Supervising the vaccinations and treatment control gains in a discrete SEIADR epidemic model

R. Nistal; M. de la Sen; S. Alonso-Quesada; A. Ibeas

doi:10.24507/ijicic.15.06.2053

Supervising the vaccinations and treatment control gains in a discrete SEIADR epidemic model

R. Nistal, M. de la Sen, S. Alonso-Quesada, A. Ibeas

Department of Telecommunications and Systems Engineering

Research output: Contribution to journal › Article › Research

6 Citations (Scopus)

Abstract

A discrete SEIADR (Susceptible-Exposed-Infectious-Asymptomatic-Dead-Recovered) epidemic model is proposed based on a continuous one and its fundamental properties are discussed such as the non negativity and the stability of the equilibrium points. A supervised control gain based on vaccination strategies is proposed in order to minimize a certain cost figure defined for the system and associated to the spreading of the disease. Some simulation examples are provided in order to show that the supervised strategy proposed in this paper is able to improve the efficacy of infection removal.

Original language	English
Pages (from-to)	2053-2069
Number of pages	17
Journal	International Journal of Innovative Computing, Information and Control
Volume	15
Issue number	6
DOIs	https://doi.org/10.24507/ijicic.15.06.2053
Publication status	Published - Dec 2019

Keywords

Discrete models
Epidemic models
Nonlinear dynamics and control systems
Reproduction number
Vaccination controls

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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10.24507/ijicic.15.06.2053

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AU - Ibeas, A.

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AB - A discrete SEIADR (Susceptible-Exposed-Infectious-Asymptomatic-Dead-Recovered) epidemic model is proposed based on a continuous one and its fundamental properties are discussed such as the non negativity and the stability of the equilibrium points. A supervised control gain based on vaccination strategies is proposed in order to minimize a certain cost figure defined for the system and associated to the spreading of the disease. Some simulation examples are provided in order to show that the supervised strategy proposed in this paper is able to improve the efficacy of infection removal.

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KW - Nonlinear dynamics and control systems

KW - Reproduction number

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JF - International Journal of Innovative Computing, Information and Control

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ER -

Supervising the vaccinations and treatment control gains in a discrete SEIADR epidemic model

Abstract

Keywords

UN SDGs

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