RNNs can model complex, temporal dependencies in [epidemiological data](https://) such as disease incidence, prevalence, and [spread patterns](https://). Unlike traditional neural networks, RNNs can use their internal state to process sequences of inputs, making them ideal for predicting the future course of an epidemic based on historical data.
