Epidemic Model

Epidemic modeling aims to understand and predict the spread of infectious diseases, informing public health interventions. Current research heavily utilizes machine learning, including graph neural networks, physics-informed neural networks, and large language models, to analyze diverse data sources (e.g., social networks, mobility patterns, news reports) and improve the accuracy and timeliness of predictions. These advanced modeling techniques are coupled with optimization algorithms and agent-based modeling to explore optimal control strategies and the impact of various interventions. The resulting models offer valuable tools for resource allocation, policy design, and ultimately, mitigating the impact of future outbreaks.