Construction and challenges of an intelligent infectious disease surveillance and early warning system based on real world data
10.19485/j.cnki.issn2096-5087.2026.04.012
- Author:
LI Lin
;
FENG-GAO Yujie
;
ZHU Xingyu
;
MENG Xiangjie
;
WU Chen
- Publication Type:Journal Article
- Keywords:
infectious diseases;
surveillance;
warning;
real world data
- From:
Journal of Preventive Medicine
2026;38(4):377-381
- CountryChina
- Language:Chinese
-
Abstract:
Abstract:Surveillance and early warning systems are fundamental to the prevention and control of infectious diseases. However, existing systems are constrained by limitations such as single-source data, making it difficult to achieve early detection and timely response to infectious diseases. Real world data (RWD), characterized by diverse sources and extensive coverage, combined with technologies such as big data and artificial intelligence, offer significant advantages in constructing intelligent, multi-trigger surveillance and early warning systems for infectious diseases. This article systematically reviews relevant literature published over the past two decades, outlines the current status of infectious disease surveillance and early warning systems both domestically and internationally, analyzes the main sources of RWD and their applications in signal detection, cross-validation, and risk assessment, and introduces practical case studies and implementation pathways. At present, the use of RWD for intelligent infectious disease surveillance and early warning still faces challenges, including variable data quality, relatively limited algorithms, and insufficient cross-sectoral collaboration. Recommendations include promoting deep integration of multi-source data, establishing standardized data-sharing mechanisms, and strengthening interdisciplinary and cross-sectoral cooperation, so as to progressively build an efficient intelligent surveillance and early warning system and further enhance the early identification and rapid response capabilities for infectious diseases.
- Full text:2026110916444868014基于真实世界数据的智慧化传染病监测预警体系构建与挑战.pdf
