Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

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,...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Du, Hongru, Zhao, Jianan, Zhao, Yang, Xu, Shaochong, Lin, Xihong, Chen, Yiran, Gardner, Lauren M, Yang, Hao Frank
Format: Article
Language:English
Subjects:
Complexity
COVID-19
Demographic variables
Demographics
Epidemiology
Forecasting
Large language models
Machine learning
Mathematical models
Modal data
Pandemics
Public health
Public policy
Real time
Representations
Robustness (mathematics)
Time series
Viral diseases
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Summary: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.
ISSN:2331-8422