The isolation and treatment of symptomatic individuals coupled with the quarantining of individuals that have a high risk of having been infected, constitute two commonly used epidemic control measures. Mass quarantine can inflict significant social, psychological and economic costs without resulting in the detection of many infected individuals. Day et al.  , Hethcote et al.  considered SIQS and SIQR epidemic models with three forms of incidence, which include the bilinear, standard and quarantined-adjusted incidences.
Feng and Thieme  considered SEIQR models with arbitrarily distributed periods of infection, including quarantine and a general incidence assumed that all infected individuals go through the quarantine stage and investigated the model dynamics. Settapat and Wirawah  discussed the SIQ epidemic model with constant immigration. Yang et al.  also studied an SIQ epidemic model with isolation and nonlinear incidence rate. El-Marouf and Alihaby  studied the equilibrium points and their local stability for SIQ and SIQR epidemic models with three forms of incidence rates. They also studied the global stability of the equilibrium by constructing the new forms of Lyapunov functions.
Gbadamosi and Adebimpe investigated an SIQ epidemic model with nonlinear incidence rate. They introduced the concept that describes the present and past states of the disease.
We extended the work of Gbadamosi and Adebimpe to include the rates at which individuals recover and return to susceptible compartment from compartments I and Q respectively and we apply Lyapunov functions and Poincare-Bendixson theorem plus Dulac’s criterion to prove the global stability of disease-free and endemic equilibria respectively.
2. The Model
The model that governs a system of differential equation is presented as follows:
Subject to initial conditions
The parameters with their descriptions are presented in Table 1.
The addition of the system (1), gives
From above equation, we get
From the first equation of the system (1), it follows
And the second equation gives
Table 1. Descriptions of parameters.
So, from the above, if , then .
We can now write
The system (1) has always the disease-free equilibrium at
3. Local Stability
In this section, we discussed the local stability of the disease-free equilibrium and endemic equilibrium for the system (1).
We state and prove the following results:
Theorem 1: At , the disease-free equilibrium of the system (1) is locally asymptotically stable when .
Proof: The Jacobian matrix at the point through linearization is given by
By finding the eigenvalues, we have the following :
For to be negative
Since and if , the disease-free equilibrium is locally asymptotically stable.
Theorem 3.1: The system (1) is locally asymptotically stable at if , otherwise unstable.
Proof: At the endemic equilibrium , the Jacobian matrix of the system (1) is given by:
The characteristic equation of the Jacobian matrix is given by
If , by Routh Hurwitz criterion, all the eigenvalues of the system (1) has negative real part. Therefore, the endemic equilibrium of the system (1) at is locally asymptotically stable.
4. Global Stability
In this section, we study the global stability of the disease-free equilibrium and endemic equilibrium by Lyapunov function and Poincare-Bendixson theorem respectively.
Theorem 3: (Dulac’s Criterion)
Consider the following general nonlinear autonomous system of de
Let where E is a simple connected region in R2. If the exists a function it such that is not identically zero and does not change sign in E, the system (*) has no close orbit lying entirely in E. if A is an annular region contained in E on which does not change sign, then there is at most one limit cycle of the system (*) in A.
Theorem 4: (The Poincare-Bendixson Theorem):Suppose that where E is an open subset of Rn and that the system (*) has a rejecting contained in a compact subset f of E. assume that the system (*) has only one unique equilibrium point x0 in f, then one of the following possibilities holds.
(a) is the equilibrium point x0
(b) is a periodic orbit
(c) is a graphic
Theorem 5: The disease-free equilibrium of the model (1) is globally asymptotically stable if
Proof: To prove this result, we construct the following Lyapunov function
where are positive constants to be determined later. Differentiating equation (3) with respect to t, we obtain
After rearrangements, we get
Let us choose the constants . Finally, we obtain
Thus, the disease-free equilibrium of the system (1) is globally asymptotically stable if
In the next theorem, we present the global stability of the endemic equilibrium of the system (1) at
Theorem 6: The endemic equilibrium of the system (1) is globally asymptotically stable if .
Proof: In order to prove the result, we use Dulac plus Poincare Bendixson theorem as follow
Hence, by Dulac’s criterion, there is no closed orbit in the first quadrant. Therefore, the endemic equilibrium is globally asymptotically stable.
5. Discussion of Results
The mathematical and stability analysis of SIQS epidemic model with saturated incidence rate and temporary immunity has been presented. We investigated the local stability of the disease-free equilibrium and endemic equilibrium using the basic reproduction number, . We observed that, when , the disease-free equilibrium is stable at locally and endemic equilibrium is unstable which means there is tendency for the disease to die out in the long run. We proved the global stability of the disease free equilibrium and endemic equilibrium of the model using Lyapunov function and Dulac’s criterion plus Poincare-Bendixson theorem respectively.
 Day, T., Park, A., Madras, N., Gumel, A. and Wu, J. (2006) When Is Quarantine a Useful Control Strategy for Emerging Infectious Diseases? American Journal of Epidemiology, 163, 479-485.
 Hethcote, H., Ma, Z. and Liao, S. (2002) Effects of Quarantine in Six Endemic Models for Infectious Diseases. Mathematical Biosciences, 180, 141-160.
 Feng, Z. and Thieme, H.R. (2000) Endemic Models with Arbitrarily Distributed Periods of Infection, I. General Theory. SIAM Journal on Applied Mathematics, 61, 803-833.
 Yang, X., Li, F. and Cheng, Y. (2012) Global Stability Analysis on the Dynamics of an SIQ Model with Nonlinear Incidence Rate. In: Advances in Future Computer and Control Systems, 2, Volume 160 of the series Advances in Intelligent and Soft Computing, 561-565.
 El-marouf, S.A.A. and Alihaby, S.M. (2011) Global Analysis of an Epidemic Model with Nonlinear Incidence Rate. Journal of Mathematics and Statistics, 7, 319-325.