Application of Bayesian Inference Methods in Modeling Epidemiological Data: A Comparative Study in Infectious Diseases
DOI:
https://doi.org/10.55204/trc.v5i1.e453Keywords:
Bayesian inference, epidemiological modeling, infectious diseases, comparison of methods, statistical analysisAbstract
Bayesian inference has emerged as a powerful tool in the modeling of epidemiological data, especially in the context of infectious diseases. This study compares the efficacy of Bayesian methods versus traditional frequentist approaches in estimating key parameters and predicting epidemic outbreaks. Using simulated and real data from various infectious diseases, Bayesian and frequentist models were evaluated in terms of accuracy, uncertainty management, and adaptability to incomplete data. The results indicate that Bayesian models offer more robust and flexible estimates, especially in scenarios with limited data or high variability. It is concluded that the adoption of Bayesian methods can significantly improve epidemiological surveillance and outbreak response
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Copyright (c) 2025 Nancy Elizabeth Chariguamán Maurisaca
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