Overfitting is a common problem in statistical modeling and machine learning where a model learns not only the underlying patterns in the training data but also the noise. This results in a model that performs well on the training data but poorly on unseen data. In the context of epidemiology, overfitting can lead to incorrect conclusions about disease patterns, transmission, and risk factors.
