Improving Influenza Epidemiological Models under Caputo Fractional-Order Calculus

Author:

E. Alsubaie Nahaa1ORCID,EL Guma Fathelrhman23ORCID,Boulehmi Kaouther34ORCID,Al-kuleab Naseam5ORCID,A. Abdoon Mohamed67ORCID

Affiliation:

1. Department of Mathematics, AlKhurmah University College, Taif University, Taif 29353, Saudi Arabia

2. Department of Statistical Study, Alsalam University, Alfula 54411, Sudan

3. Department of Mathematics, Faculty of Science, Al-Baha University, Albaha 65525, Saudi Arabia

4. Faculty of Science of Bizerte, (UR17ES21) Dynamical Systems and Their Applications, University of Carthage, Jarzouna 7021, Tunisia

5. Department of Mathematics and Statistics, College of Science, King Faisal University, Al-Ahsa 31982, Saudi Arabia

6. Department of Basic Sciences, King Saud University, Riyadh 12373, Saudi Arabia

7. Department of Mathematics, Faculty of Science, Bakht Al-Ruda University, Duwaym 28811, Sudan

Abstract

The Caputo fractional-order differential operator is used in epidemiological models, but its accuracy benefits are typically ignored. We validated the suggested fractional epidemiological seasonal influenza model of the SVEIHR type to demonstrate the Caputo operator’s relevance. We analysed the model using fractional calculus, revealing its basic properties and enhancing our understanding of disease progression. Furthermore, the positivity, bounds, and symmetry of the numerical scheme were examined. Adjusting the Caputo fractional-order parameter α = 0.99 provided the best fit for epidemiological data on infection rates. We compared the suggested model with the Caputo fractional-order system and the integer-order equivalent model. The fractional-order model had lower absolute mean errors, suggesting that it could better represent sickness transmission and development. The results underline the relevance of using the Caputo fractional-order operator to improve epidemiological models’ precision and forecasting. Integrating fractional calculus within the framework of symmetry helps us build more reliable models that improve public health interventions and policies.

Publisher

MDPI AG

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