Epidemic Time Series

Epidemic time series analysis focuses on modeling and forecasting the spread of infectious diseases using time-stamped data, aiming to improve public health interventions. Current research emphasizes the development of sophisticated machine learning models, including deep learning architectures like graph neural networks and convolutional neural networks, often incorporating pre-training techniques and physics-informed approaches to enhance accuracy and efficiency. These advanced methods aim to capture complex spatiotemporal dynamics and non-linear patterns in epidemic data, ultimately leading to more reliable predictions and better-informed resource allocation strategies. The improved forecasting capabilities offer significant potential for mitigating the impact of future outbreaks.