Vol. 6, Issue 2, Part A (2024)

In this paper presents a novel extension of the SIDARTHE model by integrating fuzzy logic and fractional calculus to enhance its predictive capabilities. The fuzzy fractional order SIDARTHE model incorporates uncertainties inherent in epidemiological parameters, such as transmission rates, recovery rates and mortality rates by defining these parameters as fuzzy sets. The COVID-19 pandemic has necessitated the development of advanced mathematical models to accurately capture the complex dynamics of the disease spread. Additionally, the fractional order differential equations provide a more flexible and accurate representation of the memory and hereditary properties of the epidemic process. The extended model is validated against real-world COVID- 19 data, demonstrating improved accuracy in capturing the disease's progression and offering better insights into the potential outcomes of various intervention strategies. Our findings suggest that the fuzzy fractional order approach not only refines the predictive performance of the SIDARTHE model but also offers a robust framework for handling the inherent uncertainties in epidemic modeling, thereby aiding policymakers in making informed decisions during health crises. This study highlights the integration of fuzzy logic and fractional calculus in extending the SIDARTHE model, emphasizing its improved accuracy and robustness in handling uncertainties and its practical implications for policymaking in epidemic scenarios.