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Mathematics
Article . 2022 . Peer-reviewed
License: CC BY
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Mathematics
Article . 2022
Data sources: DOAJ
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New Coronavirus (2019-nCov) Mathematical Model Using Piecewise Hybrid Fractional Order Derivatives; Numerical Treatments

Authors: Nasser H. Sweilam; Seham M. AL-Mekhlafi; Saleh M. Hassan; Nehaya R. Alsenaideh; Abdelaziz Elazab Radwan;

New Coronavirus (2019-nCov) Mathematical Model Using Piecewise Hybrid Fractional Order Derivatives; Numerical Treatments

Abstract

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented.

Keywords

hybrid fractional coronavirus (2019-nCov) mathematical models, nonstandard fractional Euler–Maruyama technique, piecewise numerical methods; hybrid fractional coronavirus (2019-nCov) mathematical models; nonstandard fractional Euler–Maruyama technique; fractional stochastic–deterministic models; Grünwald–Letnikov nonstandard finite difference method, QA1-939, piecewise numerical methods, fractional stochastic–deterministic models, Grünwald–Letnikov nonstandard finite difference method, Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
gold
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