利用大型语言模型推进实时疫情预测：一项 COVID-19 案例研究
Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study
AI总结：
本文提出多模态 LLM 框架 PandemicLLM，将疫情实时预测转化为文本推理，融合政策、基因组、空间与流行病学数据，在全美 50 州 COVID-19 预测中优于现有模型。
摘要：
预测正在发生的疾病暴发的短期传播是一项艰巨挑战，原因在于影响因素复杂，其中一些因素可通过相互关联的多模态变量加以刻画，例如流行病学时间序列数据、病毒生物学、人口统计学，以及公共政策与人类行为的交叉。现有预测模型框架难以处理相关数据的多面性，也难以稳健地转化结果，这限制了其性能，并阻碍其向公共卫生决策者提供可执行洞见。我们的工作提出 PandemicLLM，这是一个采用多模态大型语言模型（LLM）的新颖框架，将疾病传播的实时预测重新表述为文本推理问题，并能够纳入传统预测模型此前无法获得的实时、复杂、非数值信息。该方法通过独特的 AI-人类协作提示设计和时间序列表示学习，为 LLM 编码多模态数据。该模型被应用于 COVID-19 疫情，经过训练以利用文本形式的公共卫生政策、基因组监测、空间和流行病学时间序列数据，随后在美国全部 50 个州接受测试。实证结果表明，PandemicLLM 是一个高性能疫情预测框架，能够有效捕捉新出现变异株的影响，并可提供及时、准确的预测。所提出的 PandemicLLM 为整合异构格式的各类疫情相关数据开辟了途径，并相较现有模型展现出性能优势。本研究阐明了改造 LLM 和表示学习以增强疫情预测的潜力，展示了 AI 创新未来如何能够强化疫情应对和危机管理。
英文摘要：
Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
