2+2)×((m+1k)κ*2(mk)κ*2(m+1k+κ*2))],
Ic(tm+1)=1κ*3B(κ*3)Y3(tk,Ic)+κ*3B(κ*3)×k=0n[hκ*3Y3(tk,Ic)Γ(κ*3+2)((m+1k)κ*3(mk+2+κ*3)(mk)κ*3(mk+2+2κ*3))hκ*3Y3(tk1,Ic)Γ(κ*3+2)×((m+1k)κ*3(mk)κ*3(m+1k+κ*3))],
Rc(tm+1)=1κ*4B(κ*4)Y4(tk,Rc)+κ*4B(κ*4)×k=0n[hκ*4Y4(tk,Rc)Γ(κ*4+2)((m+1k)κ*4(mk+2+κ*4)(mk)κ*4(mk+2+2κ*4))hκ*4Y4(tk1,Rc)Γ(κ*4+2)×((m+1k)κ*4(mk)κ*4(m+1k+κ*4))],
Vc(tm+1)=1κ*5B(κ*5)Y5(tk,Vc)+κ*5B(κ*5)×k=0n[hκ*5Y5(tk,Vc)Γ(κ*5+2)((m+1k)κ*5(mk+2+κ*5)(mk)κ*5(mk+2+2κ*5))hκ*5Y5(tk1,Vc)Γ(κ*5+2)×((m+1k)κ*5(mk)κ*5(m+1k+κ*5))],

This numerical scheme will be applied in the next sub-portion for its accuracy.

5.1. Computational results

Here, we present the computational results based on the numerical scheme discussed above. We have considered the initial data Sc(0)=1.35*107,Ec(0)=150,Ic(0)=0,Rc(0)=0,Vc(0)=0, and the parametric values Πc=8859.23*104,βE=6.11*108,βI=2.62*108,βv=3.03*108,μ=0.1801,α=0.3,w=0.01,γ=0.197,ψ1=1.1230,ψ2=0.06,τ=2.0 from the [14]. And for more detail about the numerical results and fractional order modeling, we refer the readers to the work in [25], [26], [27], [28], [29], [30], [31], [32], [33], [34].

In Fig. 1 , we have given the solution of ABC-Covid-19 model for the κi=1.0, for i=1,2,,6. The second Fig. 2 is the solution of the system for the κi=0.99, for i=1,2,,6. The third Fig. 3 , represents the solution of the fractional order model (1) for the κi=0.98, for i=1,2,,6. The fourth Fig. 4 represent the comparison of the susceptible people Sc(t) for κi=κ=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6. This class shows that initially this class is increasing and after 10 days there comes a rapid decrease in the population which is ultimately transferred to the exposed class Ec(t) given in Fig. 5 and to the infection class Ic(t) given in Fig. 6 . Figure 7 gives us the detail of the recovery which starts after 15 days and increases in the next upcoming days. In Fig. 8 , the population of the virus is given for the κi=κ=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6.

Fig. 1.

Fig. 1

Joint solution of the fractal-fractional model (1) for κi*=1.0, for i=1,2,,6.

Fig. 2.

Fig. 2

Joint solution of the fractal-fractional model (1) for κi*=0.99, for i=1,2,,6.

Fig. 3.

Fig. 3

Joint solution of the fractal-fractional model (1) for κi*=0.98, for i=1,2,,6.

Fig. 4.

Fig. 4

Comparison of Sc(t) for κi*=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6.

Fig. 5.

Fig. 5

Comparison of Ec(t) for κi*=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6.

Fig. 6.

Fig. 6

Comparison of Ic(t) for κi*=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6.

Fig. 7.

Fig. 7

Comparison of Rc(t) for κi*=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6.

Fig. 8.

Fig. 8

Comparison of Vc(t) for κi*=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6.

6. Conclusions

In this paper, we have given an analytical and computational study of the fractional order COVID-19 model (1) for the existence results and numerical simulations. The model is analyzed for the fractional orders κi=κ=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6. A computational scheme is derived for the numerical simulation based on the Lagrange’s interpolation polynomial and is tested for the available data given in [14]. The results are graphical represented in eight figures. In Fig. 1, we have given the solution of the fractional order ABC-Covid-19 model for the κi=1.0, for i=1,2,,6. The second Fig. 2 is the solution of the system for the κi=0.99, for i=1,2,,6. The third Fig. 3, represents the solution of the fractional order model (1) for the κi=0.98, for i=1,2,,6. The fourth Fig. 4 represent the comparison of the susceptible people Sc(t) for κi=κ=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6. This class shows that initially this class is increasing and after 10 days there comes a rapid decrease in the population which is ultimately transferred to the exposed class Ec(t) given in Fig. 5 and to the infection class Ic(t) given in Fig. 6. Figure 7 gives us the detail of the recovery which starts after 15 days and increases in the next upcoming days. In Fig. 8, the population of the virus is given for the κi=κ=1.0,0.99,0.98,0.97,0.96, for i=1,2,,6. All the results show similar behavior as the classical. For the future study, we suggest the readers for reconsideration of the problem for the optimal control and sensitivity analysis. Also, one can compare the obtained results with the simulations for other fractional derivatives.

Availability of data and material

Not applicable.

CRediT authorship contribution statement

Hasib Khan: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing. Muhammad Ibrahim: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - original draft, Writing - review & editing. Abdel-Haleem Abdel-Aty: Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Validation, Visualization, Writing - original draft, Writing - review & editing. M. Motawi Khashan: Conceptualization, Funding acquisition, Investigation, Validation, Visualization, Writing - original draft, Writing - review & editing. Farhat Ali Khan: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing. Aziz Khan: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing.

Declaration of Competing Interest

The authors have no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research, King Saud University for its funding through Research Unit of Common First Year Deanship. Aziz Khan is thankful to the Prince Sultan University for his support in this work under the project NAMAM.

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