The main cause of leishmania type parasites is leishmaniasis. Four types of leishmaniasis have been studied in the literature [1, 2] . Among them is cutaneous leishmaniasis (CL) which is considered to be the most widespread form of leishmaniasis caused by L.tropica and L.major. The macrophagic cells of the host are attacked which results in lesion skin on uncovered parts of the body such as the legs, arm, and face. The parts of the world which are endemic to this disease are South America, Europe, and Asia. The disease is declared as one of the most dangerous diseases in the world by the WHO as the spreading rate of new cases per year is very fast (more than a million). The ten countries which together account for 70-75% of a e-mail: amir.maths@gmail.com b e-mail: minc@firat.edu.tr (corresponding author) strategies for eradicating the disease transmission. Numerical simulations are carried out with the help of the Runge-Kutta fourth-order procedure in the last section.
Mathematical modeling of epidemic diseases have been widely studied by researchers [15] [16] [17] [18] . In this section, CL epidemic model is presented consisting of seven classes. These classes are classified into four human population subclasses i.e S h (t), E h (t), I h (t), and R h (t), denoting the susceptible, exposed, infected, and recovered people. The three vector population subclasses i.e S v (t), E v (t), and I v (t) represent susceptible, exposed, and infected vectors. N h (t) represents the total human population i.e N h (t) = S h (t)
h is the new recruitment of humans to susceptible class S h . The infected vector i.e sand fly bites the susceptible humans, and they are infected at the rate of ab 1
where transmission probability of CL to human from the sand fly is denoted by b 1 , and the sand fly biting rate is represented by a. θ shows the rate due to which the uninfected exposed humans are recovered and k 1 is the rate due to which the exposed humans get infectious. β is the rate that shows the natural recovery of humans from infected class. The infected humans after being bitten by the sand flies infect the sand flies at the rate ac 1 (t) . μ h is the natural death rate in humans, while μ v shows the natural death rate in sand flies. The CL probability of transmission from humans to sand flies is represented by c 1 . k 2 is the rate which shows transmission of sand flies exposed class to infected class. The model is given below:
Next, for the system (1), we establish some basic results.
Total population dynamics is represented by: 
The feasible region (biological) is
From Eqs. (2) and (3), we obtain
Which shows that the model is well posed and is positively invariant domain.
The orthant R 7 + is invariant positively for the system described by (1). (43) is expressed in the following form:
where
Here, we see that L is the Metzler matrix as it has nonnegative entries on its off diagonal and B ≥ 0. Hence, it is concluded that the system (1) is positively invariant in R 7 + .
Lemma 2 Solutions (if exist) of system (1) are positive under the initial conditions (2) , for all t > 0.
Proof Let us assume that the solutions exists in I , for all t ∈ I ⊂ [0, inf). Consider the second equation of (1) and the solution of it has the following form
We also take the third equation and solution of it has the following form
Clearly, it can be seen from the above solutions that these are strictly positive. In the same fashion, it can be shown that S h , R h , S v , E v , and I v possess nonnegative solutions.
The disease-free equilibrium point of system (1) is denoted by E 0 , i.e
The threshold value commonly known as basic reproduction number is very important and informative regarding the spread of infectious disease. First of all, we need to calculate this threshold value, for the sake of this let
is our infected compartment, then it follows from system (1) that:
Deploying the method of next-generation matrix, we have the following;
The inverse of V is
Thus,
The dominant eigen value gives us R 0 , i.e
We establish the local stability of the system (1) in this section at disease-free point E 0 as well as at endemic equilibrium point E * .
3.1 At disease-free equilibrium point Proof The Jacobian matrix of the system (1) at E 0 is given by
Clearly, three eigenvalues of the Jacobian matrix J |0| of model (1) around the disease-free equilibrium E 0 are negative, i.e., λ 1 = −μ h , λ 2 = −μ h and λ 3 = −μ h . For the remaining, we taking the following reduced matrix:
Using the elementary row operation, J |2| 1 takes the following form:
where
Clearly, the three eigenvalues of J |0| 2 are negative, the last eigenvalue also has neg-
, then the endemic equilibrium E * of the system (1) is locally asymptotically stable.
Proof The Jacobian matrix of the system (1) at E * is given by
Where
Clearly, one eigenvalue of the Jacobian matrix J | * | of model (1) around the disease present equilibrium point E * is negative, i.e., λ 1 = −μ h < 0. For the remaining six eigenvalues, we take the following reduced matrix: Eur. Phys. J. Plus (2020) 135:528
(24) Using the elementary row operation, J | * | 1 takes the following form:
where
The eigenvalues of J | * | 2 takes the following form:
The last eigenvalue also has negative real part if
this implies that
As we have shown that the real part of all eigenvalues are negative, hence endemic equilibrium point E * is locally asymptotically stable under the condition that 1
For the model (1), the global stability at disease-free point is achieved by taking into account the Castillo-Chavez approach [19] . The method is summarized as, the proposed model (1) is reduced into the following two subsystems given by
In the system (26), χ 1 and χ 2 represent the number of uninfected and infected individuals, respectively, that is,
The diseasefree equilibrium is denoted by E 0 and define as E 0 = (χ 0 1 , 0). Thus, the existence of the global stability at disease-free equilibrium point depends on the following two conditions 1. If dχ 1 dt = G(χ 1 , 0), χ 0 1 is globally asymptotically stable.
At second condition, B = D χ 2 H (χ 0 1 , 0) is an M-matrix that is the off diagonal entries are positive and is the feasible region. Then, the following statement holds.
For R 0 < 1, then the equilibrium point E 0 = (χ 0 , 0) of the system (1) is said to be globally asymptotically stable, if the above conditions are satisfied [19] . Theorem 4.1 If R 0 < 1, then the proposed model (1) is globally asymptotically stable at disease-free equilibrium E 0 and unstable otherwise.
By using model system (1), we have
Thus, from equation (29) as t → ∞, χ 1 → χ 0 1 . So χ 1 = χ 0 1 is globally asymptotically stable. Now,
As 2ab 1 S 0
Clearly, B is M-matrix and hence both the conditions are proved, so by Lemma 1, the disease-free equilibrium point E 0 is stable globally asymptotically.
For the global stability of (1) at endemic equilibrium E * , we use the geometrical approach [20] . The method is summarized as To investigate the sufficient condition through which the E * is globally asymptotically stable, consider the differential equatioṅ
where the open set U ⊂ R n is simply connected and f : U → R n is a function such that f ∈ C 1 (U ). Assuming that f (x * ) = 0 is the solution of Eq. (32) and for x(t, x 0 ), the following are true.
a. There exist a compact absorbing set K ∈ U. b. System (32) has a unique equilibrium.
The solution x * is said to be globally asymptotically stable in U, if it is locally asymptotically stable and all trajectories in U converges to the equilibrium x * . For n ≥ 2, a condition satisfied for f, which precludes the existence of non-constant periodic solution of equation (32) known as Bendixson criteria. The classical Bendixson criteria div f (x) < 0 for n = 2 is robust under C 1 . Furthermore, a point x 0 ∈ U is wandering for Eq. (32), if there exist a neighborhood N of x 0 and τ > 0, such that N ∩ x(t, N ) is empty for all t > τ. Thus, the following global stability principle is established for autonomous system in any finite dimension.
Define a matrix valued function P on U by
Equation (33) is a matrix valued function on U . Further assume that P −1 exist and is continuous for x ∈ K . Now, define a quantity define, such that
and J [3] be the third additive compound matrix of J , i.e., J (x) = U f (x) and B = P f P −1 + P J [3] P −1 . Let (B) be the Lozinskii measure of the matrix B with respect to the norm . in R n defined by
Hence, ifq < 0, which shows that the presence of any orbit that give rise to a simple closed rectifiable curve, such as periodic orbits and heterocyclic cycles. (32) is globally asymptotically stable in U , ifq < 0 [20] . Now, we apply the above techniques to prove the global stability of model (1) at endemic equilibrium. Thus, we have the following stability 2 and R 0 > 1, then the model (2) is globally asymptotically stable at endemic equilibrium E * and unstable otherwise.
Proof Consider the subsystem of (1), Let J be the Jacobian matrix of the system (36) given by
The third additive compound matrix of J is:
where
Therefore,
and
So that B = P f P −1 + P J |3| P −1 ,
where;
Consequently, 
Eur. Phys. J. Plus (2020) 135:528
and if
Thus, combining the above four inequalities, we get the following inequality:
where μ(B) = h i , i = 1, 2, 3, 4 is the Lozinskii measure. The subsystem of model (1) containing four nonlinear differential equations is globally asymptotically stable. Solving the remaining three linear differential equations results in I h (t) → I * , R h (t) → R * , and
Hence, E * is globally asymptotically stable. a S a 1.000000000
component of disease modeling although its computation becomes tedious for complex biological model. Sensitivity analysis of R 0 has received much attention from the ecologist and epidemiologist.
The normalized forward sensitivity index of the R 0 that depends differentiably on a parameter ω is defined as
Three methods are normally used to calculate the sensitivity indices, (i) by direct differentiation, (ii) by a Latin hypercube sampling method (iii) by linearizing system (1), and then solving the obtain set of linear algebraic equations. We will apply the direct differentiation method as it gives analytical expressions for the indices. The indices not only shows us the influence of various aspects associated with the spreading of infectious disease but also gives us important information regarding the comparative change between R 0 and different parameter. Consequently, it helps in developing control strategies. Table 1 shows that the parameters a, b 1 , k 1 , c 1 , and k 2 have a positive influence on the reproduction number R 0 , which describe that the growth or decay of these parameters say by 10% will increase or decrease the reproduction number by 10%, 4.9%, 1.0%, 5.0%, 0.17%, respectively. But on the other hand, the index for parameters θ , μ h , μ v and β illustrates that increasing their values by 10% will decrease the values of reproduction number R 0 by 0.04%, 3.6%, 5.1%, and 2.2%, respectively. h and v have no impact on the reproduction number. Sand fly biting rate a has got the highest sensitivity index, i.e., 1, while μ v has the secondhighest sensitivity index, i.e., − 0.517543860. The third highest sensitivity index is of c 1 which is 0.5. Since the effect of all parameters is coupled with these three key parameters i.e sand fly biting rate, transmission of CL from humans to sand flies, and the mortality rate of sand flies. So instead of addressing all the parameters, one can address only the three key parameters, which are the main cause of transmission. An increase or decrease in these key parameters causes a change in the rest of the parameters in the form of an increase or decrease. For example, decrease in the sand fly biting rate a means decrease in the contact rate of humans and sand flies. This creates difficulties for female sand flies to have human blood, which they needs for laying eggs. Consequently, a decrease occurs in the sand fly 
The RK4 method is a fourth-order method, meaning that the local truncation error is on the order of O(h 5 ), while the total accumulated error is on the order of O(h 4 ), where h is step-size.
Estimating the error has little or negligible computational cost compared to a step with the higher-order method. In most situations of interest, a fourth-order Runge-Kutta integration method represents an appropriate compromise between the competing requirements of a low truncation error per step and a low computational cost per step.
for n = 0, 1, 2, 3, . . . , using ⎧ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎩ k 1 = f (t n , y n ),
We used initial condition of the state variables as S h (0) = 40, E ( 0) = 50, I ( 0) = 40, R ( 0) = 30, S v (0) = 25, E v (0) = 20 and I v (0) = 40. The Runge-Kutta method is popular because of its simplicity and efficiency. It is one of the most powerful predictorcorrectors methods, following the form of a single predictor step and one or more corrector steps. The fourth-order Runge-Kutta approximation is given by dy dt = f (t, y), y(t 0 ) = y 0 . In this paper, we established global dynamics and sensitivity analysis of the anthroponotic cutaneous leishmania epidemic model. The sharp threshold parameter i.e basic reproduction number R 0 totally establishes the global stability of the proposed model in Theorems 4.1 and 4.2. Theorem 4.1 guarantees the global stability in the disease-free equilibrium case with the condition that the threshold parameter will be less than or equal to one. On the other hand, Theorem 4.2 proves the global stability at the endemic equilibrium point with the condition that the threshold parameter will greater than one. The global dynamics have been studied by utilizing the Castillo-Chavez method and geometrical approach. Now, we provide some simulations of anthroponotic cutaneous leishmania epidemic model to attest our findings. Choosing parameters as mentioned in Table 2 , the basic reproduction number R 0 = 0.000000016232 < 1, hence by theorem 2.1 the disease-free equilibrium is globally asymptotically stable, and S h (t), E h (t), I h (t), R h (t), S v (t), E v (t), and I v (t) tend to their disease-free equilibrium point (see Figs. 2, 3) . Exposed Human (b) The plot represents the exposed individuals. Exposed Vector (f) The plot represents the exposed vectors. Exposed Human (b) The plot represents the exposed individuals. Exposed Vector (f) The plot represents the exposed vectors. 
Recently, Covid-19 which was initially an epidemic but quickly become pandemic broke out in China. Covid-19 is locally as well as globally unstable till March, 2020. Similarly, from time to time cutaneous leishmaniasis (CL) also adopts the shape of the epidemic, particularly in Pakistan. That is why it is very necessary to model cutaneous leishmaniasis and discuss its local as well as global stability. Important parameters are highlighted which is sensitive to a threshold value commonly known as basic reproductive number. For endemic stability analysis, we consider the generalization of the Lyapunov method called a geometrical approach in which the third additive compound matrix is taken into account. The feasibility of our result is verified by numerical simulations. Extending our work, one can use harmonic mean type incidence rate to reformulate the visceral leishmaniasis epidemic model. One can check its stability analysis, the sensitivity of parameters, bifurcation analysis, and optimal control.