Optimal Control Strategy for SEIR with Latent Period and a Saturated Incidence Rate

We propose an SEIR epidemic model with latent period and a modified saturated incidence rate. This work investigates the fundamental role of the vaccination strategies to reduce the number of susceptible, exposed, and infected individuals and increase the number of recovered individuals. The existence of the optimal control of the nonlinear model is also proved. The optimality system is derived and then solved numerically using a competitive Gauss-Seidel-like implicit difference method.


Introduction
Epidemiological models with latent or incubation period have been studied by many authors because many diseases have a latent or incubation period, during which the individual is said to be infected but not infectious.This period can be modeled by introducing an exposed class [1].Therefore, it is an important subject to determine the optimal vaccination strategies for the models which take into account the incubation period.
In this paper, our aim is to set up an optimal control problem related to the SEIR epidemic model.The dynamics of this model are governed by the following equations [2,3]: where  is the number of the susceptible individuals,  is the number of exposed individuals,  is the number of infected individuals,  is the number of the recovered individuals,  is the recruitment rate of the population,  is the natural death of the population,  is the death rate due to disease,  is the transmission rate,  1 and  2 are the parameter that measure the inhibitory effect,  is the recovery rate of the infective individuals, and  is the rate at which exposed individuals become infectious.Thus 1/ is the mean latent period.(2)

ISRN Applied Mathematics
In addition, for biological reasons, we assume that the initial data for system (2) satisfy The rest of the paper is organized as follows.In Section 2, we use Pontryagin's maximum principle to investigate analysis of control strategies and to determine the necessary conditions for the optimal control of the disease.Mathematical results are illustrated by numerical simulations in Section 3. Finally, we summarize our work and propose the future focuses.

The Optimal Control Problem
The optimal control problem is to minimize the objective (cost) functional given by subject to the differential equations (2), where the first tree terms in the functional objective represent benefit of (), (), and () populations that we wish to reduce, and the parameters  1 ,  2 , and  3 are positive constants to keep a balance in the size of (), (), and (), respectively.We use in the second term in the functional objective (as it is customary) the quadratic term (1/2) 2 , where  is a positive weight parameter which is associated with the control (), and the square of the control variable reflects the severity of the side effects of the vaccination.
Our target is to minimize the objective functional defined in (4) by decreasing the number of infected, exposed, and susceptible individuals and increasing the number of recovered individuals by using possible minimal control variables ().In other words, the control variable () ∈  ad represents the percentage of susceptible individuals being vaccinated per unit of time and  ad is the control set defined by (5)

Existence of an Optimal Control.
For the existence of an optimal control we use the result in Lukes [4], and we obtain the following theorem.

Numerical Simulations
In this section, we solve numerically the optimality system (16) using the Gauss-Seidel-like implicit finite-difference method developed by Gumel et al. [7], and we use in this simulation the parameter values given in Table 1.
Figure 1 shows that a significant difference in the number of susceptible, exposed, infected, and recovered individuals with and without control from the twenty days of vaccination, after that it begins to go to the stable state.

Conclusions and Future Research
We have presented in this paper the SEIR model with latent period and a modified saturated incidence rate.Our aim is to outline the steps in setting up an optimal control problem, so we presented an efficient numerical method based on optimal control to identify the best vaccination strategy of SEIR model.Our numerical results show that the optimal vaccination strategies for the diseases have a latent period to reduce the number of susceptible, exposed, and infected individuals and increase the number of recovered after twenty days of vaccination.
In future research, we determine the optimal control strategies for the delayed SIR model and compare it with that presented in this work.It is an important subject to study these two types of modeling the incubation period.

Figure 1 :
Figure 1: Evolution of different classes of individuals with or without control.