Fractional-order modeling of influenza dynamics: Impact of memory effect and severity-based variations

In order to examine the dynamics of seasonal influenza transmission, this study suggests a fractional-order epidemiological model that was created using the Caputo fractional derivative. The model effectively replicates the observed pandemic progression over a order parameter, with an ideal value of [Formula: see text], offers better accuracy than traditional show that a non-integer order parameter, with an ideal value of [Formula: see text], offers better accuracy than traditional integer-order models. The idea of memory trace, which depicts the impact of previous infection levels on current disease dynamics, is a significant contribution of this work. This characteristic makes it possible to comprehend the impact of past immunity on present epidemiological results in greater detail. Furthermore, the model provides a more accurate depiction of disease spread by taking into consideration variation in infection severity among various population groups. The findings show that incorporating population heterogeneity and memory effects improves predictive performance and offers insightful information for designing public health interventions and policies. Overall, this study highlights fractional calculus’s potential as a potent instrument for enhancing the precision and usefulness of epidemiological models in the control of infectious diseases.