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Benito M. Chen-Charpentier

Researcher at University of Texas at Arlington

Publications -  82
Citations -  1264

Benito M. Chen-Charpentier is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Population & Differential equation. The author has an hindex of 19, co-authored 76 publications receiving 998 citations. Previous affiliations of Benito M. Chen-Charpentier include University of Wyoming.

Papers
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A fractional order epidemic model for the simulation of outbreaks of influenza A(H1N1)

TL;DR: In this paper, a nonlinear fractional order model was proposed to explain and understand the outbreaks of influenza A(H1N1) in order to explain how the next state depends upon its current state but also upon all of its historical states.
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Construction of nonstandard finite difference schemes for the SI and SIR epidemic models of fractional order

TL;DR: It is concluded that the NSFD schemes, which are explicit and computationally inexpensive, are reliable methods to obtain realistic positive numerical solutions of the SI and SIR fractional-order epidemic models.
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An unconditionally positivity preserving scheme for advection–diffusion reaction equations

TL;DR: A new scheme is proposed that guarantees the positivity of the solutions for arbitrary step sizes for reaction terms that consist of the sum of a positive function and a negative function and is applicable to both advection and diffusion dominated problems.
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A nonstandard numerical scheme of predictor-corrector type for epidemic models

TL;DR: The nonstandard finite difference scheme with Conservation Law (NSFDCL) developed here satisfies some important properties associated with the considered SIR epidemic model, such as positivity, boundedness, monotonicity, stability and conservation of frequency of the oscillations.
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Modeling plant virus propagation with delays

TL;DR: A system of ordinary differential equations was first used to model the interaction between the insects and the plants, and the equilibria of the model were found and the stability was analyzed using the Reproductive number, derived by the next generation matrix.