用大模型预测疫情传播，效果优于传统方法
EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting
- 双分支架构对齐疫情数据与语言标记
- 自回归建模实现多步预测，准确率显著提升
- 适合公共卫生、流行病学研究者参考
精准的疫情预测对制定有效防控策略至关重要，具有重要的公共健康意义。尽管大语言模型（LLM）在特定领域任务中表现出色，但其在疫情预测中的潜力尚未充分探索。本文提出EpiLLM，一种面向时空疫情预测的新型LLM框架。结合感染病例和人类移动性两大现实传播因素，设计双分支架构，实现复杂疫情模式与语言标记之间的细粒度对齐。为释放LLM的多步预测与泛化能力，提出自回归建模范式，将疫情预测重构为下一个词元预测任务。为进一步增强模型对疫情的感知能力，引入时空提示学习技术，从数据驱动角度提升预测性能。大量实验表明，EpiLLM在真实世界新冠肺炎数据集上显著优于现有基线，并展现出大模型特有的缩放特性。
原文摘要 · Abstract (English)
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.
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