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

TL;DR

This paper introduces PandemicLLM, a multi-modal large language model framework that reformulates pandemic forecasting as a text reasoning task, effectively integrating diverse data types for improved COVID-19 spread predictions.

Contribution

The work presents a novel LLM-based framework that incorporates multi-modal data and text reasoning for real-time pandemic forecasting, surpassing traditional models in performance and flexibility.

Findings

PandemicLLM outperforms existing models in COVID-19 forecasting accuracy.

The framework effectively captures the impact of emerging variants.

It demonstrates the ability to incorporate heterogeneous pandemic data.

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.