Epidemiology often deals with uncertain and complex data, making traditional frequentist approaches less effective. PyMC3 provides a Bayesian framework that can incorporate prior knowledge and handle uncertainty in a principled way. This is crucial for making robust inferences about disease dynamics, risk factors, and intervention effects. The library's flexibility allows for custom model building, which is essential in a field as diverse as epidemiology.
