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

• URL: http://arxiv.org/abs/2408.14277v1
• Date: Mon, 26 Aug 2024 13:53:04 GMT
• Title: Epidemic Information Extraction for Event-Based Surveillance using Large Language Models
• Authors: Sergio Consoli, Peter Markov, Nikolaos I. Stilianakis, Lorenzo Bertolini, Antonio Puertas Gallardo, Mario Ceresa,
• Abstract summary: This paper presents a novel approach to epidemic surveillance, leveraging the power of Artificial Intelligence and Large Language Models (LLMs) LLMs can significantly enhance the accuracy and timeliness of epidemic modelling and forecasting, offering a promising tool for managing future pandemic events.
• Score: 2.1679642267617756
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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