Abstract
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.
Abstract (translated)
预测正在进行的疾病爆发的短期浮动是一个具有挑战性的任务，因为相关因素的复杂性，一些因素可以通过相互关联、多模态变量（如流行病学时间序列数据、病毒生物学、人口学、公共卫生与人类行为之间的交汇）进行特征化。现有的预测模型框架在处理相关数据的复杂性以及可靠结果的转化方面存在困难，这阻碍了它们的表现和对公共卫生决策者的实用性洞察力。我们的工作引入了PandemicLLM，一种新颖的多模态大型语言模型（LLM）框架，将疾病传播的实时预测重新建模为文本推理问题，具有将实时、复杂、非数值信息纳入传统预测模型的能力。通过独特的AI-人类合作提示设计和时间序列表示学习，为LLM编码了多模态数据。该模型应用于COVID-19大流行，并训练利用文本公共卫生政策、基因组监测、空间和流行病学时间序列数据，随后在所有50个州进行了测试。实证研究证明，PandemicLLM是一个高效的 pandemic forecasting 框架，有效捕捉了新兴变种的传播影响，并提供及时、准确的预测。所提出的PandemicLLM为将 various pandemic-related data以异构格式纳入模型以及展示现有模型的性能优势提供了道路。本研究阐明了将LLM和表示学习适应以提高 pandemic forecasting的潜力，表明 AI 创新可以在未来加强 pandemic 应对和危机管理。
URL
https://arxiv.org/abs/2404.06962
