Epidemic Information Extraction for Event-Based Surveillance using Large Language Models

TL;DR

This paper explores using large language models to improve epidemic surveillance by extracting critical outbreak information from unstructured data sources, enhancing accuracy and timeliness of epidemic forecasting.

Contribution

It introduces a novel approach combining multiple LLMs and in-context learning to improve epidemic information extraction from unstructured data sources.

Findings

LLMs significantly improve epidemic data interpretation.

Ensemble models outperform individual LLMs.

Enhanced models lead to more timely epidemic forecasting.

Abstract

This paper presents a novel approach to epidemic surveillance, leveraging the power of Artificial Intelligence and Large Language Models (LLMs) for effective interpretation of unstructured big data sources, like the popular ProMED and WHO Disease Outbreak News. We explore several LLMs, evaluating their capabilities in extracting valuable epidemic information. We further enhance the capabilities of the LLMs using in-context learning, and test the performance of an ensemble model incorporating multiple open-source LLMs. The findings indicate that LLMs can significantly enhance the accuracy and timeliness of epidemic modelling and forecasting, offering a promising tool for managing future pandemic events.