【摘要】 监测预警是传染病防控的重要支撑，但现有体系存在信息来源单一等局限，难以实现传染病的早期预警和及时处置。真实世界数据（RWD）具有来源多元、覆盖广泛等特点，结合大数据、人工智能等技术，在构建智慧化多点触发传染病监测预警体系中展现出显著优势。本文系统检索近20年相关文献，梳理国内外传染病监测预警体系的发展现状，分析RWD的主要来源及其在信号捕获、交叉验证和风险评估等环节的应用，并介绍实践案例与实现路径。目前，基于RWD实现智慧化传染病监测预警仍面临数据质量参差不齐、算法较单一和跨部门协作不畅等挑战。建议聚焦多源数据深度融合、建立标准共享机制、加强跨学科与跨部门合作，逐步构建高效的智慧化传染病监测预警体系，进一步提升传染病的早期识别与快速响应能力。更多还原

【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.更多还原