Startseite Analysis of a fractional-order model for acute and chronic hepatitis-B transmission with Mittag-Leffler kernels
Artikel Open Access

Analysis of a fractional-order model for acute and chronic hepatitis-B transmission with Mittag-Leffler kernels

  • Eric Neebo Wiah , Monica Veronica Crankson , John Awuah Addor und Stephen Edward Moore EMAIL logo
Veröffentlicht/Copyright: 8. Juli 2025
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

This study explores the dynamic characteristics of a fractional-order model for the hepatitis B virus (HBV) epidemic. We present the existence, uniqueness, and Ulam-Hyers stability of solutions for a fractional-order HBV model utilizing the Atangana-Baleanu-Caputo fractional operator with Mittag-Leffler kernels. For the fractional numerical simulations, we employ the Adams-Moulton numerical scheme. The results from the numerical solutions indicate that the parameter χ (order of derivative) plays a crucial role, highlighting the importance of the two infection stages (acute and chronic) within the model. We observed the relationship between the basic reproduction number, the HBV transmission rate, the birth-rate, and the acutely infected individuals to the chronic stage.

MSC 2010: 34D23; 37N25; 92-10

1 Introduction

In recent years, fractional calculus has gained significant attention due to its effectiveness in modeling a myriad of complex problems. In particular, fractional-order models have shown promising results in the study of infectious diseases, such as hepatitis-B infection. The hepatitis B is a viral infection that affects the liver and can lead to both acute and chronic liver infections [14,31,32].

Acute hepatitis-B infection is characterized by the presence of the hepatitis-B virus (HBV) in the blood for less than 6 months. The symptoms of acute hepatitis-B infection include fatigue, abdominal pain, and jaundice. Often, people with acute hepatitis B may have no symptoms or only mild illness. Others with acute hepatitis B may have severe illness that requires hospitalization [15]. Chronic hepatitis-B infection, on the other hand, is defined as the presence of HBV in the blood for more than 6 months after first test. Chronic infection can lead to liver cirrhosis and liver cancer [27,29].

Fractional calculus is an extension of traditional calculus that deals with derivatives and integrals of noninteger order. Fractional-order models are nonlocal and allows for the inclusion of memory effects, which is important in the study of infectious diseases. Hepatitic B fractional models have been studied and discussed in the literature by several authors using Caputo, Caputo-Fabrizio (CF), and the Atangana-Baleanu Caputo (ABC) derivative [10,16]. Mittag-Leffler (M-L) kernels, which are a class of special functions, are used in fractional calculus to represent memory effects [8,22,25,27,29,34]. These kernels have been shown to provide a more accurate representation of diverse phenomena than traditional exponential functions [22].

For example, in [17], the study introduces a novel fractional derivative concept using a nonsingular kernel, aligning with Caputo’s perspective and extending multiple existing versions documented in scholarly works. In addition, they establish a version following the Riemann-Liouville approach and also thoroughly examine the essential characteristics of these newly formulated generalized fractional derivatives, in both the Caputo and Riemann-Liouville contexts.

Also, in [18], they investigated the qualitative properties, including stability, asymptotic stability, and M-L stability, of solutions to fractional differential equations utilizing the newly developed generalized Hattaf fractional derivative. This derivative integrates various prevalent nonsingular kernel fractional derivatives. The research involved constructing a suitable Lyapunov function to understand these properties. Moreover, it introduced a novel numerical method aimed at approximating solutions for such equations, extending the classic Euler numerical scheme applicable to ordinary differential equations. The culmination of the study was the application of these analytical and numerical insights to a biological nonlinear system, specifically in the context of epidemiology.

The present article introduces a new class of generalized differential and integral operators. This class includes and generalizes a large number of definitions of fractal-fractional derivatives and integral operators used to model the complex dynamics of many natural and physical phenomena found in diverse fields of science and engineering. Some properties of the newly introduced class are rigorously established. As applications of this new class, two illustrative examples are presented, one for a simple problem and the other for a nonlinear problem modeling the dynamical behavior of a chaotic system [19].

In addition, [33] offered intriguing findings regarding the virus between domestic dogs and humans, which have not been adequately explored. In this study, they examined the transmission of Ebola between dogs and humans utilizing CF derivatives. The CF fractional derivative is a mathematical approach used to describe nonlocal and memory-dependent behaviors in various systems. Also, [5], they investigated the transmission dynamics of Ebola utilizing the CF fractional operator and simulated the model across different fractional order values, and the two-step Lagrange interpolation method was applied. The existence of a solution for the Ebola model was confirmed through the application of fixed-point theory.

In this work, we present a fractional-order model with M-L kernels for acute and chronic hepatitis-B transmission dynamics. Fractional-order models including fractional-order with M-L kernels have been used to model both acute and chronic hepatitis-B infections [6,8,27,29]. In these models, the number of susceptible individuals, infected individuals, and recovered individuals are represented by fractional differential equations. The M-L kernel is used to represent the memory effect in the system, which allows the model to capture the long-term behavior of the infection [9,13,24,30].

This article is organized as follows: In Section 2, we present the necessary definitions and properties of fractional calculus. In Section 3, we formulate the model and show the existence and uniqueness of positive solutions. In Section 4, we study the existence of equilibria and their local and global stabilities. In order to illustrate our theoretical results, we present the numerical simulations of the model equations given in Section 5. To illustrate our theoretical results, we present the numerical simulations of the model equations given in Section 6. We conclude this article Section 7, which highlights our conclusions and future perspectives.

2 Mathematical preliminary on fractional calculus

In this section, we present and highlight important definitions for development of the HBV model.

2.1 M-L function

In fractional calculus, the M-L function is a direct generalization of e x that is widely used. Following [3,20,23], we will recall some definitions as follows:

Definition 2.1

The one-parameter ( E a ) and two-parameter ( E a , b ) M-L functions in terms of the power series are defined as follows:

E a ( w ) = m = 0 w n Γ ( a m + 1 ) , a > 0

and

E a , b ( w ) = m = 0 w n Γ ( a m + b ) , a > 0 , b > 0 ,

where the function Γ ( w ) is defined as Γ ( w ) = 0 u w 1 e u d u = ( w 1 ) ! , ω > 0 .

2.2 Atangana-Baleanu fractional derivative

In this subsection, we present some important definitions of Atangana-Baleanu fractional derivative and integration in the Caputo (ABC) sense, which will be used in the rest of the article [13,7,9,20,21].

Definition 2.2

The Atangana-Baleanu fractional derivative in Caputo sense of a function ξ ( t ) H 1 ( 0 , c ) , c > 0 with χ ( 0,1 ] is defined as follows:

(1) D 0 , t χ A B C ξ ( t ) = B ( χ ) 1 χ 0 t E χ χ ( t δ ) χ 1 χ ξ ( 1 ) ( δ ) d δ ,

where

H 1 ( 0 , c ) = { ξ ( t ) L 2 ( 0 , c ) ξ ( t ) L 2 ( 0 , c ) } ,

and B ( χ ) = 1 χ + χ Γ ( χ ) satisfying

A B C D b 1 , t χ ξ ( t ) < B ( χ ) 1 χ ξ ( t ) , where ξ ( t ) = max b 1 t b 2 ξ ( t ) ,

for ξ ( t ) C [ b 1 , b 2 ] and the Lipschitz condition

A B C D b 1 , t χ ξ 1 ( t ) A B C D b 2 , t χ ξ 2 ( t ) < ϖ ξ 1 ( t ) ξ 2 ( t ) .

Definition 2.3

The Atangana-Baleanu fractional integral of a function is ξ ( t ) H 1 ( 0 , c ) , with c > 0 is defined as follows:

(2) I 0 , t χ A B C ξ ( t ) = 1 χ B ( χ ) ξ ( t ) + χ B ( χ ) Γ ( χ ) 0 t ξ ( δ ) ( t δ ) χ 1 d δ .

3 Formulation of fractional order HBV model

In recent years, the dynamics of infectious disease modeling have attracted a lot of interest. We consider the model available in [24] in which the total population is denoted by N ( t ) and is divided into four subgroups, namely, susceptible individuals S ( t ) , which denotes individuals vulnerable to the infection; acutely infected individuals A ( t ) ; chronically infected individuals C ( t ) ; and recovered individuals R ( t ) at time t to be

(3) d S ( t ) d t = ρ β S ( t ) C ( t ) ( τ + μ ) S ( t ) d A ( t ) d t = β S ( t ) C ( t ) ( γ + ω + μ ) A ( t ) d C ( t ) d t = ω A ( t ) ( δ + ε + μ ) C ( t ) d R ( t ) d t = δ A ( t ) + ε C ( t ) + τ S ( t ) μ R ( t )

with the initial conditions

S ( 0 ) 0 , A ( 0 ) 0 , C ( 0 ) 0 , R ( 0 ) 0 .

The model parameters are fully described in Table 1.

Table 1

Description of model parameters

Parameters Description
ρ Birth rate
β HBV transmission rate
τ Vaccination rate
μ Natural death rate
ε Constant recovery rate for chronically infected individuals
δ HBV induced death rate
ω Moving rate of acutely infected individuals to the chronic stage
γ Recovery rate for acutely infected individuals

These dynamics are reformulated using the A B C fractional-order derivatives with the fractional parameter 0 < χ 1 . The fractional-order derivative is particularly important as it considers memory effects of the transmission. The fractional-order model is posed as follows:

(4) D 0 , t χ A B C S ( t ) = ρ χ β χ S ( t ) C ( t ) ( τ χ + μ χ ) S ( t ) D 0 , t χ A B C A ( t ) = β χ S ( t ) C ( t ) ( γ χ + ω χ + μ χ ) A ( t ) D 0 , t χ A B C C ( t ) = ω χ A ( t ) ( δ χ + ε χ + μ χ ) C ( t ) D 0 , t χ A B C R ( t ) = δ χ A ( t ) + ε χ C ( t ) + τ χ S ( t ) μ χ R ( t ) ,

with the initial conditions

S ( 0 ) 0 , A ( 0 ) 0 , C ( 0 ) 0 , R ( 0 ) 0 .

3.1 Positivity and Boundedness

Lemma 3.1

The epidemiologically feasible region of the model (4) is follows:

(5) Ω = S ( t ) , A ( t ) , C ( t ) , R ( t ) R + 4 : N ( t ) ρ χ τ χ + μ χ .

Proof

The dynamics of the system’s entire human population in (4) is as follows:

(6) D 0 , t χ A B C N ( t ) ρ χ ( τ χ + μ χ ) N .

By applying the Laplace transform to (6), we obtain

(7) N ( t ) B ( χ ) B ( χ ) + ( 1 χ ) ( τ χ + μ χ ) P ( 0 ) + ( 1 χ ) ρ χ B ( χ ) + ( 1 χ ) ( τ χ + μ χ ) E χ , 1 ( k t χ ) + χ ρ χ B ( χ ) + ( 1 χ ) ( τ χ + μ χ ) E χ , χ + 1 ( k t χ ) ,

where k = χ ( τ χ + μ χ ) B ( χ ) + ( 1 χ ) ( τ χ + μ χ ) and B ( χ ) = 1 χ + χ Γ ( χ ) . By using an asymptotic characteristic of the M-L function [11,21], we observe that N ( t ) ρ χ ( τ χ + μ χ ) as t , and thus, N ( t ) is bounded, and therefore, S ( t ) , A ( t ) , C ( t ) , and R ( t ) are bounded. Hence, Ω is a positive invariant set of system (4).□

4 Stability analysis of fractional-order HBV model

The disease-free equilibrium (DFE) and endemic equilibrium (EE) of the proposed model (9) are obtained from the following system:

(8) D 0 , t χ A B C [ S ( t ) ] = D 0 , t χ A B C [ A ( t ) ] = D 0 , t χ A B C [ C ( t ) ] = D 0 , t χ A B C [ R ( t ) ] = 0 .

The DFE is represented by E 0 = ( S 0 , 0 , 0 , 0 ) , where

S 0 = ρ χ τ χ + μ χ .

Also, the EE is represented by E * = ( S * , A * , C * , R * ) such that

S * ( t ) = ( γ χ + ω χ + μ χ ) A * ( t ) β χ ( δ χ + ε χ + μ χ ) C * ( t ) , A * ( t ) = ( δ χ + ε χ + μ χ ) C * ( t ) ω χ , C * ( t ) = ω χ A * ( t ) ( δ χ + ε χ + μ χ ) , R * ( t ) = ρ χ ( τ χ + μ χ ) μ χ .

4.1 Basic reproduction number 0

The basic reproduction number denoted by R 0 is an important threshold in epidemic modeling. It can predict whether the disease will remain or die out in a population. If 0 < 1 , one infected person can infect less than one person, and therefore, the disease will be eliminated with time. If 0 > 1 , one infected individual will infect more than one susceptible individual on average, and hence, the disease will spread in the community [12]. To obtain 0 , we employ transmission and translation matrices F ( x ) and V ( x ) , respectively,

(9) F = β χ S ( t ) C ( t ) 0 , and V = ( γ χ + ω χ + μ χ ) A ( t ) ω χ A ( t ) ( δ χ + ε χ + μ χ ) C ( t ) ,

we calculate and V as follows:

(10) = 0 β χ ρ χ τ χ + μ χ 0 0 , and V = ( γ χ + ω χ + μ χ ) 0 ω χ ( δ χ + ε χ + μ χ ) .

As we know that, basic reproduction number 0 , is the spectral radius of the matrix V 1 , and it is written as follows:

(11) 0 = β χ ρ χ ω χ ( γ χ + ω χ + μ χ ) ( δ χ + ε χ + μ χ ) ( τ χ + μ χ ) .

The infection will increase if there is an increase in the HBV transmission rate β , the birth rate ρ , and the transmission rate of acutely infected individuals to the chronic stage, ω . On the other hand, the infection will decreases if the recovery rate for acutely infected individuals, γ , the recovery rate for chronically infected individuals ε , the HBV induced death rate δ , the natural death rate, μ , and the vaccination rate τ increases.

4.2 Local and global stability of DFE and DEE points

We establish the local stability of system (4) in this section at HBV free point E 0 as well as at HBV present equilibrium point.

Theorem 4.1

The HBV free equilibrium (DFE) point E 0 of the proposed fractional order model (4) is locally asymptotically stable (LAS) if 0 < 1 and unstable if 0 > 1 .

Proof

The Jacobian matrix of system (4) at E 0 is expressed as follows:

(12) J ( E 0 ) = ( τ χ + μ χ ) 0 β χ ρ χ ( τ χ + μ χ ) 0 0 ( γ χ + ω χ + μ χ ) β χ ρ χ ( τ χ + μ χ ) 0 0 ω χ ( δ χ + ε χ + μ χ ) 0 τ χ ω χ δ χ μ χ .

Therefore, by the Routh-Hurwitz stability conditions for fractional-order systems [4], the necessary and sufficient condition is

(13) arg ( eig ( J ) ) = arg ( w i ) > κ π 2 ,

for various fractional order models. Therefore, the disease-free equilibrium of system (4) is asymptotically stable if all of the eigenvalues w i , i = 1 , , 4 of J ( E 0 ) satisfy condition (13). Hence, a sufficient condition for the local asymptotic stability of the equilibrium points is that the eigenvalues w i , i = 1 , , 4 , of the Jacobian matrix J ( E 0 ) satisfy the condition a r g ( w i ) > κ π 2 . This confirms that fractional-order differential equations are, at least, as stable as their integer-order counterparts. The characteristic equation of J ( E 0 ) is expressed as follows:

(14) ( ( τ χ + μ χ ) λ χ ) ( μ χ λ χ ) ( ( λ χ ) 2 + λ χ P + Q ) = 0 ,

where P = ( γ χ + ω χ + μ χ ) + ( δ χ + ε χ + μ χ ) , and Q = ( γ χ + ω χ + μ χ ) ( δ χ + ε χ + μ χ ) ( 1 0 ) . For 0 < 1 , the quadratic equation ( ( λ χ ) 2 + λ χ P + Q ) = 0 has all terms positive, and thus, its roots must all be negative. Using the Routh-Hurwitz criteria for fractional-order models, all the eigenvalues have negative real parts and satisfy the condition given by (13). Therefore, all the eigenvalues have negative real parts if 0 < 1 . This completes the proof.□

Theorem 4.2

Given χ ( 0 , 1 ] and 0 1 . Thus, the DFE of system (4) is globally asymptotically stable (GAS) on a positively invariant region Ω .

Proof

Consider a suitable Lyapunov function of the form

(15) L ( t ) = ( S ( t ) S ( 0 ) ) + A ( t ) + C ( t ) + R ( t ) .

By applying the Atangana-Baleanu Caputo derivative on (15), we obtain

(16) D 0 , t χ A B C L ( t ) = ( D 0 , t χ A B C S ( t ) D 0 , t χ A B C S ( 0 ) ) + D 0 , t χ A B C A ( t ) + D 0 , t χ A B C C ( t ) + D 0 , t χ A B C R ( t ) .

Exploiting equations (4) and (15) gives

(17) D 0 , t χ A B C L ( t ) = ( τ χ + μ χ ) ( S ( t ) S ( 0 ) ) ( γ χ + ω χ + μ χ ) A ( t ) ( δ χ + ε χ + μ χ ) C ( t ) μ χ R ( t ) , = ( ( τ χ + μ χ ) ( S ( t ) S ( 0 ) ) + ( γ χ + ω χ + μ χ ) A ( t ) + ( δ χ + ε χ + μ χ ) C ( t ) μ χ R ( t ) ) 0 .

Hence, D 0 , t χ A B C L ( t ) 0 for 0 0 , and D 0 , t χ A B C L ( t ) = 0 if and only if S ( t ) = S ( 0 ) and A ( t ) = C ( t ) = R ( t ) = 0 . Thus, the largest compact invariant set is { ( S , A , C , R ) R 4 : A B C D t χ L ( t ) = 0 } .

Theorem 4.3

The HBV EE point E * of the proposed fractional order model (4) is LAS if 0 > 1 and is unstable if 0 < 1 .

Proof

In a similar way as in Theorem 4.1, the Jacobian matrix can be written as follows:

(18) J ( E * ) = β χ ω χ A * ( t ) ( δ χ + ε χ + μ χ ) ( τ χ + μ χ ) 0 β χ ( γ χ + ω χ + μ χ ) A * ( t ) ( δ χ + ε χ + μ χ ) C * ( t ) 0 β χ ω χ A * ( t ) ( δ χ + ε χ + μ χ ) ( γ χ + ω χ + μ χ ) β χ ( γ χ + ω χ + μ χ ) A * ( t ) ( δ χ + ε χ + μ χ ) C * ( t ) 0 0 ω χ ( δ χ + ε χ + μ χ ) 0 τ χ ω χ δ χ μ χ .

Therefore, by the Routh-Hurwitz stability conditions for fractional order systems [4,22], the necessary and sufficient condition for stability is

(19) arg ( eig ( J * ) ) = arg ( w i ) > κ π 2 ,

for various fractional order models. Therefore, the EE of system (4) is asymptotically stable if all the eigenvalues w i , i = 1 , , 4 of J * ( E * ) satisfy condition (19). Hence, a sufficient condition for the local asymptotic stability of the equilibrium points is that the eigenvalues w i , i = 1 , , 4 of the Jacobian matrix satisfy the condition arg ( w i ) > κ π 2 . This confirms that fractional order differential equations are, at least, as stable as their integer-order counterparts. The characteristic equation of J * ( E * ) is expressed as follows:

(20) ( μ χ λ χ ) ( λ 3 + B λ 2 + D λ + E ) = 0 .

The aforementioned equation gives one negative eigenvalue λ 1 χ = μ χ . The other eigenvalues can be obtained from the following equation:

(21) λ 3 + B λ 2 + D λ + E = 0 ,

where

(22) B = β χ ω χ A * ( t ) ( δ χ + ε χ + μ χ ) + ( τ χ + μ χ ) + ( γ χ + ω χ + μ χ ) + ( δ χ + ε χ + μ χ ) , D = β χ ω χ A * ( t ) ( γ χ + ω χ + μ χ ) ( δ χ + ε χ + μ χ ) + β χ ω χ A * ( t ) + ( τ χ + μ χ ) ( γ χ + ω χ + μ χ ) + ( τ χ + μ χ ) ( δ χ + ε χ + μ χ ) + ( γ χ + ω χ + μ χ ) ( δ χ + ε χ + μ χ ) + β χ ω χ A * ( t ) ( γ χ + ω χ + μ χ ) ( τ χ + μ χ ) ( δ χ + ε χ + μ χ ) C * ( t ) , E = β χ ω χ A * ( t ) ( γ χ + ω χ + μ χ ) ( τ χ + μ χ ) ( δ χ + ε χ + μ χ ) C * ( t ) 1 ( γ χ + ω χ + μ χ ) A * ( t ) ρ χ > 0 .

It is clear that B , D , E > 0 whenever 0 > 1 . Also,

(23) B D E = β χ ω χ A * ( t ) ( δ χ + ε χ + μ χ ) + ( τ χ + μ χ ) + ( γ χ + ω χ + μ χ ) + ( δ χ + ε χ + μ χ ) × β χ ω χ A * ( t ) ( γ χ + ω χ + μ χ ) ( δ χ + ε χ + μ χ ) + β χ ω χ A * ( t ) + ( τ χ + μ χ ) ( γ χ + ω χ + μ χ ) + ( τ χ + μ χ ) ( δ χ + ε χ + μ χ ) + ( γ χ + ω χ + μ χ ) ( δ χ + ε χ + μ χ ) + β χ ω χ A * ( t ) ( γ χ + ω χ + μ χ ) ( τ χ + μ χ ) ( δ χ + ε χ + μ χ ) C * ( t ) × β χ ω χ A * ( t ) ( γ χ + ω χ + μ χ ) ( τ χ + μ χ ) ( δ χ + ε χ + μ χ ) C * ( t ) 1 ( γ χ + ω χ + μ χ ) A * ( t ) ρ χ > 0 .

Therefore, by Routh-Hurwitz criteria, all roots of (21) have negative real parts if and only if 0 > 1 . Therefore, E * is LAS when ever 0 > 1 .

Theorem 4.4

The DEE point E * of the proposed fractional-order model (4) is GAS whenever 0 1 .

Proof

To study the global stability of E * for (4), we propose the following Lyapunov function:

(24) L * ( t ) = 1 2 [ ( S ( t ) S * ) + ( A ( t ) A * ) + ( C ( t ) C * ) + ( R ( t ) R * ) ] 2 .

Now, we calculate the derivative with respect to time of (31) and then using model (9), we obtain

(25) D 0 , t χ A B C L * ( t ) = [ ( S S * ) + ( A A * ) + ( C C * ) + ( R R * ) ] 2 * × [ ρ χ ( τ χ + μ χ ) S ( γ χ + ω χ + μ χ ) A ( δ χ + ε χ + μ χ ) C μ χ R ] .

If we put Φ 1 = ( τ χ + μ χ ) , Φ 2 = ( γ χ + ω χ + μ χ ) , Φ 3 = ( δ χ + ε χ + μ χ ) , and Φ 4 = μ χ . Then, after simple arrangements, we obtain

(26) D 0,1 χ A B C L * = [ ( S S * ) + ( A A * ) + ( C C * ) + ( R R * ) ] * [ S * R 0 Φ 1 S Φ 1 A Φ 2 C Φ 3 R Φ 4 ] , D 0,1 χ A B C L * = [ ( S S * ) + ( A A * ) + ( C C * ) + ( R R * ) ] * [ S * R 0 Φ 1 + S Φ 1 + A Φ 2 + C Φ 3 + R Φ 4 ] , D 0,1 χ A B C L * = [ ( S S * ) + ( A A * ) + ( C C * ) + ( R R * ) ] * [ ( S * R 0 + S ) Φ 1 + A Φ 2 + C Φ 3 + R Φ 4 ] .

Since the right-hand side of system (26) has a negative sign, the derivative on right-hand side is less than or equal to zero, i.e., D 0,1 χ A B C L * 0 . Substituting S = S * , A = A * , C = C * , and R = R * in (26), D 0,1 χ A B C L * yields zero, i.e., D 0,1 χ A B C L * = 0 . Therefore, the largest invariant set in { S ( t ) , A ( t ) , C ( t ) , R ( t ) R 4 : D 0,1 χ A B C L * = 0 } is the singleton invariant set E * , where E * is the DEE point. Then, by applying invariant principle of LaSalle [26], it implies that E * is globally asymptotically stable.□

4.3 Existence and uniqueness

We denote a Banach space by D ( V ) with V = [ 0 , b ] containing a real-valued continuous function with sup norm and P = D ( V ) × D ( V ) × D ( V ) × D ( V ) with norm ( S , A , C , R ) = S + A + C + R , where S = sup t J S ( t ) , A = sup t J A ( t ) , C = sup t J C ( t ) and R = sup t J R ( t ) . By using the ABC integral operator on model (4), we obtain

(27) S ( t ) S ( 0 ) = D 0 , t χ A B C [ S ( t ) ] { ρ χ β χ S ( t ) C ( t ) ( τ χ + μ χ ) S ( t ) } ; A ( t ) A ( 0 ) = D 0 , t χ A B C [ A ( t ) ] { β χ S ( t ) C ( t ) ( γ χ + ω χ + μ χ ) A ( t ) } ; C ( t ) C ( 0 ) = D 0 , t χ A B C [ C ( t ) ] { ω χ A ( t ) ( δ χ + ε χ + μ χ ) C ( t ) } ; R ( t ) R ( 0 ) = D 0 , t χ A B C [ R ( t ) ] { δ χ A ( t ) + ε χ C ( t ) + τ χ S ( t ) μ χ R ( t ) } .

Now, by using equation (1), we obtain

(28) S ( t ) S ( 0 ) = 1 χ B ( χ ) κ 1 ( χ , t , S ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , ξ , S ( ξ ) ) d ξ , A ( t ) A ( 0 ) = 1 χ B ( χ ) κ 2 ( χ , t , A ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 2 ( χ , ξ , A ( ξ ) ) d ξ , C ( t ) C ( 0 ) = 1 χ B ( χ ) κ 3 ( χ , t , C ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 3 ( χ , ξ , C ( ξ ) ) d ξ , R ( t ) R ( 0 ) = 1 χ B ( χ ) κ 4 ( χ , t , R ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 4 ( χ , ξ , R ( ξ ) ) d ξ ,

where

(29) κ 1 ( χ , t , S ( t ) ) = ρ χ β χ S ( t ) C ( t ) ( τ χ + μ χ ) S ( t ) , κ 2 ( χ , t , A ( t ) ) = β χ S ( t ) C ( t ) ( γ χ + ω χ + μ χ ) A ( t ) , κ 3 ( χ , t , C ( t ) ) = ω χ A ( t ) ( δ χ + ε χ + μ χ ) C ( t ) , κ 4 ( χ , t , R ( t ) ) = δ χ A ( t ) + ε χ C ( t ) + τ χ S ( t ) μ χ R ( t ) .

Here, κ 1 , κ 2 , κ 3 , and κ 4 satisfy the Lipschitz condition only if S ( t ) , A ( t ) , C ( t ) , and R ( t ) possess an upper bound. Supposing ( t ) and S * ( t ) are couple functions, we have

κ 1 ( χ , t , S ( t ) ) κ 1 ( χ , t , S * ( t ) ) = β χ S ( t ) C ( t ) ( τ χ + μ χ ) ( S ( t ) S * ( t ) ) .

Considering

η 1 = ( β χ S ( t ) C ( t ) + ( τ χ + μ χ ) ) ,

we obtain

(30) κ 1 ( χ , t , S ( t ) ) κ 1 ( χ , t , S * ( t ) ) = η 1 S ( t ) S * ( t ) .

Similarly,

(31) κ 2 ( χ , t , A ( t ) ) κ 2 ( χ , t , A * ( t ) ) = η 2 A ( t ) A * ( t ) , κ 3 ( χ , t , C ( t ) ) κ 3 ( χ , t , C * ( t ) ) = η 3 C ( t ) C * ( t ) , κ 4 ( χ , t , R ( t ) ) κ 4 ( χ , t , R * ( t ) ) = η 4 R ( t ) R * ( t ) .

where η 2 = ( γ χ + ω χ + μ χ ) , η 3 = ( δ χ + ε χ + μ χ ) , and η 4 = 7 ( μ χ ) , which shows that the Lipschitz condition holds. Continuing in a recursive manner, (28) gives us the following equation:

(32) S n ( t ) S ( 0 ) = 1 χ B ( χ ) κ 1 ( χ , t , S n 1 ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , ξ , S n 1 ( ξ ) ) d ξ , A n ( t ) A ( 0 ) = 1 χ B ( χ ) κ 2 ( χ , t , A n 1 ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 2 ( χ , ξ , A n 1 ( ξ ) ) d ξ , C n ( t ) C ( 0 ) = 1 χ B ( χ ) κ 3 ( χ , t , C n 1 ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 3 ( χ , ξ , C n 1 ( ξ ) ) d ξ , R n ( t ) R ( 0 ) = 1 χ B ( χ ) κ 4 ( χ , t , R n 1 ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 4 ( χ , ξ , R n 1 ( ξ ) ) d ξ ,

together with the initial condition S 0 ( t ) = S ( 0 ) , A 0 ( t ) = A ( 0 ) , C 0 ( t ) =C(0), and R 0 ( t ) = R ( 0 ) . Difference between consecutive terms yields

(33) Ξ S , n ( t ) = S n ( t ) S n 1 ( t ) = 1 χ B ( χ ) ( κ 1 ( χ , t , S n 1 ( t ) ) κ 1 ( χ , t , S n 2 ( t ) ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 ( κ 1 ( χ , ξ , S n 1 ( ξ ) ) κ 1 ( χ , ξ , S n 2 ( ξ ) ) ) d ξ , Ξ A , n ( t ) = A n ( t ) A n 1 ( t ) = 1 χ B ( χ ) ( κ 1 ( χ , t , A n 1 ( t ) ) κ 1 ( χ , t , A n 2 ( t ) ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 ( κ 1 ( χ , ξ , A n 1 ( ξ ) ) κ 1 ( χ , ξ , A n 2 ( ξ ) ) ) d ξ , Ξ C , n ( t ) = C n ( t ) C n 1 ( t ) = 1 χ B ( χ ) ( κ 1 ( χ , t , C n 1 ( t ) ) κ 1 ( χ , t , C n 2 ( t ) ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 ( κ 1 ( χ , ξ , C n 1 ( ξ ) ) κ 1 ( χ , ξ , C n 2 ( ξ ) ) ) d ξ , Ξ R , n ( t ) = R n ( t ) R n 1 ( t ) = 1 χ B ( χ ) ( κ 1 ( χ , t , R n 1 ( t ) ) κ 1 ( χ , t , R n 2 ( t ) ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 ( κ 1 ( χ , ξ , R n 1 ( ξ ) ) κ 1 ( χ , ξ , R n 2 ( ξ ) ) ) d ξ .

We note that

(34) S n ( t ) = i = 0 n Ξ S , i ( t ) , A n ( t ) = i = 0 n Ξ A , i ( t ) , C n ( t ) = i = 0 n Ξ C , i ( t ) , R n ( t ) = i = 0 n Ξ R , i ( t ) .

Taking into account equations (38)–(39), and considering that

(35) Ξ S , n 1 ( t ) = S n 1 ( t ) S n 2 ( t ) , Ξ A , n 1 ( t ) = A n 1 ( t ) A n 2 ( t ) , Ξ C , n 1 ( t ) = C n 1 ( t ) C n 2 ( t ) , Ξ R , n 1 ( t ) = R n 1 ( t ) R n 2 ( t ) ,

we obtain

(36) Ξ S , n ( t ) 1 χ B ( χ ) η 1 Ξ S , n 1 ( t ) χ B ( χ ) Γ ( χ ) η 1 0 t ( t ξ ) χ 1 Ξ S , n 1 ( ξ ) d ξ , Ξ A , n ( t ) 1 χ B ( χ ) η 2 Ξ A , n 1 ( t ) χ B ( χ ) Γ ( χ ) η 2 0 t ( t ξ ) χ 1 Ξ A , n 1 ( ξ ) d ξ , Ξ C , n ( t ) 1 χ B ( χ ) η 3 Ξ C , n 1 ( t ) χ B ( χ ) Γ ( χ ) η 3 0 t ( t ξ ) χ 1 Ξ C , n 1 ( ξ ) d ξ , Ξ R , n ( t ) 1 χ B ( χ ) η 4 Ξ R , n 1 ( t ) χ B ( χ ) Γ ( χ ) η 4 0 t ( t ξ ) χ 1 Ξ R , n 1 ( ξ ) d ξ .

Theorem 4.5

System (4) has a unique solution for t [ 0 , b ] subject to the condition if

(37) 1 χ B ( χ ) η i + χ B ( χ ) Γ ( χ ) b χ η i , i = 1 , 2 , 3 , 4 ,

holds.

Proof

S ( t ) , A ( t ) , C ( t ) , and R ( t ) are bounded functions, and equations (30)–(31) hold, in a recursive manner equation (36) leads to

(38) Ξ S , n ( t ) S 0 ( t ) ( 1 χ B ( χ ) η 1 + χ B ( χ ) Γ ( χ ) b χ η 1 ) n , Ξ A , n ( t ) A 0 ( t ) ( 1 χ B ( χ ) η 2 + χ B ( χ ) Γ ( χ ) b χ η 2 ) n , Ξ C , n ( t ) C 0 ( t ) ( 1 χ B ( χ ) η 3 + χ B ( χ ) Γ ( χ ) b χ η 3 ) n , Ξ R , n ( t ) R 0 ( t ) ( 1 χ B ( χ ) η 4 + χ B ( χ ) Γ ( χ ) b χ η 4 ) n .

So Ξ S , n 0 , Ξ A , n 0 , Ξ C , n 0 and Ξ R , n 0 as n . Incorporating the triangle inequality, and for any k , (38) yields

(39) S n + k ( t ) S n ( t ) j = n + 1 n + k Z 1 j = Z 1 n + 1 Z 1 n + k + 1 1 Z 1 , A n + k ( t ) A n ( t ) j = n + 1 n + k Z 2 j = Z 2 n + 1 Z 2 n + k + 1 1 Z 2 , C n + k ( t ) C n ( t ) j = n + 1 n + k Z 3 j = Z 3 n + 1 Z 3 n + k + 1 1 Z 3 , R n + k ( t ) R n ( t ) j = n + 1 n + k Z 4 j = Z 4 n + 1 Z 4 n + k + 1 1 Z 4 ,

with Z i = 1 χ B ( χ ) η i + χ B ( χ ) Γ ( χ ) b χ η i < 1 by hypothesis. Similar to the method mentioned in [13,28], we can obtain the existence of a unique solution for system (4).□

5 Hyers-Ulam stability

Definition 5.1

[1,3,6] The ABC fractional integral system given by (35) is said to be Hyers-Ulam stable if there exist constants Δ i > 0 , i R 4 satisfying: For every θ i > 0 , i R 4 , for

(40) S ( t ) = 1 χ B ( χ ) κ 1 ( χ , t , S ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , ξ , S ( ξ ) ) d ξ θ 1 , A ( t ) = 1 χ B ( χ ) κ 2 ( χ , t , A ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 2 ( χ , ξ , A ( ξ ) ) d ξ θ 2 , C ( t ) = 1 χ B ( χ ) κ 3 ( χ , t , C ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 3 ( χ , ξ , C ( ξ ) ) d ξ θ 3 , R ( t ) = 1 χ B ( χ ) κ 4 ( χ , t , R ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 4 ( χ , ξ , R ( ξ ) ) d ξ θ 4 ,

there exist ( S ( t ) , A ( t ) , C ( t ) , and R ( t ) ) , which satisfy

(41) S ( t ) = 1 χ B ( χ ) κ 1 ( χ , t , S ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , ξ , S ( ξ ) ) d ξ , A ( t ) = 1 χ B ( χ ) κ 2 ( χ , t , A ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 2 ( χ , ξ , A ( ξ ) ) d ξ , C ( t ) = 1 χ B ( χ ) κ 3 ( χ , t , C ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 3 ( χ , ξ , C ( ξ ) ) d ξ , R ( t ) = 1 χ B ( χ ) κ 4 ( χ , t , R ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 4 ( χ , ξ , R ( ξ ) ) d ξ ,

such that

(42) S ( t ) S ˙ ( t ) ζ 1 θ 1 , A ( t ) A ˙ ( t ) ζ 2 θ 2 , C ( t ) C ˙ ( t ) ζ 3 θ 3 , R ( t ) R ˙ ( t ) ζ 4 θ 4 .

Theorem 5.2

Model (4) is Hyers-Ulam stable subject to the Jacobian (J) of system (4).

Proof

Following Theorem 4.5, the proposed ABC fractional model (4) has a unique solution ( S ( t ) , A ( t ) , C ( t ) , R ( t ) ) satisfying (28). Then, we have

(43) S ( t ) S ˙ ( t ) 1 χ B ( χ ) κ 1 ( χ , t , S ( t ) ) κ 1 ( χ , t , S ˙ ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , t , S ( ξ ) ) κ 1 ( χ , t , S ˙ ( ξ ) ) d ξ 1 χ B ( χ ) + χ B ( χ ) Γ ( χ ) χ 1 S S ˙ ,

(44) A ( t ) A ˙ ( t ) 1 χ B ( χ ) κ 1 ( χ , t , A ( t ) ) κ 1 ( χ , t , A ˙ ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , t , A ( ξ ) ) κ 1 ( χ , t , A ˙ ( ξ ) ) d ξ 1 χ B ( χ ) + χ B ( χ ) Γ ( χ ) χ 1 A A ˙ ,

(45) C ( t ) C ˙ ( t ) 1 χ B ( χ ) κ 1 ( χ , t , C ( t ) ) κ 1 ( χ , t , C ˙ ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , t , C ( ξ ) ) κ 1 ( χ , t , C ˙ ( ξ ) ) d ξ 1 χ B ( χ ) + χ B ( χ ) Γ ( χ ) χ 1 C C ˙ ,

(46) R ( t ) R ˙ ( t ) 1 χ B ( χ ) κ 1 ( χ , t , R ( t ) ) κ 1 ( χ , t , R ˙ ( t ) ) + χ B ( χ ) Γ ( χ ) 0 t ( t ξ ) χ 1 κ 1 ( χ , t , R ( ξ ) ) κ 1 ( χ , t , R ˙ ( ξ ) ) d ξ 1 χ B ( χ ) + χ B ( χ ) Γ ( χ ) χ 1 R R ˙ .

Taking θ i = χ i , Δ i = 1 χ B ( χ ) + χ B ( χ ) Γ ( χ ) implies

(47) S ( t ) S ˙ ( t ) θ 1 Δ 1 .

Similarly, we derive estimates for the rest

(48) A ( t ) A ˙ ( t ) θ 2 Δ 2 , C ( t ) C ˙ ( t ) θ 3 Δ 3 , R ( t ) R ˙ ( t ) θ 4 Δ 4 .

System (28) is Hyers-Ulam stable by taking into account (47) and (48), and hence, model (4) is Hyers-Ulam stable.□

6 Numerical simulations

In this section, we present numerical simulations to illustrate our theoretical results. The numerical method employed to solve the model problem (4) is hinged on the Adams-Moulton Rule based on the M-L kernel for the ABC fractional derivative approximation [26]. The approximate solutions of (4) with 0 < χ 1 are displayed in Figures 2 and 3. The solutions converge to the equilibrium points. The parameter values used in the simulations for Figures 2 are as follows: ρ = 0.032 , β = 2.005 , τ = 0.7 , μ = 0.152 , ω = 0.09 , γ = 0.03 , δ = 0.005 , and ε = 0.07 . By using the MATLAB numerical computing environment, we obtain 0 = 0.0041 . Hence, system (4) has a unique disease-free equilibrium. According to Theorem 4.2, E 0 is globally asymptotically stable (Figure 1(a)). Also, the parameter values used in the simulations for Figure 3 are as follows: ρ = 2 , β = 0.6 , τ = 0.001 , μ = 0.2 , ω = 0.5 , γ = 0.2 , δ = 0.4 , and ε = 0.5 with 0 = 2.1474 . Hence, the condition E * is LAS whenever 0 > 1 is satisfied, as well as Theorem 4.4 (Figure 1(b)).

Figure 1 
               Simulation of the dynamics of the compartments; 
                     
                        
                        
                           S
                           
                              (
                              
                                 t
                              
                              )
                           
                           ,
                           A
                           
                              (
                              
                                 t
                              
                              )
                           
                           ,
                           C
                           
                              (
                              
                                 t
                              
                              )
                           
                           ,
                        
                        S\left(t),A\left(t),C\left(t),
                     
                   and 
                     
                        
                        
                           R
                           
                              (
                              
                                 t
                              
                              )
                           
                        
                        R\left(t)
                     
                   of our HBV model (4). The simulation result of DFE point (left) and simulation result depicting DEE point (right). Source: created by authors
Figure 1

Simulation of the dynamics of the compartments; S ( t ) , A ( t ) , C ( t ) , and R ( t ) of our HBV model (4). The simulation result of DFE point (left) and simulation result depicting DEE point (right). Source: created by authors

Figure 2 
               Numerical simulation of the HBV-free equilibrium for varying fractional-order values of 
                     
                        
                        
                           χ
                        
                        \chi 
                     
                   = [1.0, 0.95, 0.9, 0.85, 0.8, 0.75]. Source: created by authors
Figure 2

Numerical simulation of the HBV-free equilibrium for varying fractional-order values of χ = [1.0, 0.95, 0.9, 0.85, 0.8, 0.75]. Source: created by authors

Figure 3 
               Stability of the HBV EE for different values of 
                     
                        
                        
                           χ
                        
                        \chi 
                     
                   = [1.0, 0.95, 0.9, 0.85, 0.8, 0.75]. Source: created by authors
Figure 3

Stability of the HBV EE for different values of χ = [1.0, 0.95, 0.9, 0.85, 0.8, 0.75]. Source: created by authors

Figure 2 illustrates the outcomes of numerical simulations derived from the M-L generalized function, which is distinguished by its crossover property when transitioning between different operators. This operator possesses a statistical representation, enhancing its applicability. In the graph representing susceptible individuals, it is observed that as the fractional order value χ increases, the count of susceptible individuals diminishes and approaches zero asymptotically. Furthermore, in the graphs for acutely and chronically infected individuals, the number of infected individuals initially increases before subsequently declining as the fractional order derivative χ increases at the HBV-free equilibrium.

7 Conclusion

The fractional analysis of acute and chronic HBV encompasses the evaluation of various biological markers, stages of infection, and the body’s responses to the virus. This analysis is crucial for public health, as it significantly contributes to diagnosis, disease management, prevention strategies, and the understanding of disease progression. Overall, the fractional analysis of acute and chronic HBV infections is essential for formulating targeted public health initiatives. It facilitates the efficient allocation of resources, minimizes transmission risks, prevents complications, and enhances the overall health of the population. The model captures the memory effects and long-range interactions that are present in the infection and can be used to investigate the effectiveness of various treatment strategies. The use of fractional calculus in infectious disease modeling is a promising area of research that has the potential to lead to new insights into the dynamics of infectious diseases and to the development of more effective treatment strategies. The local and global stability of both DFE and DEE points are demonstrated by utilizing various criteria and conditions. The generalized Adams-Moulton method is employed to present the numerical scheme. Large-scale numerical simulations are performed by varying the value of the parameter χ (order of derivative) to observe the effects that the parameter has on the dynamics of the proposed model. The fractional operator χ utilized is particularly well suited for examining the transmission dynamics of hepatitis B diseases as presented in the literature. Consequently, several significant characteristics of the proposed fractional model have been identified, including model formulation, the existence and uniqueness of solutions as established by the fixed-point theorem, invariant regions, stability analysis, and, crucially, the basic reproduction number. It is important to highlight that the fractional disease model under consideration provides a more accurate representation of disease behavior compared to its integer-order counterpart.

Acknowledgment

We would like to thank Daniel Bentil who passed away on January 17, 2025, for his emence contribution to the writing, editing, and reviewing of the article.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results, and approved the final version of the manuscript. ENW – conceptualization, methodology, software, investigation, formal analysis, validation, writing – original draft. MVC – formal analysis, validation, writing-review & editing. JAA – validation, supervision, writing-review & editing. SEM - conceptualization, methodology, software, investigation, formal analysis, validation, writing – original draft.

  3. Conflict of interest: The authors have no conflict of interest to disclose.

  4. Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

  5. Use of AI tools declaration: The authors declare that they have not used Artificial Intelligence (AI) tools in the creation of this article.

References

[1] Abdeljawad, T. (2017). A lyapunov type inequality for fractional operators with nonsingular mittag-leffler kernel. Journal of Inequalities and Applications, 2017(1), 130. DOI: https://doi.org/10.1186/s13660-017-1400-5.Suche in Google Scholar PubMed PubMed Central

[2] Abdeljawad, T. (2019). Fractional operators with generalized Mittag-Leffler kernels and their iterated differintegrals. Chaos, 29(2), 023102. DOI: https://doi.org/10.1063/1.5085726.Suche in Google Scholar PubMed

[3] Abdeljawad, T., & Baleanu, D. (2018). On fractional derivatives with generalized Mittag-Leffler kernels. Advances in Difference Equations, 2018(1), 468. DOI: https://doi.org/10.1186/s13662-018-1914-2.Suche in Google Scholar

[4] Addai, E., Adeniji, A., Ngungu, M., Tawiah, G. K., Marinda, E., Asamoah, J. K. K., & Khan, M. A. (2023). A nonlinear fractional epidemic model for the marburg virus transmission with public health education. Scientific Reports, 13(1), 19292. DOI: https://doi.org/10.1038/s41598-023-47506-w.Suche in Google Scholar PubMed PubMed Central

[5] Adu, I. K., Wireko, F. A., Osman, M. A.-R. E.-N., & Asamoah, J. K. K. (2024). A fractional order Ebola transmission model for dogs and humans. Scientific African, 24, e02230, DOI: https://doi.org/10.1016/j.sciaf.2024.e02230.Suche in Google Scholar

[6] Atangana, A. (2017). Fractal-fractional differentiation and integration: Connecting fractal calculus and fractional calculus to predict complex system. Chaos, Solitons & Fractals, 102, 396–406. Future Directions in Fractional Calculus Research and Applications. DOI: https://doi.org/10.1016/j.chaos.2017.04.027.Suche in Google Scholar

[7] Atangana, A., & Baleanu, D. (2016). New fractional derivatives with non-local and non-singular kernel: Theory and application to heat transfer model. Thermal Science, 20(2), 763–769. DOI: https://doi.org/10.2298/TSCI160111018A.Suche in Google Scholar

[8] Bachraoui, M., Ait Ichou, M., Hattaf, K., & Yousfi, N. (2021). Spatiotemporal dynamics of a fractional model for hepatitis b virus infection with cellular immunity. Mathematical Modelling of Natural Phenomena, 16, 5. DOI: https://doi.org/10.1007/978-3-030-73241-7_19.Suche in Google Scholar

[9] Caputo, M., & Fabrizio, M. (2015). A new definition of fractional derivative without singular kernel. Progress in Fractional Differentiation & Applications, 1(2), 73–85. DOI: https://doi.org/10.12785/pfda/010201.Suche in Google Scholar

[10] Cardoso, L. C., Dos Santos, F. L. P., & Camargo, R. F. (2018). Analysis of fractional-order models for hepatitis B. Computational and Applied Mathematics, 37(4), 4570–4586. DOI: https://doi.org/10.1007/s40314-018-0588-4.Suche in Google Scholar

[11] Dasumani, M., Moore, S. E., Gathungu, D. K., & Diallo, B. (2024). A nonlinear fractional fishery resource system model with Crowley-Martin functional response under Mittag-Leffler kernel. Results in Control and Optimization, 16, 100461. DOI: https://doi.org/10.1016/j.rico.2024.100461.Suche in Google Scholar

[12] Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the definition and the computation of the basic reproduction ratio r0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology, 28(4), 365–382. DOI: https://doi.org/10.1007/BF00178324.Suche in Google Scholar PubMed

[13] Diethelm, K., Ford, N. J., & Freed, A. D. (2004). Detailed error analysis for a fractional Adams method. Numerical Algorithms, 36(1), 31–52. DOI: https://doi.org/10.1023/B:NUMA.0000027736.85078.be.Suche in Google Scholar

[14] Din, A., Li, Y., & Liu, Q. (2020). Viral dynamics and control of hepatitis B virus (HBV) using an epidemic model. Alexandria Engineering Journal, 59(2), 667–679. DOI: https://doi.org/10.1016/j.aej.2020.01.034.Suche in Google Scholar

[15] Centers for Disease Control and Prevention. Hepatitis B. 2025. Available: https://www.cdc.gov/hepatitis-b.Suche in Google Scholar

[16] Gul, N., Bilal, R., Algehyne, E. A., Alshehri, M. G., Khan, M. A., Chu, Y.-M., & Islam, S. (2021). The dynamics of fractional order hepatitis b virus model with asymptomatic carriers. Alexandria Engineering Journal, 60(4), 3945–3955. DOI: https://doi.org/10.1016/j.aej.2021.02.057.Suche in Google Scholar

[17] Hattaf, K. (2020). A new generalized definition of fractional derivative with non-singular kernel. Computation, 8(2), 49. DOI: https://doi.org/10.3390/computation8020049.Suche in Google Scholar

[18] Hattaf, K. (2022). On the stability and numerical scheme of fractional differential equations with application to biology. Computation, 10(6), 97. DOI: https://doi.org/10.3390/computation10060097. Suche in Google Scholar

[19] Hattaf, K. (2023). A new class of generalized fractal and fractal-fractional derivatives with non-singular kernels. Fractal and Fractional, 7(5), 395. DOI: https://doi.org/10.3390/fractalfract7050395. Suche in Google Scholar

[20] Heydarpour, Z., Parizi, M. N., Ghorbnian, R., Ghaderi, M., Rezapour, S., & Mosavi, A. (2022). A study on a special case of the Sturm-Liouville equation using the Mittag-Leffler function and a new type of contraction. AIMS Mathematics, 7(10), 18253–18279. DOI: https://doi.org/10.3934/math.20221004.Suche in Google Scholar

[21] Houas, M., Kaushik, K., Kumar, A., Khan, A., & Abdeljawad, T. (2023). Existence and stability results of pantograph equation with three sequential fractional derivatives. AIMS Mathematics, 8(3), 5216–5232, doi: https//doi.org/10.3934/math.2023262. Suche in Google Scholar

[22] Iqbal, M. S., Ahmed, N., Akgül, A., Raza, A., Shahzad, M., Iqbal, Z., Rafiq, M., & Jarad, F. (2022). Analysis of the fractional diarrhea model with Mittag-Leffler kernel. AIMS Mathematics, 7(7), 13000–13018, doi: https//doi.org/10.3934/math.2022720. Suche in Google Scholar

[23] Jeelani, M. B., Alnahdi, A. S., Almalahi, M. A., Abdo, M. S., Wahash, H. A., & Abdelkawy, M. A. (2022). Study of the Atangana-Baleanu-Caputo type fractional system with a generalized Mittag-Leffler kernel. AIMS Mathematics, 7(2), 2001–2018. DOI: https://doi.org/10.3934/math.2022115.Suche in Google Scholar

[24] Khan, A., Hussain, G., Zahri, M., Zaman, G., & Humphries, U. W. (2020). A stochastic sacr epidemic model for hbv transmission. Journal of Biological Dynamics, 14(1), 788–801. PMID: 33073738. DOI: https://doi.org/10.1080/17513758.2020.1833993.Suche in Google Scholar PubMed

[25] Kilbas, A., Srivastava, H., & Trujillo, J. (2006). Theory And Applications of Fractional Differential Equations. North-Holland Mathematics Studies. Elsevier Science & Tech. DOI: https://doi.org/10.1016/S0304-0208(06)80001-0.Suche in Google Scholar

[26] Lassong, B. S., Dasumani, M., Mung’atu, J. K., & Moore, S. E. (2024). Power and Mittag-Leffler laws for examining the dynamics of fractional unemployment model: A comparative analysis. Chaos, Solitons & Fractals: X, 13, 100117. DOI: https://doi.org/10.1016/j.csfx.2024.100117.Suche in Google Scholar

[27] Li, X.-P., Din, A., Zeb, A., Kumar, S., & Saeed, T. (2022). The impact of l’vy noise on a stochastic and fractal-fractional Atangana-Baleanu order hepatitis B model under real statistical data. Chaos, Solitons & Fractals, 154, 111623. DOI: https://doi.org/10.1016/j.chaos.2021.111623.Suche in Google Scholar

[28] Nortey, S., Akorly, E., Dadzie, M., & Moore, S. E. (2024). Public health interventions for fractional optimal control of buruli ulcer. medRxiv. DOI: https://doi.org/10.1101/2024.09.05.24313151.Suche in Google Scholar

[29] Opoku, M. O., Wiah, E. N., Okyere, E., Sackitey, A. L., Essel, E. K., & Moore, S. E. (2023). Stability analysis of caputo fractional order viral dynamics of hepatitis B cellular infection. Mathematical and Computational Applications, 28(1), 24. DOI: https://doi.org/10.3390/mca28010024.Suche in Google Scholar

[30] Podlubny, I. (1998). Fractional Differential Equations: An Introduction to Fractional Derivatives, Fractional Differential Equations, to Methods of Their Solution and Some of Their Applications. Mathematics in Science and Engineering, Elsevier Science. https://books.google.com.gh/books?id=F1idlAEACAAJ.Suche in Google Scholar

[31] Wiah, E. N., Makinde, O. D., & Adetunde, I. A. (2015). A fractional-order HBV infection model with constant vaccination strategy. Communication Mathematics Biology Neuroscience, 2015, 27. DOI: https://scik.org/index.php/cmbn/article/view/2180.Suche in Google Scholar

[32] Wiah, E. N., Makinde, O. D., & Adetunde, I. A. (2015). Optimal control of hepatitis b virus disease in a population with infected immigrants. Engineering Mathematics Letters, 2015, 8. https://scik.org/index.php/eml/article/view/2365.Suche in Google Scholar

[33] Wireko, F. A., Asamoah, J. K. K., Adu, I. K., & Ndogum, S. (2024). Non-optimal and optimal fractional control analysis of measles using real data. Informatics in Medicine Unlocked, 49, 101548. DOI: https://doi.org/10.1016/j.imu.2024.101548.Suche in Google Scholar

[34] Zou, L., Zhang, W., & Ruan, S. (2010). Modeling the transmission dynamics and control of hepatitis B virus in China. Journal of Theoretical Biology, 262(2), 330–338. DOI: https//doi.org/10.1016/j.jtbi.2009.09.035. Suche in Google Scholar

Received: 2024-09-26
Revised: 2025-01-19
Accepted: 2025-01-31
Published Online: 2025-07-08

© 2025 the author(s), published by De Gruyter

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

Heruntergeladen am 3.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cmb-2025-0021/html
Button zum nach oben scrollen