Bayesian models are particularly useful in epidemiology for several reasons:
1. Incorporation of Prior Knowledge: They allow the integration of prior knowledge or expert opinion into the analysis, which can be crucial when dealing with new or emerging diseases. 2. Flexibility: Bayesian models can handle complex data structures and multiple levels of uncertainty, making them versatile tools for various epidemiological studies. 3. Dynamic Updating: As new data becomes available, Bayesian models can be updated dynamically, providing real-time insights into disease spread and control measures.
