In the field of epidemiology, GANs hold the potential to revolutionize the way we analyze and interpret data. Here are some key applications:
Disease Simulation: GANs can create realistic simulations of disease spread, helping researchers understand potential future scenarios and plan accordingly. Data Augmentation: By generating synthetic patient data, GANs can augment existing datasets, making them more robust for training machine learning models. Privacy Preservation: Synthetic data generated by GANs can be used in place of real patient data, ensuring privacy while still allowing for meaningful analysis. Anomaly Detection: GANs can identify unusual patterns or anomalies in health data, potentially flagging new outbreaks or unusual disease presentations.
