发热伴出疹症候群监测预警研究进展

林帆; 郭玉清; 吴彦霖; 李开明; 郑亚明; 王丽萍

Abstract

林帆,郭玉清,吴彦霖,李开明,郑亚明,王丽萍.发热伴出疹症候群监测预警研究进展[J].Chinese journal of Epidemiology,2024,45(3):455-463

发热伴出疹症候群监测预警研究进展

Progress in research of rash and fever syndrome surveillance and early warning

Received:July 24, 2023

DOI：10.3760/cma.j.cn112338-20230724-00034

KeyWord: 发热伴出疹症候群监测预警系统综述

English Key Word: Rash and fever syndrome Surveillance and early warning Systematic review

FundProject:国家科技重大专项（2018ZX10713001）；公共卫生应急反应机制（102393220020010000017）

Author Name	Affiliation	E-mail
Lin Fan	Division of Infectious Disease/National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Guo Yuqing	Division of Infectious Disease/National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Wu Yanlin	Division of Infectious Disease/National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Li Kaiming	Division of Infectious Disease/National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Zheng Yaming	Division of Infectious Disease/National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China	zhengym@chinacdc.cn
Wang Liping	Division of Infectious Disease/National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China

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Abstract:

目的对发热伴出疹症候群（RFS）监测预警研究进行系统综述，为我国RFS监测与防控提供参考。方法以“发热”“出疹”“监测”以及“fever” “rash” “surveillance”为中英文检索词并补充其自由词，系统检索中国知网、万方数据知识服务平台、PubMed、Web of Science数据库，语种限定为中文及英文，对纳入文献信息进行摘录整理及汇总描述。结果共纳入36篇文献，中英文分别为21篇和15篇。RFS病原学监测共19篇，病原体主要包括麻疹病毒、水痘-带状疱疹病毒、风疹病毒、肠道病毒、人类B19病毒、登革病毒、A组链球菌、伤寒/副伤寒沙门菌、人疱疹病毒、腮腺炎病毒和腺病毒；重大活动/突发自然灾害监测8篇，监测时段集中在2010-2015年，包括运动会、世界博览会、地震、热带风暴和宗教集会等；异常病例/聚集性疫情监测预警8篇，以国外研究为主，监测症候群范围广，监测场所为医疗机构，主要依靠《国际疾病分类》第九版（ICD-9）编码或患者主诉中诊断及症状进行自动判别预警；仅1篇针对预测研究，为蒙古国开展的基于传染病监测数据预测研究。36篇文献的分析方法包括描述性分析法、基于时间的预警模型（如回归分析法、固定阈值法、休哈特控制图法及累积和控制图法）和时间序列分析方法。结论未来RFS监测体系方向应聚焦已知病原和可能的未知病原监测预警相结合的模式，可进一步完善预警系统设计，实现无感化病例监测与早期预警，应用当下的信息抓取技术和智能建模方法，提高RFS监测预警的敏感度和特异性。

English Abstract:

Objective To introduce the progress in research of rash and fever syndrome (RFS) surveillance and early warning both at home and abroad, and provide reference for surveillance and prevention of RFS in China. Methods The keywords "fever" "rash" and "surveillance" and others were used for a literature retrieval by using China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, PubMed and Web of Science. The languages of literatures were limited in Chinese and English. The key information of the literatures were collected and analyzed with Excel. Results A total of 36 study papers (21 in Chinese and 15 in English) were included. The studies mainly focused on the pathogen surveillance of RFS (n=19). The pathogens included measles virus, varicella-zoster virus, rubella virus, enterovirus, human B19 virus, dengue virus, streptococcus group A, Salmonella typhi and Salmonella paratyphoid,human herpesvirus, mumps virus and adenovirus. Eight studies were about the surveillance in major events, such as sport game, World Expo and religious gathering, or sudden natural disasters, such as earthquake and tropical storm, during 2010-2015. Eight studies focused on case or epidemic surveillance, most of which were studies from other counties. The surveillance sites were medical institutions. RFS was diagnosed according to the International Classification of Diseases, 9^th (ICD-9) and symptoms descripted in chief-complaint. Only one study in Mongolia conducted RFS epidemic prediction. The analysis methods of 36 papers included simple descriptive analysis, time-based early warning models (such as regression analysis, fixed threshold method, Hugh Hart control chart method and cumulative sum control chart method) and time series analysis method. Conclusions In the future, RFS surveillance system should cover both known pathogens and emerging pathogens. Automatic surveillance using information capture and intelligent modelling can be applied to improve the sensitivity and specificity of RFS surveillance and early warning.

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