TY  - JOUR
AU  - Shafqat, Ramsha
AU  - Alsaadi, Ateq
PY  - 2025
TI  - Mathematical and numerical analysis of a fractional SIQR epidemic model with normalized Caputo–Fabrizio operator and machine learning approaches
JO  - AIMS Mathematics
SP  - 20235
EP  - 20261
VL  - 10
IS  - 9
AB  - 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.
UR  - https://doi.org/10.3934/math.2025904
DO  - 10.3934/math.2025904