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. 2021 Mar 10;44(11):8598–8614. doi: 10.1002/mma.7286

Generalized form of fractional order COVID‐19 model with Mittag–Leffler kernel

Muhammad Aslam 1, Muhammad Farman 2, Ali Akgül 3,, Aqeel Ahmad 2, Meng Sun 4
PMCID: PMC8242392  PMID: 34226779

Abstract

An important advantage of fractional derivatives is that we can formulate models describing much better systems with memory effects. Fractional operators with different memory are related to the different type of relaxation process of the nonlocal dynamical systems. Therefore, we investigate the COVID‐19 model with the fractional derivatives in this paper. We apply very effective numerical methods to obtain the numerical results. We also use the Sumudu transform to get the solutions of the models. The Sumudu transform is able to keep the unit of the function, the parity of the function, and has many other properties that are more valuable. We present scientific results in the paper and also prove these results by effective numerical techniques which will be helpful to understand the outbreak of COVID‐19.

Keywords: COVID‐19, Mittag–Leffler kernel, numerical methods, Sumudu transform

1. INTRODUCTION

Epidemiological study presents a crucial role to see the impact of infectious disease in a community. In mathematical modeling, we check models, estimation of parameters, and measure sensitivity through different parameters and compute their numerical simulations through building models. The control parameters and ratio spread of disease can be understood through this type of research. 1 These types of diseased models are often called infectious diseases (i.e., the disease which transferred from one person to another person). Measles, rubella, chicken pox, mumps, aids, and gonorrhea syphilis are the examples of infectious disease. 2

Severe acute respiratory syndrome (SARS) is caused by a coronavirus and plays important role for its investigation. 3 According to the group of investigators that has been working since 30 years on the coronavirus family investigated that SARS and coronavirus have many similar features like biology and pathogenesis. 4 RNA enveloped viruses known as coronavirus are spread among humans, mammals, and birds. Many respiratory, enteric, hepatic, and neurological diseases are caused by coronavirus. 5 Human disease is caused by six types of coronavirus. 6 In 2019, China faced a major outbreak of coronavirus disease 2019 (COVID‐19), and this outbreak has the potential to become a worldwide pandemic. Interventions and real‐time data are needed for the control on this outbreak of coronavirus. 7 In previous studies, the transfer of the virus from one person to another person, its severity and history of the pathogen in the first week of the outbreak, has been explained with the help of real‐time analyses. In December 2019, a group of people in Wuhan admitted to the hospital that all were suffering from pneumonia, and the cause of pneumonia was idiopathic. Most of the people linked cause of pneumonia with the eating of wet markets meet and seafood. Investigation on etiology and epidemiology of disease was conducted on December 31, 2019, by Chinese Center for Disease Control and Prevention (China CDC) with the help of Wuhan city health authorities. Epidemically changing was measured by time‐delay distributions including date of admission to hospital and death. According to the clinical study on the COVID‐19, symptoms of coronavirus appear after 7 days of onset of illness. 8 The time from hospital admission to death is also critical to the avoidance of underestimation when calculating case fatality risk. 9

The fractional order techniques are very helpful to better understand the explanation of real‐world problems other than ordinary derivative. 10 , 11 The idea of fractional derivative has been introduced by Riemann–Liouville, which is based on power law. The new fractional derivative which is utilizing the exponential kernel is prosed in Atangana and Alkahtani. 12 A new fractional derivative with a nonsingular kernel involving exponential and trigonometric functions is proposed in previous studies, 13 , 14 , 15 , 16 and some related new approaches for epidemic models have been illustrated here. 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 Important results related to this new operator have been established, and some examples have been provided in Khan et al. 25 The equation of one‐dimensional finite element can be established generalized three‐dimensional motion by using the Lagrange's equations. The problem is important in technical applications of the last decades, characterized by high velocities and high applied loads. 26 A constant Thomson coefficient, instead of traditionally a constant Seebeck coefficient, is assumed. The charge density of the induced electric current is taken as a function of time. A normal mode method is proposed to analyze the problem and to obtain numerical solutions. 27

We organize our work as follows: We present the main definitions of fractional calculus in Section 2. We give the fractional order COVID‐19 model in Section 3. Sumudu transform (ST) is applied in this section, also present some scientific theorems. We discuss Adams–Moulton method with the Mittag–Leffler kernel in Section 4. The new numerical scheme is developed in Section 5. Conclusion is provided in the last section.

2. SOME BASIC CONCEPTS OF FRACTIONAL CALCULUS

We give some basic definitions related to fractional calculus and ST in this section.

Definition 2.1

For any function ϕ(t) over a set, the Sumudu transform

A=ϕt:there existΛτ1τ2>0ϕt<Λexpττiift1j×0

is defined by

Fu=STϕt=0expτϕut,uτ1τ2. (1)

Definition 2.2

We define the fractional order derivative of Atangana–Baleanu in Caputo sense (ABC) as 13

DταaABCϕτ=ABαnαaτdndwnfwEαατwαnαdw,n1<α<n, (2)

where E α is the Mittag–Leffler function and AB(α) is normalization function and AB(0) = AB(1) = 1. The Laplace transform of Equation 2 is presented as

LDταaABCϕτs=ABα1αsαLϕτssα1ϕ0sα+a1a. (3)

By using ST for (2), we obtain

STDτα0ABCϕτs=Bα1α+αsα×STϕtϕ0. (4)

Definition 2.3

We have the Atangana–Baleanu fractional integral of order α of a function ϕ(t) as 23

IταaABCϕτ=1αBαϕτ+αBαΓαaτϕsτsα1ds. (5)

3. FRACTIONAL ORDER COVID‐19 MODEL

The model of COVID‐19 with quarantine and isolation has eight sub‐compartments which are S(t) Susceptible individual, E(t) Exposed individuals, I(t) Infected individuals, A(t) Asymptomatically infected, Q(t) Quarantined, H(t) Hospitalized, R(t) recovered Individuals, M(t) environmental generating function. The model parameters are the Birth rate is presented with Λ in the model. The natural morality rate of the human population is described with μ. The healthy individuals require infection after contacting with infected and asymptomatic infected individuals by a rate ƞ1, while ψ denotes the transmissibility factor. The asymptomatic infection is generated by the parameter θ. The incubation periods are shown by ω and ρ. The parameters τ1, τ2, ϕ 1 , and ϕ 2 define, respectively, the recovery of infected, asymptomatically infected, quarantined, and hospitalized individuals. The hospitalization rates of infected and quarantined individuals are demonstrated, respectively, by γ and δ2. The disease death rates of infected and hospitalized individuals are shown by ξ1 and ξ2. The variable δ1 shows the quarantine rate of exposed individuals. Individuals who are visiting the seafood market and catch the infection are increasing with rate ƞ 2. The infection generated in the seafood market due to infected and asymptomatically infected is presented by the parameters q1 and q2, respectively, while the removal of infection from the market is shown by q3. The system of governing equations for the model is given as

dSdt=ΛμStλtSt,dEdt=λtSt1θω+θρ+μ+δ1Et,dIdt=1θωEtτ1+μ+ξ1+γIt,dAdt=θρEtτ2+μAt,dQdt=δ1Etμ+ϕ1+δ2Qt,dHdt=γIt+δ2Qtμ+ϕ2+ξ2Ht,dRdt=τ1It+τ2At+ϕ1Qt+ϕ2HtμRt,dMdt=q1It+q2Atq3Mt, (6)

where

λt=ƞ1I+ψAN+ƞ2M.

We replace the classical derivative with the ABC derivative and obtain:

DtαStOABC=ΛμStλtSt,DtαEtOABC=λtSt1θω+θρ+μ+δ1Et,DtαItOABC=1θωEtτ1+μ+ξ1+γIt,DtαAtOABC=θρEtτ2+μAt,DtαQtOABC=δ1Etμ+ϕ1+δ2Qt,DtαHtOABC=γIt+δ2Qtμ+ϕ2+ξ2Ht,DtαRtOABC=τ1It+τ2At+ϕ1Qt+ϕ2HtμRt,DtαMtOABC=q1It+q2Atq3Mt. (7)

We apply the ST and get

BααΓα+1Eα11αwαSTStS0=STΛμStλtSt,BααΓα+1Eα11αwαSTEtE0=STλtSt1θω+θρ+μ+δ1Et,BααΓα+1Eα11αwαSTItI0=ST1θωEtτ1+μ+ξ1+γIt,BααΓα+1Eα11αwαSTAtA0=STθρEtτ2+μAt,BααΓα+1Eα11αwαSTQtQ0=STδ1Etμ+ϕ1+δ2Qt,BααΓα+1Eα11αwαSTHtH0=STγIt+δ2Qtμ+ϕ2+ξ2Ht,BααΓα+1Eα11αwαSTRtR0=STτ1It+τ2At+ϕ1Qt+ϕ2HtμRt,BααΓα+1Eα11αwαSTMtM0=STq1It+q2Atq3Mt. (8)

Reorganizing system 8, we have

STSt=S0+1αBααΓα+1Eα11αwαSTΛμStλtSt,STEt=E0+1αBααΓα+1Eα11αwαxSTλtSt1θω+θρ+μ+δ1Et,STIt=I0+1αBααΓα+1Eα11αwαST1θωEtτ1+μ+ξ1+γIt,STAt=A0+1αBααΓα+1Eα11αwαSTθρEtτ2+μAt,STQt=Q0+1αBααΓα+1Eα11αwαSTδ1Etμ+ϕ1+δ2Qt,STHt=H0+1αBααΓα+1Eα11αwαSTγIt+δ2Qtμ+ϕ2+ξ2Ht,STRt=R0+1αBααΓα+1Eα11αwαSTτ1It+τ2At+ϕ1Qt+ϕ2HtμRt,STMt=M0+1αBααΓα+1Eα11αwαSTq1It+q2Atq3Mt. (9)

Then, we apply the inverse ST and obtain

St=S0+ST11αBααΓα+1Eα11αwαSTΛμStλtSt,Et=E0+ST11αBααΓα+1Eα11αwαSTλtSt1θω+θρ+μ+δ1Et,It=I0+ST11αBααΓα+1Eα11αwαST1θωEtτ1+μ+ξ1+γIt,At=A0+ST11αBααΓα+1Eα11αwαSTθρEtτ2+μAt,Qt=Q0+ST11αBααΓα+1Eα11αwαSTδ1Etμ+ϕ1+δ2Qt,Ht=H0+ST11αBααΓα+1Eα11αwαSTγIt+δ2Qtμ+ϕ2+ξ2Ht,Rt=R0+ST11αBααΓα+1Eα11αwαSTτ1It+τ2At+ϕ1Qt+ϕ2HtμRt,Mt=M0+ST11αBααΓα+1Eα11αwαSTq1It+q2Atq3Mt.

Since

λt=ƞ1I+ψAN+ƞ2M

and λt=ƞ1NInt+ψAnt+ƞ2M.

Therefore, the following is obtained:

Sn+1t=Sn0+ST11αBααΓα+1Eα11αwαSTΛμSntƞ1Int+ψAntN+ƞ2MntSnt,En+1t=En0+ST11αBααΓα+1Eα11αwα×STƞ1Int+ψAntN+ƞ2MntSnt1θω+θρ+μ+δ1EntIn+1t=In0+ST11αBααΓα+1Eα11αwαST1θωEntτ1+μ+ξ1+γInt,An+1t=An0+ST11αBααΓα+1Eα11αwαSTθρEntτ2+μAnt,Qn+1t=Qn0+ST11αBααΓα+1Eα11αwαSTδ1Entμ+ϕ1+δ2Qnt,Hn+1t=Hn0+ST11αBααΓα+1Eα11αwαSTγInt+δ2Qntμ+ϕ2+ξ2Hnt,Rn+1t=Rn0+ST11αBααΓα+1Eα11αwαSTτ1Int+τ2Ant+ϕ1Qnt+ϕ2HntμRnt,Mn+1t=Mn0+ST11αBααΓα+1Eα11αwαSTq1Int+q2Antq3Mnt. (10)

And obtained solution of 10 is presented as

St=limnSnt;Et=limnEnt;It=limnInt;At=limnAnt;Qt=limnQnt;Ht=limnHnt;Rt=limnRnt;Mt=limnMnt.

Assume that (X, |·|) is a Banach space and H is a self‐map of X. Suppose that rn + 1 = g (Hrn) is a specific recursive procedure. The following conditions must be satisfied for rn + 1 = Hrn.

  1. The fixed point set of H has at least one element.

  2. rn converges to a point P Є F (H).

  3. limnSnt=P.

Theorem 3.1

Assume that (X, |·|) is a Banach space and H is a self‐map of X fulfilling

ǁHxHrǁθǁXHxǁ+θǁx. (11)

for all x, r Є X where 0 ≤ θ < 1. Let H be a picard H‐stable.

We take into consideration Equation 5 and get

1αBααΓα+1Eα11αwα. (12)

Theorem 3.2

We describe K as a self‐map by

KSn+1t=Sn+1t=Snt+ST11αBααΓα+1Eα11αwα×STΛμSntƞ1Int+ψAntN+ƞ2MntSnt,KEn+1t=En+1t=Ent+ST11αBααΓα+1Eα11αwα×STƞ1Int+ψAntN+ƞ2MntSnt1θω+θρ+μ+δ1Ent,KIn+1t=In+1t=Int+ST11αBααΓα+1Eα11αwα×ST1θωEntτ1+μ+ξ1+γInt,KAn+1t=An+1t=Ant+ST11αBααΓα+1Eα11αwα×STθρEntτ2+μAnt,KQn+1t=AQn+1t=AQnt+ST11αBααΓα+1Eα11αwα×STδ1Entμ+ϕ1+δ2Qnt,KHn+1t=Hn+1t=Hnt+ST11αBααΓα+1Eα11αwα×STγInt+δ2Qntμ+ϕ2+ξ2Hnt,KRn+1t=Rn+1t=Rnt+ST11αBααΓα+1Eα11αwα×STτ1Int+τ2Ant+ϕ1Qnt+ϕ2HntμRnt,KMn+1t=Mn+1t=Mnt+ST11αBααΓα+1Eα11αwα×STq1Int+q2Antq3Mnt.

Then, we reach

ǁKSntKSmtǁǁSntSmtǁ+ST11αBααΓα+1Eα11αwα×STΛμǁSntSmtǁƞ1ǁIntImtǁ+ψǁAntAmtǁN+ƞ2(ǁMntMmtǁǁSntSmtǁ,
ǁKEntKEmtǁǁEntEmtǁ+ST11αBααΓα+1Eα11αwα×STƞ1ƞ1ǁIntImtǁ+ψǁAntAmtǁN+ƞ2(ǁMntMmtǁǁSntSmtǁ1θω+θρ+μ+δ1ǁ(EntEmtǁ,
ǁKIntKImtǁǁIntImtǁ+ST11αBααΓα+1Eα11αwα×ST1θωǁEntEmtǁτ1+μ+ξ1+γǁIntImtǁ,
ǁKAntKAmtǁǁAntAmtǁ+ST11αBααΓα+1Eα11αwα×STθρǁEntEmtǁτ2+μǁAntAmtǁ,
ǁKQntKQmtǁǁQntQmtǁ+ST11αBααΓα+1Eα11αwαxSTδ1ǁEntEmtǁμ+ϕ1+δ2ǁQntQmtǁ,
ǁKHntKHmtǁǁHntHmtǁ+ST11αBααΓα+1Eα11αwα×STγǁIntImtǁ+δ2ǁQntQmtǁμ+ϕ2+ξ2ǁHntHmtǁ,
ǁKRntKRmtǁǁRntRmtǁ+ST11αBααΓα+1Eα11αwα×STτ1ǁIntImtǁ+τ2ǁAntAmtǁ+ϕ1ǁQntQmtǁ+ϕ2ǁHntHmtǁμǁRntRmtǁ,
ǁKMntKMmtǁǁMntMmtǁ+ST11αBααΓα+1Eα11αwα×STq1ǁIntImtǁ+q2ǁAntAmtǁ)q3ǁMntMmtǁ.

K satisfies the condition associated with the Theorem 3.1 if

θ=0,0,0,0,0,0,0,0,θ=ǁSntSmtǁ×ǁSnt+Smtǁ+ΛμǁSntSmtǁƞ1ǁIntImtǁ+ψǁAntAmtǁN+ƞ2(ǁMntMmtǁǁSntSmtǁ×ǁEntEmtǁ×ǁEnt+Emtǁ+ƞ1ǁIntImtǁ+ψǁAntAmtǁN+ƞ2(ǁMntMmtǁǁSntSmtǁ1θω+θρ+μ+δ1ǁEntEmtǁ×ǁIntImtǁ×ǁInt+Imtǁ+1θωǁEntEmtǁτ1+μ+ξ1+γǁIntImtǁ×ǁAntAmtǁ×ǁAnt+Amtǁ+θρǁEntEmtǁτ2+μǁAntamtǁ×ǁQntQmtǁ×ǁQnt+Qmtǁ+δ1ǁEntEmtǁμ+ϕ1+δ2ǁQntQmtǁ×ǁHntHmtǁ×ǁHnt+Hmtǁ+γǁIntImtǁ+δ2QntQmtμ+ϕ2+ξ2ǁHntHmtǁ×ǁRntRmtǁ×ǁRnt+Rmtǁ+τ1ǁIntImtǁ+τ2ǁAntamtǁ+ϕ1ǁQntQmtǁ+ϕ2ǁHntHmtǁμǁRntRmtǁ×ǁMntMmtǁ×ǁMnt+Mmtǁ+q1ǁIntImtǁ+q2ǁAntAmtǁq3ǁMntMmtǁ.

We add that K is Picard K‐stable.

Theorem 3.3

The special solution of system 6 using the iteration method is unique singular solution.

We consider the following Hilbert space H = L 2((p,q) × (0,T)) which can be defined as

h:p,q×0,T,ghdgdh<.

Then, we take into consideration:

θ0,0,0,0,0,0,0,0,θ=ΛμStλtSt,λtSt1θω+θρ+μ+δ1Et,1θωEtτ1+μ+ξ1+γIt,θρEtτ2+μAt,δ1Etμ+ϕ1+δ2Qt,γIt+δ2Qtμ+ϕ2+ξ2Ht,τ1It+τ2At+ϕ1Qt+ϕ2HtμRt,q1It+q2Atq3Mt. (13)

We establish that the inner product of

TS11tS12tE21tE22tI31tI32tA41tA42tQ51tQ52tH61tH62tR71tR72tM81tM82tV1V2V3V4V5V6V7V8,

where

(S 11(t) − S 12(t), E 21(t) − E 22(t), I 31(t) − I 32(t), A 41(t) − A 42(t), Q 51(t) − Q 52(t), H 61(t) − H 62(t), R 71(t) − R 72(t), M 81(t) − M 82(t)) are the special solutions of the system. Then, we have

ΛμS11tS21tλtS11tS21tV1ΛV1+μS11tS21tV1+λtS11tS21tV1,
λtS11tS21t1θω+θρ+μ+δ1E21tE22tV2λtS11tS21tV2+1θω+θρ+μ+δ1E21tE22tV2,
1θωE21tE22tτ1+μ+ξ1+γI31tI32tV31θωE21tE22tV3+τ1+μ+ξ1+γI31tI32tV3,
θρE21tE22tτ2+μA41tA42tV4θρE21tE22tV4+τ2+μA41tA42tV4,
δ1E21tE22tμ+ϕ1+δ2Q51tQ52tV5δ1E21tE22tV5+μ+ϕ1+δ2Q51tQ52tV5,
γI31tI32t+δ2Q51tQ52tμ+ϕ2+ξ2H61tH62tV6γI31tI32tV6+δ2Q51tQ52tV6+μ+ϕ2+ξ2H61tH62tV6,
τ1I31tI32t+τ2A41tA42t+ϕ1Q51tQ52t+ϕ2H61tH62tμR71tR72tV7τ1I31tI32tV7+τ2A41tA42tV7+ϕ1Q51tQ52tV7+ϕ2H61tH62tV7+μR71tR72tV7,
q1I31tI32t+q2A41tA42tq3M81tM82tV8q1I31tI32tV8+q2A41tA42tV8+q3M81tM82tV8.

In the case for large number e 1, e 2, e 3, e 4, e 5, e 6, e 7, and e 8, both solutions happen to be converged to the exact solution. Applying the topology concept, we can get eight positive very small variables χe1χe2χe3χe4χe5χe6χe7andχe8.

SS11,SS12χe1ϖ,EE21,EE22χe2ς,II31,II32χe3υ,AA41,AA42χe4κ,QQ51,QQ52χe5ϱ,HH61,HH62χe6ζ,RR71,RR72χe7νandMM81,MM82χe8ε,

where

ϖ=8Λ+μS11tS21t+λtS11tS21tV1
ς=8λtS11tS21t+1θω+θρ+μ+δ1E21tE22tV2
υ=81θωE21tE22t+τ1+μ+ξ1+γI31tI32tV3
κ=8θρE21tE22t+τ2+μA41tA42tV4
ϱ=8δ1E21tE22t+μ+ϕ1+δ2Q51tQ52tV5
ζ=8γI31tI32t+δ2Q51tQ52t+μ+ϕ2+ξ2H61tH62tV6
ν=8τ1I31tI32t+τ2A41tA42t+ϕ1Q51tQ52t+ϕ2H61tH62t+μR71tR72tV7
ε=8q1I31tI32t+q2A41tA42t+q3M81tM82tV8.

But, it is obvious that

Λ+μS11tS21t+λtS11tS21t0
λtS11tS21t+1θω+θρ+μ+δ1E21tE22t0
1θωE21tE22t+τ1+μ+ξ1+γI31tI32t0
θρE21tE22t+τ2+μA41tA42t0
δ1E21tE22t+μ+ϕ1+δ2Q51tQ52t0
γI31tI32t+δ2Q51tQ52t+μ+ϕ2+ξ2H61tH62t0
τ1I31tI32t+τ2A41tA42t+ϕ1Q51tQ52t+ϕ2H61tH62t+μR71tR72t0
q1I31tI32t+q2A41tA42t+q3M81tM82t0,

where ‖V 1‖,‖V 2‖,‖V 3‖,‖V 4‖,‖V 5‖,‖V 6‖,‖V 7‖,‖V 8‖ ≠ 0.

Therefore, we have

S11S12=0,E21E22=0,I31I32=0,A41A42=0,
Q51Q52=0,H61H62=0,R71R72=0,M81M82=0.

which yields that

S11=S12,E21=E22,I31=I32,A41=A42,Q51=Q52,H61=H62,R71=R72,M81=M82.

This completes the proof of uniqueness.

4. ADAMS–MOULTON METHOD FOR ATANGANA–BALEANU FRACTIONAL DERIVATIVE

We define the numerical scheme of the Atangana–Baleanu fractional integral by using Adams–Moulton rule as 20

Itα0ABftn+1=1αβαftn+1ftn2+αΓαk=0ftn+1ftn2dkα, (14)

where

dkα=k+11αk1α.

We obtain the following for system 6:

Sn+1tSnt=S0nt+1αβαΛμStn+1Stn2λtn+1λtn2Stn+1Stn2+αΓαk=0dkαΛμStn+1Stn2λtn+1λtn2Stn+1Stn2,
En+1tEnt=E0nt+1αβαλtn+1λtn2Stn+1Stn21θω+θρ+μ+δ1Etn+1Etn2+αΓαk=0dkαλtn+1λtn2Stn+1Stn21θω+θρ+μ+δ1Etn+1Etn2,
In+1tInt=I0nt+1αβα1θωEtn+1Etn2τ1+μ+ξ1+γItn+1Itn2+αΓαk=0dkα1θωEtn+1Etn2τ1+μ+ξ1+γItn+1Itn2,
An+1tAnt=A0nt+1αβαθρEtn+1Etn2τ2+μAtn+1Atn2+αΓαk=0dkαθρEtn+1Etn2τ2+μAtn+1Atn2,
Qn+1tQnt=Q0nt+1αβαδ1Etn+1Etn2μ+ϕ1+δ2Qtn+1Qtn2+αΓαk=0dkαδ1Etn+1Etn2μ+ϕ1+δ2Qtn+1Qtn2.
Hn+1tHnt=H0nt+1αβαγItn+1Itn2+δ2Qtn+1Qtn2μ+ϕ2+ξ2Htn+1Htn2+αΓαk=0dkαγItn+1Itn2+δ2Qtn+1Qtn2μ+ϕ2+ξ2Htn+1Htn2.
Rn+1tRnt=R0nt+1αβατ1Itn+1Itn2+τ2Atn+1Atn2+ϕ1Qtn+1Qtn2+ϕ2Htn+1Htn2μRtn+1Rtn2+αΓαk=0dkατ1Itn+1Itn2+τ2Atn+1Atn2+ϕ1Qtn+1Qtn2+ϕ2Htn+1Htn2μRtn+1Rtn2.
Mn+1tMnt=M0nt+1αβαq1Itn+1Itn2+q2Atn+1Atn2q3Mtn+1Mtn2+αΓαk=0dkαq1Itn+1Itn2+q2Atn+1Atn2q3Mtn+1Mtn2.

5. NEW NUMERICAL SCHEME

In this section, we construct a new numerical scheme for nonlinear fractional differential equations with fractional derivative with nonlocal and nonsingular kernel. To do this, we consider the following nonlinear fractional ordinary equation. 24

D0ABCyt=ftyt,y0=y0. (15)

The above equation can be converted to a fractional integral equation by using the fundamental theorem of fractional calculus.

yty0=1αABCαftyt+αΓα×ABCα0tfτyτtτα1. (16)

At a given point t n+1, n = 0,1,2,3,…, the above equation is reformulated as follows:

ytn+1y0=1αABCαftnytn+αΓα×ABCα0tn+1fτyτtn+1τα1=1αABCαftnytn+αΓα×ABCαk=0ntktk+1fτyτtn+1τα1 (17)

Within the interval [t k, t k+1], the function f(τ, y(τ)), using the two‐step Lagrange polynomial interpolation, can be approximate as follows:

Pkτ=τtk1tktk1ftkytkτtktktk1ftk1ytk1=ftkytkhτtk1ftk1ytk1hτtkftkykhτtk1ftk1yk1hτtk. (18)

The above approximation can therefore be included in Equation 17 to produce

yn+1=y0+1αABCαftnytn+αΓα×ABCαk=0nftkykhtktk+1τtk1tn+1τα1ftk1yk1htktk+1τtktn+1τα1. (19)

For simplicity, we let

Aa,k,1=tktk+1τtk1tn+1τα1 (20)

and also

Aa,k,2=tktk+1τtktn+1τα1Aa,k,1=hα+1n+1kαnk+2+αnkαnk+2+2ααα+1Aa,k,2=hα+1n+1kα+1nkαnk+1+ααα+1. (21)

Thus, integrating Equations 20 and 21 and replacing them in Equation 19, we obtain

yn+1=y0+1αABCαftnytn+αABCαk=0n×hαftkykΓα+2n+1kαnk+2+αnkαnk+2+2αhαftk1yk1Γα+2n+1kα+1nkαnk+1+α. (22)

We obtain the following for system 6:

StS0=1αABCαΛμStλtSt+αΓα×ABCα0tΛμSτλτSτtτα1,EtE0=1αABCαλtSt1θω+θρ+μ+δ1Et+αΓα×ABCα0tλτSτ1θω+θρ+μ+δ1Eτtτα1,ItI0=1αABCα1θωEtτ1+μ+ξ1+γIt+αΓα×ABCα0t1θωEττ1+μ+ξ1+γIτtτα1,AtA0=1αABCαθρEtτ2+μAt+αΓα×ABCα0tθρEττ2+μAτtτα1,QtQ0=1αABCαδ1Etμ+ϕ1+δ2Qt+αΓα×ABCα0tδ1Eτμ+ϕ1+δ2Qτtτα1,HtH0=1αABCαγIt+δ2Qtμ+ϕ2+ξ2Ht+αΓα×ABCα0tγIτ+δ2Qτμ+ϕ2+ξ2Hτtτα1.RtR0=1αABCατ1It+τ2At+ϕ1Qt+ϕ2HtμRt+αΓα×ABCα0tτ1fτIτ+τ2Aτ+ϕ1Qτ+ϕ2HτμRτtτα1.MtM0=1αABCαq1It+q2Atq3Mt+αΓα×ABCα0tq1Iτ+q2Aτq3Mτtτα1. (23)

At a given point t n+1,n = 0,1,2,3,…, the above equation is reformulated as

Stn+1S0=1αABCαΛμStnλtnStn+αΓα×ABCαk=0ntktk+1ΛμSτλτSτtn+1τα1,Etn+1E0=1αABCαλtnStn1θω+θρ+μ+δ1Etn+αΓα×ABCαk=0ntktk+1λτSτ1θω+θρ+μ+δ1Eτtn+1τα1,Itn+1I0=1αABCα1θωEtnτ1+μ+ξ1+γItn+αΓα×ABCαk=0ntktk+11θωEττ1+μ+ξ1+γIτtn+1τα1,Atn+1A0=1αABCαθρEtnτ2+μAtn+αΓα×ABCαk=0ntktk+1θρEττ2+μAτtn+1τα1,Qtn+1Q0=1αABCαδ1Etnμ+ϕ1+δ2Qtn+αΓα×ABCαk=0ntktk+1δ1Eτμ+ϕ1+δ2Qτtn+1τα1,Htn+1H0=1αABCαγItn+δ2Qtnμ+ϕ2+ξ2Htn+αΓα×ABCαk=0ntktk+1γIτ+δ2Qτμ+ϕ2+ξ2Hτtn+1τα1,Rtn+1R0=1αABCατ1Itn+τ2Atn+ϕ1Qtn+ϕ2HtnμRtn+αΓα×ABCαk=0ntktk+1τ1Iτ+τ2Aτ+ϕ1Qτ+ϕ2HτμRτtn+1τα1,Mtn+1M0=1αABCαq1Itn+q2Atnq3Mtn+αΓα×ABCαk=0ntktk+1q1Iτ+q2Aτq3Mτtn+1τα1. (24)

By using Equation 18, we have

Sn+1=S0+1αABCαΛμStnλtnStn+αΓα×ABCαk=0nΛμSkλkSkhB1ΛμSk1λk1Sk1hAa,k,2,En+1=E0+1αABCαλtnStn1θω+θρ+μ+δ1Etn+αΓα×ABCαk=0nλkSk1θω+θρ+μ+δ1EkhB1λk1Sk11θω+θρ+μ+δ1Ek1hAa,k,2,In+1=I0+1αABCα1θωEtnτ1+μ+ξ1+γItn+αΓα×ABCαk=0n1θωEkτ1+μ+ξ1+γIkhB11θωEk1τ1+μ+ξ1+γIk1hAa,k,2,An+1=A0+1αABCαθρEtnτ2+μAtn+αΓα×ABCαk=0nθρEkτ2+μAkhB1θρEk1τ2+μAk1hAa,k,2,Qn+1=Q0+1αABCαδ1Etnμ+ϕ1+δ2Qtn+αΓα×ABCαk=0nδ1Ekμ+ϕ1+δ2QkhB1δ1Ek1μ+ϕ1+δ2Qk1hAa,k,2,Hn+1=H0+1αABCαγItn+δ2Qtnμ+ϕ2+ξ2Htn+αΓα×ABCαk=0nγIk+δ2Qkμ+ϕ2+ξ2HkhB1γIk1+δ2Qk1μ+ϕ2+ξ2Hk1hAa,k,2,Rn+1=R0+1αABCατ1Itn+τ2Atn+ϕ1Qtn+ϕ2HtnμRtn+αΓα×ABCαk=0n×τ1Ik+τ2Ak+ϕ1Qk+ϕ2HkμRkhB1τ1Ik1+τ2Ak1+ϕ1Qk1+ϕ2Hk1μRk1hAa,k,2,Mn+1=M0+1αABCαq1Itn+q2Atnq3Mtn+αΓα×ABCαk=0nq1Ik+q2Akq3MkhB1q1Ik1+q2Ak1q3Mk1hAa,k,2, (25)

where Aa,k,2=tktk+1τtktn+1τα1 and B1=tktk+1τtk1tn+1τα1.

Thus, integrating Equations 20 and 21 and replacing them in equations of system 25, we get

Sn+1=S0+1αABCαΛμStnλtnStn+αABCαk=0nhαΛμSkλkSkΓα+2A2hαΛμSk1λk1Sk1Γα+2A1,En+1=E0+1αABCαλtnStn1θω+θρ+μ+δ1Etn+αABCαk=0nhαλkSk1θω+θρ+μ+δ1EkΓα+2A2hαλk1Sk11θω+θρ+μ+δ1Ek1Γα+2A1,In+1=I0+1αABCα1θωEtnτ1+μ+ξ1+γItn+αABCαk=0nhα1θωEkτ1+μ+ξ1+γIkΓα+2A2hα1θωEk1τ1+μ+ξ1+γIk1Γα+2A1,An+1=A0+1αABCαθρEtnτ2+μAtn+αABCαk=0nhαθρEkτ2+μAkΓα+2A2hαθρEk1τ2+μAk1Γα+2A1,Qn+1=Q0+1αABCαδ1Etnμ+ϕ1+δ2Qtn+αABCαk=0nhαδ1Ekμ+ϕ1+δ2QkΓα+2A2hαδ1Ek1μ+ϕ1+δ2Qk1Γα+2A1,Hn+1=H0+1αABCαγItn+δ2Qtnμ+ϕ2+ξ2Htn+αABCαk=0nhαγIk+δ2Qkμ+ϕ2+ξ2HkΓα+2A2hαγIk1+δ2Qk1μ+ϕ2+ξ2Hk1Γα+2A1,Rn+1=R0+1αABCατ1Itn+τ2Atn+ϕ1Qtn+ϕ2HtnμRtn+αABCαk=0nhατ1Ik+τ2Ak+ϕ1Qk+ϕ2HkμRkΓα+2A2hατ1Ik1+τ2Ak1+ϕ1Qk1+ϕ2Hk1μRk1Γα+2A1,Mn+1=M0+1αABCαq1Itn+q2Atnq3Mtn+αABCαk=0nhαq1Ik+q2Akq3MkΓα+2A2hαq1Ik1+q2Ak1q3Mk1Γα+2A1, (26)

where A 1 = {(n+1 − k)α+1 − (nk)α(nk+1+α)} and A 2 = {(n+1 − k)α(nk+2+α) − (nk)α(nk+2+2α)}.

6. CONCLUSION

In this manuscript, we investigated the COVID‐19 model with the help of ST and some effective numerical methods. The Mittag–Leffler kernel is used to obtain very effective results for the proposed model. Some theoretical results are developed for the model to prove the efficiency of the developed techniques. Results will be very helpful for analysis of COVID‐19 outbreak and to check the actual behavior of this pandemic disease, also helpful for further study on fractional derivatives.

CONFLICT OF INTEREST

This work does not have any conflicts of interest.

ACKNOWLEDGEMENT

There are no funders to report for this submission.

Aslam M, Farman M, Akgül A, Ahmad A, Sun M. Generalized form of fractional order COVID‐19 model with Mittag–Leffler kernel. Math Meth Appl Sci. 2021;44:8598–8614. 10.1002/mma.7286

REFERENCES

  • 1. Abubakar S, Akinwande NI, Abdulrahman S. A mathematical model of yellow fever epidemics. Afr Mat. 2012;6:56‐58. [Google Scholar]
  • 2. Hethcote HW. The mathematics of infectious diseases, society for industrial and applied mathematics SIAM. Review. 2000;42(4):599‐653. [Google Scholar]
  • 3. Baric RS, Fu K, Chen W, Yount B. High recombination and mutation rates in mouse hepatitis virus suggest that coronaviruses may be potentially important emerging viruses. Adv Exp Med Biol. 1995;380:5716. [DOI] [PubMed] [Google Scholar]
  • 4. Weiss SR, Leibowitz JL. Coronavirus pathogenesis. Adv Virus Res. 2011;81:85‐164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Su S, Wong G, Shi W, et al. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol. 2016;24(6):490‐502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Zhong NS, Zheng BJ, Li YM, et al. Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People's Republic of China, in February, 2003. Lancet. 2003;362(9393):1353‐1358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Rivers C, Chretien JP, Riley S, et al. Using outbreak science to strengthen the use of models during epidemics. Nat Commun. 2019;10(1):3102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Linton MN, Kobayashi T, Yang Y. Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: a statistical analysis of publicly available case data. J Clin Med. 2020;9(2):538. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497‐506. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Caputo M, Fabrizio M. A new definition of fractional derivative without singular kernel. Prog Fract Differ Appl. 2015;1(2):1‐13. [Google Scholar]
  • 11. Losada J, Nieto JJ. Properties of a new fractional derivative without singular kernel. Prog Fract Differ Appl. 2015;1(2):87‐92. [Google Scholar]
  • 12. Atangana A, Alkahtani BST. Analysis of the Keller–Segel model with a fractional derivative without singular kernel. Entropy. 2015;17(6):4439‐4453. [Google Scholar]
  • 13. Atangana A, Baleanu D. New fractional derivatives with nonlocal and non‐singular kernel theory and application to heat transfer model. Therm Sci. 2016;20(2):763‐769. [Google Scholar]
  • 14. Farman M, Saleem MU, Tabassum MF, Ahmad A, Ahmad MO. A linear control of composite model for glucose insulin glucagon. Ain Shams Eng J. 2019;10:867‐872. [Google Scholar]
  • 15. Farman M, Saleem MU, Ahmad A, et al. A control of glucose level in insulin therapies for the development of artificial pancreas by Atangana Baleanu fractional derivative. Alex Eng J. 2020;59(4):2639‐2648. [Google Scholar]
  • 16. Farman M, Akgül A, Baleanu D, Imtiaz S, Ahmad A. Analysis of fractional order chaotic financial model with minimum interest rate impact. Fractal Fractional. 2020;4(3):43. [Google Scholar]
  • 17. Saleem MU, Farman M, Ahmad A, Ehsan H, Ahmad MO. A Caputo Fabrizio fractional order model for control of glucose in insulin therapies for diabetes. Ain Shams Eng J. 2020;11(4):1309‐1316. 10.1016/j.asej.2020.03.006 [DOI] [Google Scholar]
  • 18. Marin M. On existence and uniqueness in thermoelasticity of micropolar bodies. Comptes Rendus de l'Académie Des Sciences Paris, Série II, B. 1995;321(12):375‐480. [Google Scholar]
  • 19. Marin M. A partition of energy in thermoelasticity of microstretch bodies. Nonlinear Anal: RWA. 2010;10(3):2436‐2447. [Google Scholar]
  • 20. Bushnaq S, Khan SA, Shah K, Zaman G. Mathematical analysis of HIV/AIDS infection model with Caputo‐Fabrizio fractional derivative. Cogent Math Stat. 2018;5:1432521. [Google Scholar]
  • 21. Huo H‐F, Chen R, Wang XY. Modelling and stability of HIV/AIDS epidemic model with treatment. App Math Model. 2016;40(13–14):6550‐6559. [Google Scholar]
  • 22. Moore EJ, Sirisubtawee S, Koonprasert S. A Caputo–Fabrizio fractional differential equation model for HIV/AIDS with treatment compartment. Adv Differ Equ. 2019;2019(1):200. 10.1186/s13662-019-2138-9 [DOI] [Google Scholar]
  • 23. Atangana A, Bonyah E, Elsadany AA. A fractional order optimal 4D chaotic financial model with Mittag‐Leffler law. Chin J Phys. 2020;65:38‐53. [Google Scholar]
  • 24. Toufik M, Atangana A. New numerical approximation of fractional derivative with non‐local and non‐singular kernel: application to chaotic models. Eur Phys J Plus. 2017;132(10):444. [Google Scholar]
  • 25. Khan MA, Atangana A, Alzahrani E. The dynamics of COVID‐19 with quarantined and isolation. Adv Difference Equ. 2020;2020(1):425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Vlase S, Marin M, Öchsner A, Scutaru ML. Motion equation for a flexible one‐dimensional element used in the dynamical analysis of a multibody system. Contin Mech Thermodyn. 2019;31(3):715‐724. [Google Scholar]
  • 27. Abd‐Elaziz EM, Marin M, Othman MIA. On the effect of Thomson and initial stress in a thermo‐porous elastic solid under G‐N electromagnetic theory. Symmetry. 2019;11(3):413. 10.3390/sym11030413 [DOI] [Google Scholar]

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