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.
@article{arxiv.2408.14277,
title = {Epidemic Information Extraction for Event-Based Surveillance using Large Language Models},
author = {Sergio Consoli and Peter Markov and Nikolaos I. Stilianakis and Lorenzo Bertolini and Antonio Puertas Gallardo and Mario Ceresa},
journal= {arXiv preprint arXiv:2408.14277},
year = {2024}
}
Comments
11 pages, 4 figures, Ninth International Congress on Information and Communication Technology (ICICT 2024)