EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
Advanced epidemic forecasting is critical for enabling precision containment
strategies, highlighting its strategic importance for public health security.
While recent advances in Large Language Models (LLMs) have demonstrated
effectiveness as foundation models for domain-specific tasks, their potential
for epidemic forecasting remains largely unexplored. In this paper, we
introduce EpiLLM, a novel LLM-based framework tailored for spatio-temporal
epidemic forecasting. Considering the key factors in real-world epidemic
transmission: infection cases and human mobility, we introduce a dual-branch
architecture to achieve fine-grained token-level alignment between such complex
epidemic patterns and language tokens for LLM adaptation. To unleash the
multi-step forecasting and generalization potential of LLM architectures, we
propose an autoregressive modeling paradigm that reformulates the epidemic
forecasting task into next-token prediction. To further enhance LLM perception
of epidemics, we introduce spatio-temporal prompt learning techniques, which
strengthen forecasting capabilities from a data-driven perspective. Extensive
experiments show that EpiLLM significantly outperforms existing baselines on
real-world COVID-19 datasets and exhibits scaling behavior characteristic of
LLMs.
