Advancing real-time infectious disease forecasting using large language models

Hongru Du, Yang Zhao, Jianan Zhao, Shaochong Xu, Xihong Lin, Yiran Chen, Lauren M. Gardner, Hao Frank Yang

Abstract: Forecasting the short-term spread of an ongoing disease outbreak poses a challenge owing to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables, and the intersection of public policy and human behavior. Here we introduce PandemicLLM, a 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. This approach, through an artificial intelligence–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 tested across all 50 states of the United States for a duration of 19 months. PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and shows performance benefits over existing models. PandemicLLM adapts the large language model to predict disease trends by converting diverse disease-relevant data into text. It responds to new variants in real time, offering robust, interpretable forecasts for effective public health responses.

External IDs:dblp:journals/ncs/DuZZXLCGY25