6 papers
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Yiming Lu, Sihang Zeng, Zhengxu Tang +3
Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after…
EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning
Mingyang Wei, Dehai Min, Zewen Liu +8
Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effects at the population level. Existing m…
Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?
Zewen Liu, Juntong Ni, Xianfeng Tang +4
Uncovering hidden symbolic laws from time series data, as an aspiration dating back to Kepler's discovery of planetary motion, remains a core challenge in scientific discovery and…
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes
Zewen Liu, Juntong Ni, Bohan Wang +2
Accurate epidemic forecasting is crucial for outbreak preparedness, but existing data-driven models are often brittle. Typically trained on a single pathogen, they struggle with da…
Higher-order Interaction Matters: Dynamic Hypergraph Neural Networks for Epidemic Modeling
Songyuan Liu, Shengbo Gong, Tianning Feng +3
The ongoing need for effective epidemic modeling has driven advancements in capturing the complex dynamics of infectious diseases. Traditional models, such as Susceptible-Infected-…
Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph
Guancheng Wan, Zewen Liu, Max S. Y. Lau +2
Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases.…