z-logo
open-access-imgOpen Access
Caputo fractional-order SEIRP model for COVID-19 Pandemic
Author(s) -
Saheed Ojo Akindeinde,
Eric Okyere,
Adebayo Olusegun Adewumi,
Ramoshweu Solomon Lebelo,
O. O. Fabelurin,
Stephen E. Moore
Publication year - 2021
Publication title -
alexandria engineering journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.584
H-Index - 58
eISSN - 2090-2670
pISSN - 1110-0168
DOI - 10.1016/j.aej.2021.04.097
Subject(s) - uniqueness , mathematics , stability (learning theory) , basic reproduction number , extension (predicate logic) , epidemic model , fixed point theorem , context (archaeology) , nonlinear system , fractional calculus , equilibrium point , population , integer (computer science) , mathematical optimization , mathematical analysis , computer science , differential equation , paleontology , physics , demography , quantum mechanics , machine learning , sociology , biology , programming language
We propose a Caputo-based fractional compartmental model for the dynamics of the novel COVID-19 transmission dynamics. The newly proposed nonlinear fractional order differential equation epidemic model is an extension a recently formulated integer-order COVID-19 mathematical model. Using basic concepts such as continuity and Banach fixed-point theorem, the existence and uniqueness of the solution to the proposed model were shown. Furthermore, we analyze the stability of the model in the context of Ulam-Hyers and generalized Ulam-Hyers stability criteria. The concept of next-generation matrices was used to compute the basic reproduction number R0, a number that determines the spread or otherwise of the disease into the general population. We also investigated the local asymptotic stability for the derived disease-free equilibrium point. Numerical simulation of the constructed epidemic model was carried out using the fractional Adam-Bashforth-Moulton method to validate the obtained theoretical results.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom