One of the primary reasons for using multiple models in epidemiology is to account for the complexity and uncertainty inherent in disease transmission. Different models can incorporate various assumptions, data sources, and analytical techniques, which can lead to varied insights and predictions. For example, combining _deterministic_ models with _stochastic_ models can help in understanding both the average behavior of an epidemic and the random variations that might occur.
