| Abstract: |
| The need for advanced modeling techniques in epidemiology has become increasingly evident. This presentation explores the integration of fuzzy set theory and genetic algorithms to enhance data fitting in fuzzy epidemic models. We focus on fuzzy epidemic models that accommodate uncertainties and population heterogeneity by treating epidemiological parameters as fuzzy variables. Our approach employs genetic algorithms for parameter estimation, enabling effective fitting of real-world epidemic data while addressing the complexities of disease transmission dynamics. The presentation highlights how genetic algorithms can refine model parameters and improve alignment between theoretical models and observed data. |
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