@article{Shafqat2025, 
author = {Ramsha Shafqat and Ateq Alsaadi},
title = {Mathematical and numerical analysis of a fractional SIQR epidemic model with normalized Caputo–Fabrizio operator and machine learning approaches},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {9},
pages = {20235-20261},
keywords = {fractional differential equation, normalized Caputo–Fabrizio, SIQR model, numerical outcomes, machine learning},
url = {https://www.sciopen.com/article/10.3934/math.2025904},
doi = {10.3934/math.2025904},
abstract = {This paper introduces, analyzes, and numerically investigates a fractional-order SIQR epidemic model with the normalized Caputo–Fabrizio derivative. The model captures memory effects and the impact of quarantine or isolation interventions, offering a more realistic description of epidemic dynamics. We establish the existence, uniqueness, positivity, and population conservation properties, and then propose a robust numerical scheme. The influence of the memory parameter and kernel normalization is illustrated via simulations, with a discussion on their implications for epidemic forecasting and real-world control strategies. Furthermore, artificial neural networks are applied, with the dataset partitioned into training, validation, and testing subsets. A comprehensive assessment is carried out for each dataset partition.}
}