Epidemic Forecasting

Epidemic forecasting aims to predict the spread of infectious diseases, enabling proactive public health interventions and resource allocation. Current research heavily utilizes machine learning, employing diverse architectures like neural ordinary differential equations, graph neural networks (including those incorporating physical constraints), and transformer models to analyze spatiotemporal data from multiple sources (e.g., epidemiological time series, social media, weather patterns). These advancements improve forecasting accuracy and uncertainty quantification, offering valuable insights for public health decision-making and potentially enhancing pandemic preparedness and response.