Dynamical analysis, optimum control and pattern formation in the biological pest (EFSB) control model

In this research article, an optimal control model of dengue disease with standard incidence rate is proposed. Maximum Principle was employed to derive the necessary conditions for the existence of optimal control. Stochastic version of the model is derived by introducing a random perturbation in the main parameters of the model equations. Numerical solution of the optimality was derived and computed to investigate the optimum control strategy that would be efficacious to be implemented in reducing the number of exposed and infected humans as well as illustrating the explicit differences in the dynamics of the models

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An optimum control model for resistance fumigation for dengue

10.1063/1.5062765 ◽

2018 ◽

Author(s):

Dipo Aldila ◽

Kinanthi Nareswari ◽

Hengki Tasman

Keyword(s):

Control Model ◽

Optimum Control

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Stochastic Optimal Control Model of Haemorrhagic Conjunctivitis Disease

Journal of Advances in Mathematics and Computer Science ◽

10.9734/jamcs/2019/v31i330113 ◽

2019 ◽

pp. 1-19

Author(s):

Sacrifice Nana-Kyere ◽

Desmond Titus Banon ◽

Seth N. Marmah ◽

Daniel Kwarteng

Keyword(s):

Optimal Control ◽

Control Strategy ◽

Necessary Conditions ◽

Reproduction Number ◽

Control Model ◽

Optimum Control ◽

Stochastic Perturbations ◽

Stochastic Version ◽

Research Article ◽

Existence Of Optimal Control

In this research article, a model for the transmission dynamics of haemorrhagic conjunctivitis disease is presented. The tool of dynamical system is employed in investigating the potency of the spreading of the epidemic. The analysis revealed the likelihood of the epidemic to spread when the basic reproduction number exceeds one. The model is reformulated as optimal control problem to assess the effectiveness of the proposed control strategy. Maximum Principle was employed to derive the necessary conditions for the existence of optimal control. Numerical solution of the optimality was derived and computed to investigate the optimum control strategy that would be efficacious to be implemented in reducing the number of exposed and infected individuals. Stochastic version of the model is deduced by introducing stochastic perturbations in the deterministic one. Numerical simulations are provided to illustrate the differences in the dynamics of the models and to understand the epidemic phenomenon.

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