The discrete fractional order difference applied to an epidemic model with indirect transmission
TLDR
In this paper, a discrete fractional order model is proposed to analyze the behavior of an epidemic process with indirect transmission, which is based on a discrete version of the Grunwald-Letnikov fractional derivative operator.About:
This article is published in Applied Mathematical Modelling.The article was published on 2022-03-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Epidemic model & Fractional calculus.read more
Citations
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A brief note on fractal dynamics of fractional Mandelbrot sets
TL;DR: In this paper , a connectivity index is proposed based on numerical methods, which avoids difficulties in discussion at the topological level, and the dynamics of those sets in two kinds of noise environments are considered involving connectivity, symmetry and dimension.
Journal ArticleDOI
Stability and bifurcation control analysis of a delayed fractional-order eco-epidemiological system
Haotian Qi,Wen Cai Zhao +1 more
TL;DR: In this article , a delayed fractional eco-epidemiological system with an extended feedback controller is proposed, where the authors considered the factor of artificial intervention in biological control, and they used the digestion delay as bifurcation parameter.
Journal ArticleDOI
Malware propagation model of fractional order, optimal control strategy and simulations
TL;DR: In this article , an improved SEIR model of fractional order is investigated to describe the behavior of malware propagation in the wireless sensor network, and the optimal control strategy is also discussed based on the adaptive model.
References
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TL;DR: In this article, the authors present a method for computing fractional derivatives of the Fractional Calculus using the Laplace Transform Method and the Fourier Transformer Transform of fractional Derivatives.
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Some discrete-time SI, SIR, and SIS epidemic models
TL;DR: Single-population and multi-population, discrete-time epidemic models are analyzed and positive feedback from the infective class to the susceptible class allows for more diverse behavior in the discrete model.
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