In this section, a region in which solutions of the model system (2) are uniformly bounded is the proper subset

Let be any solution with positive initial conditions. Then from Equation (2) it is noted that in the absence of the disease related mortality (i.e.), the rate of change of the population is given by,

Using Birkhoff and Rota’s theorem [9] on the differential inequality (3), the following expression is obtained;

where is the value evaluated at the initial conditions of the respective variables.

Thus, as in (4), the population size, , which implies that In respectof this, all the feasible solutions of system (2) enter the region.

Hence, is positively invariant and it issufficient to consider solutions in.

Furthermore, existence, uniqueness and continuation of results for system (2) hold in this region.

Lemma 1: The region is positively invariant for the model system (2) with initial conditions in.

Lemma 2: Let the initial data be then, the solution set of the system (2) is positive

Proof:

From the first equation of the model system (2), we have

The Integration factor is, multiplying both sides by the integration factor and integrating leads to

Equations for can be similarly be obtained.

Thus are positive.

In the absence of the disease, which implies that the disease free equilibrium points is given by

In this section, the threshold parameter that governs the spread of a disease which is called the effective reproduction number is determined. Mathematically, it is the spectral radius of the next generation matrix [10] .

This definition is given for the models that represent spread of infection in a population. It is obtained by taking the largest (dominant) Eigen value, (spectral radius) of

where is the rate of appearance of new infection in compartment, is the transfer of individuals out of the compartment by all other means and is the disease free equilibrium.

Therefore,

and

The partial derivatives if (6) and (7) with respect to gives

and

In the absence of the disease and when, the matrix (8) becomes

Now, taking the inverse of matrix (9) leads to

where

The spectral radius (dominant eigenvalue) of the matrix is

Hence, the effective reproduction number of the model system (2) is given by

The effective reproduction number measures the average number of new infections generated by a typical infectious individual in a community with inflow of infected immigrants when screening, education, health care and immunization strategies are in place.

Theorem 1: The disease free equilibrium of the model system (2) is locally asymptotically stable if and unstable if.

Theorem 1 implies that HCV can be eliminated from the community when if the initial size of the sub population of the model are in the basin of attraction of the disease free equilibrium. That means if, then on average an infected individual produce less than one new infected individual over the course of its infectious period and the infection cannot grow.

From Equation (12), for to be less than 1 this will only be possible when (which implies HCV education, health care and immunization) are increased without bound in collaboration with other intervention strategies to all people including immigrants in a given locality which may result into the decreasing effect on.

In the absence of interventions (screening, education, health care and immunization) that is, is reduced to:

Thus. Hence the presence of screening, education, health care and immunization can eradicate the HCV infection if can be reduced to below unity.

Local stability of disease free equilibrium, can be determined by the variational matrix of the model system (2). The Jacobian matrix at the steady states is given by;

The local stability analysis of the Jacobian matrix (13) of the system (2) can be done by the trace/determinant method. Where by matrix is locally asymptotically stable if and only if the trace of matrix is strictly negative and its determinant is strictly positive. Whose trace and determinant are given by

and

where, , , , ,.

Hence if

That is equivalent to

since

Thus, is locally asymptotically stable if and only if. These results are summarized with the theorem 1.

Endemic equilibrium point is a steady state solution in which the disease persists in the population (i.e.)

where,

, , ,

, , , , ,

, , , ,

, , , , ,

, , ,

and is the solution of the quadratic polynomial

where,

The equation, corresponds to a situation when the disease persists (endemic). In case of backward bifurcation, multiple endemic equilibrium must exist.

However it is important to note that is always positive if and negative if.

Theorem 2: The HCV model with screening, education, health care and immunization interventions have:

i) Precisely one unique endemic equilibrium if

ii) Precisely one unique endemic equilibrium if and or

iii) Precisely two endemic equilibrium if, and.

iv) None otherwise.

Theorem 3: A unique endemic equilibrium point, exists if and only if.

The global stability of the endemic equilibrium is analysed using the following constructed Lyapunov function by [11]

Theorem 4: If, the endemic equilibrium of the model (2) is globally asymptotically stable.

Proof: To establish the global stability of the endemic equilibrium, we construct the following Lyapunov function:

By direct calculating the derivative of along the solution of (2) we have;

or

where,

Thus if then; Noting that if and only if;;;;: Therefore, the largest compact invariant set in is the singleton

where is the endemic equilibrium of the system (2). By LaSalle’s invariant principle, it implies that is globally asymptotically stable in if.

In determining how best to reduce human mortality and morbidity due to HCV, we calculate the sensitivity indices of the basic reproduction number, to the parameters in the model using approach of [12] . These indices are crucial in determining the importance of each individual parameter in transmission dynamics and prevalence of the disease. Sensitivity analysis determines parameters that have a high impact on and should be targeted by intervention strategies. Sensitivity indices allow us to measure the relative change in a state variablewhen a parameter changes [12] . When a variable is a differentiable function of the parameter, the sensitivity index may be alternatively defined using partial derivatives.

Numerical values of sensitivity indices of to parameter values for the HCV model, evaluated using the following estimated parameter values:, , , , , , , , , and.

Definition 1: The normalised forward sensitivity index of a variable “” that depends differentiable on a parameter “” is defined as:

Having an explicit formula for in Equation (18), we derive an analytical expression for the sensitivity of

as to each of parameters involved in. For example the sensitivity indices of with

respect to and are given by;

and

Other indices

are obtained following the same method and tabulated as follows:

From Table 1, it can be observed that when the parameters, , and are increased keeping the other parameters constant they increase the value of implying that they increase the endemicity of the dis- ease as they have positive indices. While the parameters, , , , , and decrease the value of when they are increased while keeping the other parameters constant, implying that they decrease the

endemicity of the disease as they have negative indices.

The specific interpretation of each parameter shows that, the most sensitive parameter is the control based on education, health care and immunization, followed by effective contact rate of individuals with chronic disease, then recovered rate of chronic individuals due to treatment, effective contact rate of individuals with acute, followed by recovery rate naturally from acute, death rate of chronic infected, recovered rate of acute individuals due to treatment, natural mortality rate, rate at which exposed develop chronic, the rate at which acute infective are detected by a screening method from exposed group, the rate at which screened develop to chronic, which is the least sensitive parameter.

In this section, we illustrate the analytical results of the study by carrying out numerical simulations of the model system (2) using the following estimated parameter values:, , , , , , , , , , , , , ,.

Figures 2(a)-(d) show the proportion of HCV exposed, infective populations (acute, chronic) and proportion of HCV infectives all plotted against the proportion of susceptible population. This shows the dynamic beha-

viour of the endemic equilibrium of the model system (2) using the estimated parameter values above.

The phase portrait in Figures 2(a)-(d) shows that for any initial starting point or initial value, the solution curves tend to the endemic equilibrium point. Hence, we infer that the system (2) is globally stable about the endemic equilibrium point for the set of parameters above.

In Figures 3(a)-(d), the variation of proportions of exposed, recovered, acute and chronic infective populations for different rates of education, health care and immunization is shown.

Figures 3(a)-(d), shows that the infected population decreases as the control strategies (education, health care and immunization), increased. This confirms that, if is not effective the disease will invade the population.

Figures 4(a)-(d) shows the variation of proportions of exposed, acute and chronic infective populations and recovered population for different rates of screening.

From Figures 4(a)-(d) we vary the screened rate of infected immigrants, and it is seen that as the degree of screening increases, the infected population decreases. The results further show that increasing the screening rate, decreases the severity of the epidemic. Once again this confirms that, screening can reduce the inflow of infected immigrants into the community.