Dynamics of an Eco-Epidemic Predator–Prey Model Involving Fractional Derivatives with Power-Law and Mittag–Lefﬂer Kernel

: In this paper, we consider a fractional-order eco-epidemic model based on the Rosenzweig– MacArthur predator–prey model. The model is derived by assuming that the prey may be infected by a disease. In order to take the memory effect into account, we apply two fractional differential operators, namely the Caputo fractional derivative (operator with power-law kernel) and the Atangana–Baleanu fractional derivative in the Caputo (ABC) sense (operator with Mittag–Lefﬂer kernel). We take the same order of the fractional derivative in all equations for both senses to maintain the symmetry aspect. The existence and uniqueness of solutions of both eco-epidemic models (i.e., in the Caputo sense and in ABC sense) are established. Both models have the same equilibrium points, namely the trivial (origin) equilibrium point, the extinction of infected prey and predator point, the infected prey free point, the predator-free point and the co-existence point. For a model in the Caputo sense, we also show the non-negativity and boundedness of solution, perform the local and global stability analysis and establish the conditions for the existence of Hopf bifurcation. It is found that the trivial equilibrium point is a saddle point while other equilibrium points are conditionally asymptotically stable. The numerical simulations show that the solutions of the model in the Caputo sense strongly agree with analytical results. Furthermore, it is indicated numerically that the model in the ABC sense has quite similar dynamics as the model in the Caputo sense. The essential difference between the two models is the convergence rate to reach the stable equilibrium point. When a Hopf bifurcation occurs, the bifurcation points and the diameter of the limit cycles of both models are different. Moreover, we also observe a bistability phenomenon which disappears via Hopf bifurcation. Author Contributions: Conceptualization, A.S.; methodology, A.S. and W.M.K.; software, H.S.P. and I.D.; validation, A.S. and I.D.; formal analysis, H.S.P., A.S. and W.M.K.; investigation, H.S.P. and I.D.; resources, A.S.; data curation, H.S.P. and W.M.K.; writing—original draft preparation, H.S.P.; writing—review and editing, A.S., W.M.K. and I.D.; visualization, H.S.P.; supervision, A.S., W.M.K. and I.D.; project administration, W.M.K.; funding acquisition, A.S. manuscript.


Introduction
The long history of mathematical biology reveals that predator-prey modeling plays an imperative role in scientific research. Since the classical Lotka-Volterra, as the fundamental predator-prey model, have been proposed [1][2][3], the theoretical ecology has been constantly developed. The Lotka-Volterra model has been modified by a lot of researchers to contrive the relevant model which corresponds to the actual phenomena, such as the functional response [4][5][6][7][8][9], the Allee effect [10][11][12][13][14], the impact of competition [15][16][17] and so forth. All of these modifications affect the density of populations as the result of interactions between two or more populations. From the biological point of view, the population density also depends on the epidemiological frameworks, which leads to the increment of the death rate caused by the disease in the population. Eco-epidemiology describes the occurrence of ecological and epidemiological circumstances simultaneously [18][19][20][21][22][23].
For instance, in a biotope that involves pests and its natural enemies, we observe that the eco-epidemiological problem occurs and described by the interaction between pest and its predator. One or both populations may be infected by a disease caused by microbiological pathogens such as parasites, viruses, fungi and bacteria, for further see refs. [24][25][26][27][28] and some references therein. For the real-world example, to suppress the growth rate of rats (Rattus sp.) in agricultural landscape, the farmers use barn owls (tyto alba) and some pathogens such as viruses (paramyxoviridae and pneumovirus family), bacterias (klebsiella pneumoniae, mycoplasma pulmonis, citrobacter rodentium and streptococcus pneumoniae), and parasites (giardia muris, spironucleus muris, oxyuriasis and acariasis) [29][30][31][32][33].
Regarding to the description above, some researchers have successfully constructed and studied the eco-epidemiological problem in a deterministic model. Mondal et al. [21], Wang et al. [34] and Suryanto et al. [35] study the dynamics of the interaction between two populations in a predator-prey relationship where the prey is infected by a disease and the predator is hunting the infected prey. In facts, many natural phenomena in the ecological system show that predation still occurs although the infected prey does not exist. This means both susceptible and infected prey are regarded to be predated. Based on this assumption, Sahoo [19], Saifuddin et al. [20], Panigoro et al. [23], Upadhyay et al. [36] and Nugraheni et al. [37] study the eco-epidemic model with the predation existing on both susceptible and infected prey. The fundamental differences of their models lie on the infectious transmission behavior, the predator functional response, the existence of the Allee effect and the operator of the derivative. Here, we study the eco-epidemic model formulated under the following assumptions. (a) In the presence of disease, the prey is divided into two compartments, namely susceptible prey S(t) and infected prey I(t). The susceptible prey becomes infected when the individuals have contact with the infected prey. Since the density of prey and predator are assumed large enough, the infection rate due to this contact is bilinear which is symbolized by b.
In the presence of the predator-prey relationship, the interaction between susceptible prey, infected prey and predator is following the Rosenzweig-MacArthur model [38] with a few adjustments. The susceptible prey growth logistically with intrinsic growth rate r and environmental carrying capacity K. The infected prey competes for food with the susceptible prey but has no contribution to the growth rate of susceptible prey. Both susceptible prey and infected prey are predated following Holling type-II with the attack rate of predator on susceptible prey m s , the attack rate of predator on infected prey m i , the half-saturation constant of predator for susceptible prey k s and the half-saturation constant of predator for infected prey k i . Since both predations contribute to the predator birth, the conversion efficiency consists of two parts, i.e., the conversion efficiency of predator on susceptible prey b s and the conversion efficiency of predator on infected prey b i . It is also assumed that both infected prey and predator are reduced due to mortality following exponential decay where d is the death rate of infected prey, and a is the death rate of predator.
Based on above assumptions, we have the following eco-pidemic model.
For simplicity, model (1) is transformed into a non-dimensional system by introducing transformation of variables (S, I, P, t) → S K , I K , m s P rK , rt to obtain the following eco-epidemic model.
To approach the superlative shape of the eco-epidemiological model, the ordinary calculus is considered less effective in describing the complex ecological phenomena that involves the system memory and hereditary biological properties of complex multiple timescale dynamics, see refs. [39][40][41]. To overcome such problem, many researchers apply fractional calculus because it is considered to have the ability to represent biological conditions related to the memory effects more powerfully and accurately than the classical calculus [42][43][44][45][46][47][48]. Particularly, the fractional-order derivatives, as part of the fundamental theory of fractional calculus, have nonlocal properties which are naturally connected to the biological systems. It means that the current state of population density depends on all earlier states [49][50][51]. If we revisited the evolution of fractional-order derivative, the Riemann-Liouville [52] and Caputo [53] operators have been widely applied to the biological modeling. To investigate the behavior of the fractional-order dynamical system, the theoretical aspect of the Caputo operator is the most complete tool compared to others, see refs. [52,54,55]. However, the kernels of the first two definitions of fractional operators are single and local [56][57][58][59]. Therefore, Caputo operator is not sufficient enough to express better nonlocal dynamics. To cover the limitation of Caputo operator, in 2015, Caputo and Fabrizio proposed a new fractional operator, which is called the Caputo-Fabrizio derivative [60]. The non-singular and exponential kernel of this fractional derivative is the novelty of their result and has been successfully applied in several fields [40,[61][62][63]. One year later, Atangana and Baleanu introduced a new fractional operator with a nonlocal and non-singular kernel. Such an operator is well-known as the Atangana-Baleanu operator, which has all the advantages of the Caputo-Fabrizio operator but it uses the Mittag-Leffler function as its kernel [64]. Most researchers reveal that the Atangana-Baleanu operator gives better results and claim that the effect of memory is represented efficiently, see refs. [9,40,51,65].
For a better approach in epidemiological modeling, the fractional-order derivative is utilized in a similar way with [48,66] which replace the first-order derivative d dt at the left-hand side of model (2) with the fractional-order derivative D α t . Therefore, we obtain Pay close attention to the dimension of the equations in model (3), where the fractionalorder derivatives have the dimensions of (time) −α while the parametersη,δ,m,μ,β and q have the dimensions of (time) −1 . This circumstance means the inconsistency of physical dimensions in the model (3) and can be surmounted by rescaling the parameters as in the following model.
By applying new parametersη = η,δ = δ,m = m,μ = µ,β = β andq = q, we achieve We note that model (5) consists of three fractional differential equations. The order of the fractional derivative in all equations is set to be the same to maintain their symmetrical aspect. Notice that we have to assume in model (5) that the disease transmission follows a bilinear incidence rate. Previously, Nugraheni et al. [37] have studied the same model but with saturated incidence rate. However, Nugraheni et al. [37] have only presented some numerical simulations of model (5) with Caputo sense without any analytical studies. In this article, we start our work by constructing the fractional-order eco-epidemic model consisting of the model assumptions, the first-order modeling, its non-dimensional form and the fractional-order modeling including the replacement of the first-order derivative with fractional-order derivative and the time dimension adjustment for some parameters to prevent the inconsistency of physical dimensions. Furthermore, we present the complete dynamics of model (5) with Caputo operator including the local and global stability, the existence of Hopf bifurcation and their appropriated numerical simulations. We also use the Atangana-Baleanu in Caputo sense as the fractional-order operator of model (5) numerically by previously showing the existence and uniqueness of solution of the model. We compare numerically the difference between model (5) with Caputo and Atangana-Baleanu operators, especially the difference of the dynamical behaviors when the Hopf bifurcation occurs. All of these analytical results and numerical simulations have never been done in [37], which is the novelty of our work. This paper is organized as follows. In Section 2, we present some fundamental concepts which consist of definitions, theorems and lemmas that are associated with Caputo and Atangana-Baleanu derivatives and dynamical systems. Further, in Section 3, we investigate the dynamics of model (5) with Caputo derivative. The investigation includes the existence, uniqueness, non-negativity, boundedness of the solutions, the existence of equilibrium points, their local and global stability, as well as the occurrence of Hopf bifurcation. The existence and uniqueness of solutions of model (5) with Atangana-Baleanu derivative in Caputo sense are discussed in Section 4. To support our theoretical findings, we demonstrate some numerical simulations in Section 5. Finally, we present some conclusions in Section 6.

Fundamental Concepts
In this section, we present the important results from previous research such as definitions, theorems and lemmas associated with the fractional-order differential equation. Definition 1 ([55]). Suppose 0 < α ≤ 1. The Caputo fractional derivative of order−α is defined by where t ≥ 0, f ∈ C n ([0, +∞), R), and Γ is the Gamma function.

Theorem 1 ([64]
). By using the inverse Laplace transform and convolution theorem, the unique solution of the time fractional differential equation can be written as Theorem 2 (Matignon condition [55,68]). Consider a Caputo fractional-order system A point x * is called an equilibrium point of Equation (9) if it satisfies f ( x * ) = 0. This equilibrium point is locally asymptotically stable if all eigenvalues λ j of the Jacobian matrix J = ∂ f ∂ x evaluated at x * satisfy | arg(λ j )| > απ 2 . If there exists at least one eigenvalue that satisfies | arg(λ k )| > απ 2 while | arg(λ l )| < απ 2 , k = l, then x * is a saddle-point.

Eco-Epidemic Model in the Caputo Sense
In this section, we consider a fractional-order eco-epidemic model (5) with the fractional derivative in the Caputo sense as defined in Definition 1: where X = (S, I, P). In the following sub-sections, we investigate the dynamics of model (12).

Existence and Uniqueness
In this section, we investigate the sufficient condition for the existence and uniqueness of solution of model (12).

Proof.
We use a similar approach as in [8]. For any X = (S, I, P),X = (S,Ī,P), X,X ∈ Ψ, it follows from model (12) that Hence, F(X) satisfies the Lipschitz condition. According to Lemma 2, the existence and uniqueness of model (12) with initial value S 0 ≥ 0, I 0 ≥ 0 and P 0 ≥ 0 is established, and the theorem is well proven.

Non-Negativity and Boundedness
Model (12) describes an eco-epidemiological model in fractional-order differential equations. Therefore, the solution of model (12) must be bounded and non-negative, as it is performed in the following theorem. Proof. We start by proving that for non-negative initial condition, S(t) ≥ 0 for t → ∞. Suppose that is not true, then there exists t 1 > 0 such that Employing (13) and the first equation of model (12), we obtain Based on Lemma 1, we have S(t + 1 ) = 0, which contradicts to the fact S(t + 1 ) < 0. Thus, S(t) ≥ 0 for all t ≥ 0. Using the similar procedure, we conclude I(t) ≥ 0 and P(t) ≥ 0 for all t > 0. Now, we show the boundedness of solutions by adopting similar manner as in [8]. We first define a function V(t) = S + I + ζP.
Then, for each ξ > 0, we obtain By choosing ξ < min{δ, q} and ζ < min m β , 1 µ , we have The standard comparison theorem for fractional-order derivative (see Lemma 3) gives for t → ∞. Therefore, all solutions of model (12) with non-negative initial conditions are confined to the region Φ, where Therefore, the proof of non-negativity and boundedness of solutions are completely presented.

The Existence of Equilibrium Point
From model (12), we can determine the nullclines of the susceptible prey, infected prey and predator, which are respectively denoted by N S , N I and N P and are defined by the following sets N S := (S, I, P) : Since we are only interested in solutions that satisfy biological conditions, we only consider equilibrium points that satisfy N S ∩ N I ∩ N P ⊂ R 3 + . We can obviously identify that the infected prey and predator are extinct if the susceptible prey is zero. Therefore, the following lemma shows that the origin is the only equilibrium point when N S = {(S, I, P) : S = 0}.
Since δ η + mP η(ω+I) = 0, then I = 0 is the only nullcline of I. By substituting I = 0 to N P , we have N P = {(S, I, P) : P = 0 ∨ q = 0}. P = 0 is the only nullcline of P because q = 0. Thus, E 0 = (0, 0, 0) is the only equilibrium point. Now, we will investigate the equilibrium point when S = 0. Notice that if N I := {(S, I, P) : I = 0}, then N S ∩ N P ∩ R 3 + is the equilibrium point of model (12) where Immediately we recognize two equilibrium points as follows: 1.
The extinction of infected prey and predator point: The infected prey free point The condition µ > (1 + κ)q is equivalent to condition that the conversion rate of susceptible prey predation into the birth rate of predator is larger than the sum of the death rate of predator and its multiplication with halfsaturation constant of predation.
By considering the following nullclines N S := (S, I, P) : S + (1 + η)I + P κ + S = 1 , we obtain the co-existence equilibrium point where The existence of E * is described by the following lemma.

Local Stability of Equilibrium Points
In this part, we investigate the local stability properties of each equilibrium point of model (12). The local stability properties are studied by observing the eigenvalues of the Jacobian matrix at each equilibrium points, and the results are described in the following theorems.
Theorem 5. The equilibrium point E 0 is always a saddle point.
Thus, Theorem 2 says that E 1 is a saddle point.
Theorem 9. Suppose that: The co-existence point E * = (S * , I * , P * ) is locally asymptotically stable if one of the following statements is satisfied.
Proof. The Jacobian matrix of model (12) evaluated at E * is, Using the Routh-Hurwitz condition for a fractional-order dynamical system (See Proposition 1 in [72]), the locally stability conditions of co-existence point E * = (S * , I * , P * ) are proven.

Global Stability of Equilibrium Points
The global asymptotic stability of the equilibrium point of model (12) is studied. The results are presented in the following theorems.

Proof. We define a Lyapunov function
Based on Lemma 4, we have Consequently, Lemma 5 says that E 2 is globally asymptotically stable.
Proof. We first writeĨ = 1−S 1+η and define a Lyapunov function By using Lemma 4, we obtain , then we have C D α t W 3 (S, I, P) ≤ 0. It follows from Lemma 5 that E 3 is globally asymptotically stable.

The Existence of Hopf Bifurcation
One of the interesting phenomena in studying the predator-prey model is the occurrence of Hopf bifurcation. This circumstance arises when the stability of an equilibrium point changes and a limit-cycle appears simultaneously as a parameter is varied [73,74]. In a system of first order differential equations, the occurrence of Hopf bifurcation is indicated by the appearance of purely imaginary eigenvalues of the Jacobian matrix. If we vary the bifurcation parameter, then the sign of the real part of the complex eigenvalues changes [75]; and therefore the stability properties of the equilibrium point also changes. In a fractionalorder system, this bifurcation also occurs when the order of fractional derivative (α) is varied [76]. It is shown in [77,78] that a 3rd-dimensional fractional-order system undergoes a Hopf bifurcation around an equilibrium point if eigenvalues λ 1,2,3 of its Jacobian matrix evaluated at the equilibrium point satisfy the following conditions: dα α=α * = 0. When α crosses α * = (2/π) tan −1 (ω/θ), the equilibrium point changes its stability and is accompanied by the appearance of a stable limit-cycle. Since the fractional-order system has no periodic orbits [79], the limit-cycle is not a periodic solution, but it is a nearby solution that converges to periodic signals [76,80].

Eco-Epidemic Model in the Atangana-Baleanu Sense
If the fractional-order eco-epidemic model (5) is expressed in the Atangana-Baleanu derivative in Caputo (ABC) sense, then we obtain By Theorem 1, the solution of model (16) can be expressed in the following Volterratype integral equation where The existence and uniqueness of the solutions of model (16) will be investigated in the following sub-section.

Existence and Uniqueness
To prove the existence and uniqueness of solutions of model (16), we first show that the kernels G i (t, S), i = 1, 2, 3 satisfy the Lipschitz condition. Suppose that S,S, I,Ī, P andP are functions that satisfy S ≤ a 1 , S ≤ a 2 , I ≤ b 1 , Ī ≤ b 2 , P ≤ c 1 and P ≤ c 2 . For the kernel G 1 (t, S) = 1 − S − (1 + η)I − P κ+S S and two functions S andS, we get where g 1 = 1 + a 1 + a 2 + (1 + η)b 1 + c 1 κ . Hence, the Lipschitz condition holds for G 1 (t, S). In a similar manner, we can show that where g 2 = a 1 η + δ + c 1 m ω and g 3 = a 1 µ κ + b 1 β ω + q. Hence, the Lipschitz condition also holds for kernels G 2 (t, I) and G 3 (t, P). Furthermore, G 2 (t, I) and G 3 (t, P) are contracted if 0 ≤ g 2 < 1 and 0 ≤ g 3 < 1, respectively. Now, we investigate the existence of solutions of model (16) by employing the fixedpoint theorem. For this purpose, we start by writing Equation (17) in the following recursive formulae The associated initial conditions along with Equation (20) are S 0 (t) = S(0), I 0 (t) = I(0), and P 0 (t) = P(0). Next, from Equation (20), we have the difference expression of successive terms as follows.
Based on Equation (21), we have that By using (18) and (19), we can show that the norm of both sides in (21) fulfill the following relations Now, by applying (23), the existence and uniqueness of model (16) are shown by the following theorem.
Proof. We assume that S(t), I(t) and P(t) are bounded functions, and hence the Lipschitz condition is satisfied. From Equation (23) we can get the following inequalities.
Therefore, the existence and smoothness of the solution presented in Equation (22) are proven since Φ 1,n (t) → 0, Φ 2,n (t) → 0 and Φ 3,n (t) → 0 as n → ∞ and t = t max . To show that the functions which satisfy Equation (17) are the solutions of Equation (16), we suppose that where Υ i,n (t), i = 1, 2, 3 are the remainder terms of series solutions. The norm of Υ 1,n (t) satisfies By applying this relation iteratively, we get at t = t max For n → ∞, we obtain Υ 1,n (t) → 0. Applying the similar manner, we have Υ 2,n (t) → 0 and Υ 3,n (t) → 0. Hence, the functions which satisfy Equation (17) are the solutions of Equation (16). Now, we show the uniqueness of solutions of Equation (16). For this aim, we suppose that S * (t), I * (t) and P * (t) are another solution of Equation (16). Then, we have Taking the norm for both sides and using the same procedures as in (23) and (25), we obtain For t = t max , we have (24). Hence, S(t) − S * (t) = 0 and consequently S(t) = S * (t). In the same way, we can show that I(t) = I * (t) and P(t) = P * (t). Hence, the uniqueness of the solution of Equation (16) is proven.

Numerical Simulations
In this section, we present some results of our numerical simulations for the fractionalorder eco-epidemic models in both Caputo sense (12) and ABC sense (16). For this aim, we solve the model in Caputo sense (12) using the predictor-corrector scheme developed by Diethelm et al. [81], while the model in ABC sense (16) is solved by applying the predictor-corrector scheme proposed by Baleanu et al. [82]. Since the field data are not available, the simulations are performed by using some hypothetical parameter values.
We first perform simulation by setting the parameter values as follows: Using these parameter values, the eco-epidemic model with fractional-order derivative in both Caputo sense (12) and ABC sense (16) have two equilibrium points, i.e., E 0 and E 1 . Based on the analysis for the model in Caputo sense (12), it is shown that E 0 is a saddle point and E 1 is asymptotically stable. This behavior is confirmed by our numerical simulation shown in Figure 2. If we modify the parameter values in (31) such that η = 0.35 and q = 0.2, then, in addition to E 0 and E 1 , the model also has equilibrium points E 2 and E 3 . All of these equilibrium points are unstable except E 2 . The stability of E 2 is clearly observed in Figure 3. Now, some parameter values in (31) are replaced by η = 0.95, δ = 0.2 and q = 0.4. Under these parameter values, the model has three equilibrium points, i.e., E 0 , E 1 and E 3 . The previous analysis for the model in Caputo sense shows that E 0 and E 1 are unstable, while E 3 is asymptotically stable. Such stability behavior can be seen in Figure 4. Furthermore, in Figures 2-4, the numerical solutions of model in the Caputo sense are compared to those of models in ABC sense. It is observed that the phase portraits and time series of both models have similar dynamical behavior. To see the difference between the solutions of the two models, we perform some simulations using the same parameter values as in Figures 2-4     Next, we perform simulation using the following parameter values: Here, the model has equilibrium points: E 0 , E 1 , E 3 and E * . By applying the stability analysis for the model in the Caputo sense, it can be shown that E 0 , E 1 and E 3 are unstable; while the stability of E * is determined by α. If α < α * ≈ 0.85662 then E * is asymptotically stable. On the other hand, E * becomes unstable if α > α * , where in this case, the solution is convergent to a limit-cycle. In other words, there occurs a Hopf bifurcation controlled by α where the bifurcation point is at α = α * . The Hopf bifurcation is indeed verified by our bifurcation diagram shown in Figure 8. We confirm numerically that both models with Caputo sense and ABC sense undergo the Hopf bifurcation, but with different bifurcation points. The bifurcation point of model with Caputo sense has smaller value of α than that of the model with ABC sense. To describe their dynamics, we select three values of α = 0.79, 0.86, 0.9, each of which is denoted by the labels [a], [b], [c] in Figure 8, respectively. When α = 0.79, both models with Caputo sense and ABC sense are convergent to E * as in Figure 9a. From the time series in Figure 10a, the solution of model with ABC sense converges faster than model with Caputo sense. For α = 0.86, E * of model with Caputo sense losses its stability and the solution goes to the limit-cycle while E * of model with ABC sense still maintains its stability, see Figures 9b and 10b. This circumstance confirms that the model with Caputo sense has undergone the Hopf bifurcation while model with ABC sense has not. When α = 0.9, E * of model with ABC sense losses its stability via Hopf bifurcation as in Figure 9c. The solution of both models converge to the limit-cycle where the diameter of limit-cycles obtained by model in ABC sense is smaller than those obtained by model in Caputo sense, see Figure 10c. To see the evolution of limit-cycle in more detail, we perform simulations using parameter values in (32) and α ∈ (0.8, 0.94). In Figure 11, we show the stable equilibrium point or limit-cycle in (I, P)−plane as function of α. As mentioned before the stable limit-cycle appears if α > α * . It can be seen that the diameter of limit-cycle obtained by both models in Caputo sense and ABC sense are getting bigger when α is increased. We notice from Figure 8 that the model with Caputo sense has a smaller critical value of α. Therefore, there are situations where the model with Caputo sense has an α that passes its critical value (α * ) while the model with ABC sense does not. This situation shows that the model with the two fractional derivative operators have different biological interpretations in determining the density of prey and predators. On one hand, the density of prey and predator obtained by the model with Caputo sense fluctuates periodically, whereas those obtained by the model with ABC sense converge to a constant value. Finally, we take the following parameter values: For this case, the model has five equilibrium points, namely E 0 , E 1 , E 3 , E * 1 and E * 2 . Using the results of a previous stability analysis, it is shown that E 0 , E 1 and E * 2 are unstable, while E 3 is asymptotically stable regardless of the value of α. We also check that there exists Hopf bifurcation around E * 1 . In the latter case, E * 1 is stable for α < α * 1 ≈ 0.84730. If α > α * 1 then E * 1 loses its stability and there appears a limit-cycle. Hence, the model exhibits a bistability phenomenon for α < α * 1 , where, in this case, E 3 and E * 1 are locally asymptotically stable. To illustrate the dynamics of eco-epidemic model with parameter values in (33), we plot numerical solutions with two slightly different initial values in Figure 12. When we take α = 0.83 < α * 1 the solutions of both models are respectively convergent to different equilibrium points, namely E 3 and E * 1 , see Figure 12a,b. Furthermore, when we increase the order of fractional derivative to α = 0.95 then E 3 remains stable but the stability of E * 1 vanishes via Hopf bifurcation as in Figure 12c,d. Thus, a captivating circumstance has been shown, where the initial condition is very sensitive in determining the limiting behavior of the system.

Conclusions
We have presented the dynamics of fractional-order Rosenzweig-MacArthur ecoepidemic model using fractional derivative in both Caputo sense and ABC-sense. We have determined conditions for the existence and uniqueness of solutions for models in both Caputo sense and ABC sense. It is also shown that all solutions are non-negative and bounded in R 3 + . The model has at most five types of equilibrium points, i.e., the origin, the extinction of infected prey and predator point, the infected prey free point, the predator-free point and the co-existence point. Based on the stability analysis for the model in the Caputo sense, it is found that the origin is a saddle point, meaning that the extinction of all populations will never happen. We also found that the other equilibrium points are conditionally asymptotically stable. Furthermore, the conditions for the existence of Hopf bifurcation have been established, where the bifurcation is driven by the order of the fractional derivative. Our theoretical results have been confirmed by numerical solutions of the model in the Caputo sense. In this article, the eco-epidemic model in the ABC sense has also been solved numerically. The comparison of our numerical results shows that model with both Caputo sense and ABC sense have the same dynamical behavior except around the interior equilibrium point. In other words, the dynamical behavior of the proposed model with both senses are symmetric around axial equilibrium points, but it is asymmetric around the interior point when a Hopf bifurcation occurs. We confirm numerically that the interior point of both models has a different bifurcation point when Hopf bifurcation occurs.
For some values of the order−α, the interior point of model with ABC sense is stable while the interior point of model with Caputo sense is unstable. Our numerical simulations also show that the proposed models may exhibit a bistable phenomenon. We finally notice that our simulations are based on some hypothetical parameter values. For further studies, it is recommended to compare the performance of both models, namely with Caputo sense and with ABC sense, by using real data of selected eco-epidemiological case.