Dynamical analysis of a fractional-order avian-human influenza epidemic model with logistic growth for avian population

Ye, Xingyang; Lin, Shunfu; Xu, Chuanju

doi:10.1177/1748302620966704

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Recently, fractional-order derivative models have been shown to outperform integer-order derivative models in terms of accuracy in various domains of engineering, physics, and biology. Moreover, fractional-order calculus equations are associated with memory-containing systems seen in most biological systems (Ye et al, 2020).…”

Section: Nonlinear Fractional-order Modeling Of Covid-19 Outbreakmentioning

confidence: 99%

Nonlinear controller design for a fractional extended model of COVID-19 outbreak using feedback linearization method

Khoshdel

Sadati

Nozari

2022

Transactions of the Institute of Measurement and Control

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This paper proposes a novel fractional-order epidemic model for the COVID-19 outbreak using the Caputo derivative that incorporates various intervention policies to manage the spread of the disease. A total of eight state variables were considered in this nonlinear model, namely, susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible. Two possible outbreak scenarios were considered to control the disease before and after vaccine discovery. The proposed system was designed using the feedback linearization method, allowing to develop a suitable controller for reducing susceptible, exposed, and infected populations. A comparative study with previous work was conducted based on Canada’s reported cases to demonstrate the advantages and disadvantages of the proposed COVID-19 outbreak control strategy using this model. The simulation results confirmed that the proposed fractional controller could effectively track the desired goals, including an exponential decrease in infected, exposed, and susceptible populations.

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Section: Nonlinear Fractional-order Modeling Of Covid-19 Outbreakmentioning

confidence: 99%