Development and implementation of innovative higher order inverse polynomial method for tackling physical models in epidemiology

Volume 36, Issue 4, pp 444--454 https://dx.doi.org/10.22436/jmcs.036.04.07

Publication Date: August 24, 2024 Submission Date: May 04, 2024 Revision Date: May 29, 2024 Accteptance Date: June 29, 2024

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Authors

S‎. ‎E‎. ‎ Fadugba - Department of Mathematics‎, Ekiti State University‎, Ado Ekiti‎, ‎360001, Nigeria. M‎. ‎C‎. ‎ Kekana - Department of Mathematics‎, Tshwane University of Technology‎, Pretoria, South Africa. N. Jeeva - PG and Research Department of Mathematics, The Madura College‎, Madurai‎, Tamil Nadu, India. I‎. ‎Ibrahim - Department of Mathematics‎, Federal University, Dutse‎, Nigeria.

Abstract

‎This study introduces a new approach termed the Higher Order Inverse Polynomial Method (HOIPM) to tackle diverse model types‎. ‎We analyze HOIPM's unique attributes and verify them through three illustrative scenarios‎. ‎Additionally‎, ‎we conduct a comparative assessment with the classical fourth-order Runge-Kutta method (RK4) to evaluate accuracy and computational efficiency‎. ‎Real-world applications‎, ‎such as predator-prey‎, ‎SIR‎, ‎and SEIR models‎, ‎highlight HOIPM's effectiveness‎. ‎

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Keywords

• Computing performance
• convergence feature
• error
• precise value
• veracity

MSC

•  34A12
•  65L05
•  65L20
•  65L70

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