HIV Surveillance

HIV surveillance aims to track the spread of HIV infection and inform public health interventions. Current research focuses on improving data integration, leveraging diverse data sources like healthcare service records alongside traditional surveillance data, and employing machine learning techniques, including deep learning and causal inference models, to optimize resource allocation and improve case detection, particularly among those who may be missed by traditional methods. These advancements are crucial for enhancing the accuracy and effectiveness of HIV prevention and treatment strategies, ultimately impacting the global fight against the disease.