Public Health Surveillance

Public health surveillance aims to monitor and predict the spread of diseases and health-related information, enabling timely interventions. Current research heavily emphasizes leveraging artificial intelligence, particularly large language models and graph neural networks, to analyze diverse data sources like social media, news articles, and medical reports, improving the speed and accuracy of disease detection and rumor tracking. These advancements are improving the efficiency and effectiveness of public health responses, particularly in resource-constrained settings, and facilitating more nuanced understanding of disease dynamics and information spread. The development of domain-specific pretrained language models further enhances the ability to extract actionable insights from unstructured data.