Fractional Calculus in Complex System Modeling
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Abstract
Fractional calculus has gained attention in epidemiology for its ability to enhance disease modeling by incorporating memory effects and non-integer order derivatives. This paper reviews key studies that apply fractional calculus to disease modeling, particularly in COVID-19 and tuberculosis, demonstrating its advantages over traditional integer-order models. By analyzing population heterogeneity, dynamic transitions, and stochastic effects, fractional models provide a more accurate representation of disease spread. The review showcases the significance of fractional calculus in capturing complex disease dynamics and its potential role in future epidemiological research and policymaking.
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