AI-driven epidemic intelligence: the future of outbreak detection and response

Epidemic intelligence, the process of detecting, verifying, and analyzing public health threats to enable timely responses, traditionally relies heavily on manual reporting and structured data, often causing delays and coverage gaps. The growing frequency of emerging infectious diseases highlights the urgency for more rapid and accurate surveillance methods. This perspective proposes a forward-looking conceptual framework for AI-driven epidemic intelligence, emphasizing the transformative potential of integrating large language models (LLMs), natural language processing (NLP), and optimization-based resource allocation strategies. While existing AI-driven systems have shown significant capabilities during the COVID-19 pandemic, several challenges remain, including real-time adaptability, multilingual data handling, misinformation, and public health policy alignment. To address these gaps, we propose an integrated, real-time adaptable LLM-based epidemic intelligence system, capable of correlating cross-source data, optimizing healthcare resource allocation, and supporting informed outbreak response. This approach aims to significantly improve early warning capabilities, enhancing forecasting accuracy, and strengthen pandemic preparedness.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.