EPIWATCH, an artificial intelligence early-warning system as a valuable tool in outbreak surveillance

作者：Dr Ashley Quigley, Mr Damian Honeyman, Mars Stone, Dr Rebecca Dawson, Professor C Raina MacIntyre · 发表于：International Journal of Infectious Diseases · 年份：2025 · DOI：10.1016/j.ijid.2024.107579 · 被引用次数：8 · 研究领域：Data-Driven Disease Surveillance、Anomaly Detection Techniques and Applications

Introduction: Utilizing artificial intelligence (AI) for outbreak detection presents a transformative approach to public health surveillance. In challenging environments where traditional surveillance methods may be insufficient, absent or compromised, AI using open-source data can provide epidemic intelligence to inform infectious disease control by identifying early warning signals of disease outbreaks. EPIWATCH is an AI-driven outbreak early-detection and monitoring system, proven to provide early signals of epidemics before official detection by health authorities. Aim: The aim of this study was to evaluate a case study of the utility of open-source epidemic intelligence. Methods & Materials: EPIWATCH reports of outbreaks of unspecified influenza-like illness and pneumonia (syndromic surveillance), together with known causes influenza A and B, SARS-CoV-2, RSV, pertussis (whooping cough), adenovirus and Mycoplasma for August – December of 2022 and 2023, were extracted and summarised to look at trends in respiratory illness in China during a known Mycoplasma pneumonia outbreak in 2023. This was compared to trends in global data for the same period. To investigate the use of EPIWATCH as a valuable surveillance tool in conflict zones, we examined the epidemiology of infectious diseases in Ukraine by utilizing data from EPIWATCH. The analysis focused on infectious disease patterns and syndromes prior to (1 November 2021 to 23 February 2022) and during the conflict (24 Febr...