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| Construction and challenges of an intelligent infectious disease surveillance and early warning system based on real world data |
| LI Lin1, FENG-GAO Yujie2, ZHU Xingyu2, MENG Xiangjie1, WU Chen3
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1. Yuhang District Center for Disease Control and Prevention (Yuhang District Institute of Public Health Supervision), Hangzhou, Zhejiang 311100, China;
2. School of Public Health and Nursing, Hangzhou Normal University,Hangzhou, Zhejiang 311121, China;
3. Zhejiang Provincial Center for Disease Control and Prevention,Hangzhou, Zhejiang 310051, China |
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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.
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Received: 20 January 2026
Revised: 28 March 2026
Published: 21 April 2026
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[1] LIU F,PANAGIOTAKOS D.Real-world data:a brief review of the methods,applications,challenges and opportunities[J/OL].BMC Med Res Methodol,2022,22(1)[2026-03-28].https://doi.org/10.1186/s12874-022-01768-6.
[2] 介万,姚明宏,张军,等.真实世界数据质量评价指标研究[J].药物流行病学杂志,2026,35(1):62-74.
[3] 孙鑫,谭婧,王雯,等.建立真实世界数据与研究技术规范,促进中国真实世界证据的生产与使用[J].中国循证医学杂志,2019,19(7):755-762.
[4] U.S. Food and Drug Administration.Real-world evidence[EB/OL].[2026-03-28].http://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence.
[5] 国家药品监督管理局药品审评中心.用于产生真实世界证据的真实世界数据指导原则(试行)[EB/OL].[2026-03-28].https://www.cde.org.cn/main/news/viewInfoCommon/2a7e4c9e8b5d3f1a6c9e8b4d2a5f7c3e.
[6] 霍大柱,张婷,李中杰,等.传染病监测预警体系智慧化建设的特点、功能与实施策略[J].疾病监测,2025,40(1):16-22.
[7] 黄硕,刘才兄,邓源,等.世界主要国家和地区传染病监测预警实践进展[J].中华流行病学杂志,2022,43(4):591-597.
[8] ZIEGLER T,MOEN A,ZHANG W Q,et al.Global Influenza Surveillance and Response System:70 years of responding to the expected and preparing for the unexpected[J].Lancet,2022,400(10357):981-982.
[9] MACKENZIE J S,DRURY P,ARTHUR R R,et al.The global outbreak alert and response network[J].Glob Public Health,2014,9(9):1023-1039.
[10] HAY A J,MCCAULEY J W.The WHO global influenza surveillance and response system(GISRS):a future perspective[J].Influenza Other Respir Viruses,2018,12(5):551-557.
[11] Centers for Disease Control and Prevention.National Notifiable Diseases Surveillance System(NNDSS)history[EB/OL].[2026-03-28].https://www.cdc.gov/nndss/about/history.html.
[12] EJIGU G S,RADHAKRISHNAN K,MCMURRAY P,et al.Data quality improvements in national syndromic surveillance program(NSSP)data[J/OL].Online J Public Health Inform,2018,10(1)[2026-03-28].https://doi.org/10.5210/ojphi.v10i1.9122.
[13] AMMON A.Strukturen derüberwachung und des managements von infektionskrankheiten in der EU[J].Bundesgesundheitsbl Gesundheitsforsch Gesundheitsschutz,2005,48(9):1038-1042.
[14] ZARAKET H,SAITO R.Japanese surveillance systems and treatment for influenza[J].Curr Treat Options Infect Dis,2016,8(4):311-328.
[15] KRAMARZ P,LOPALCO P L,HUITRIC E,et al.Vaccine-preventable diseases:the role of the European centre for disease prevention and control[J].Clin Microbiol Infect,2014,20(Suppl. 5):2-6.
[16] 熊玮仪,冯子健.中国传染病监测的发展历程、现状与问题[J].中华流行病学杂志,2011,32(10):957-960.
[17] 杨维中,兰亚佳,李中杰,等.国家突发公共卫生事件自动预警与响应系统(中国)的应用[J].中华流行病学杂志,2010,31(11):1240-1244.
[18] YANG W Z,LI Z J,LAN Y J,et al.A nationwide web-based automated system for outbreak early detection and rapid response in China[J].Western Pac Surveill Response J,2011,2(1):10-15.
[19] 赵自雄,赵嘉,马家奇.我国传染病监测信息系统发展与整合建设构想[J].疾病监测,2018,33(5):423-427.
[20] 崔志刚,周海健,徐建国,等.传染病实验室监测实践——国家致病菌识别网[J].疾病监测,2022,37(12):1520-1523.
[21] 张婷瑜,刘艳慧,鲁影,等.传染病多点触发监测预警指标体系构建[J].中国公共卫生,2024,40(4):489-495.
[22] 杜明梅,刘运喜.我国传染病监测预警系统的发展与应用[J].中华医院感染学杂志,2022,32(6):801-804.
[23] PITOUT J D D.Transmission surveillance for antimicrobial-resistant organisms in the health system[J/OL].Microbiol Spectr,2018,6(5)[2026-03-28].https://doi.org/10.1128/microbiolspec.mtbp-0010-2016.
[24] 赵自雄,马家奇.数智化在传染病预防控制中的应用与进展[J].中国卫生信息管理杂志,2024,21(5):635-640.
[25] SUN H M,HU W H,WEI Y Y,et al.Drawing on the development experiences of infectious disease surveillance systems around the world[J].China CDC Weekly,2024,6(41):1065-1074.
[26] 国家疾病预防控制局.关于建立健全智慧化多点触发传染病监测预警体系的指导意见[J].中国病毒病杂志,2024,14(6):518-520.
[27] SHERMAN R E,ANDERSON S A,DAL PAN G J,et al.Real-world evidence:what is it and what can it tell us?[J].N Engl J Med,2016,375(23):2293-2297.
[28] GINSBERG J,MOHEBBI M H,PATEL R S,et al.Detecting influenza epidemics using search engine query data[J].Nature,2009,457(7232):1012-1014.
[29] 祝丙华,王立贵,孙岩松,等.基于大数据传染病监测预警研究进展[J].中国公共卫生,2016,32(9):1276-1279.
[30] DEMETRI G D,STACCHIOTTI S.Contributions of real-world evidence and real-world data to decision-making in the management of soft tissue sarcomas[J].Oncology,2021,99(Suppl.1):3-7.
[31] LYMAN G H,KUDERER N M.Randomized controlled trials versus real-world data in the COVID-19 era:a false narrative[J].Cancer Invest,2020,38(10):537-542.
[32] 王海星,杨志清,郭燕青,等.基于大数据的传染病监测预警方法及应用[J].预防医学论坛,2020,26(10):796-798.
[33] 鲁琴宝,吴昊澄,丁哲渊,等.浙江省传染病时空模型探测暴发疫情的效果评价[J].实用预防医学,2024,31(2):152-155.
[34] 谢聪,彭雨霜,黄增辉,等.基于湖北省统筹传染病监测预警平台的疫情信息报告质量与效率分析[J].疾病监测,2025,40(1):44-48.
[35] 赵晓雪,耿兴义,王硕,等.多点触发下的传染病监测预警系统设计与实现[J].电脑编程技巧与维护,2024(6):48-51.
[36] 林鸿波,沈鹏,孙烨祥,等.基于大数据建立传染病监测预警响应模式的探索与实践[J].中国卫生信息管理杂志,2020,17(4):416-421.
[37] LU Q B,FU T Y,WU H C,et al.Automatic warning practice of multi-source surveillance and multi-point trigger for infectious diseases-Yuhang District,Hangzhou City,Zhejiang Province,China,January-April 2024[J].China CDC Weekly,2025,7(4):152-156.
[38] 聂晓伟,潘小多,李新,等.面向科学数据全生命周期的动态安全评估机制[J].科学通报,2024,69(17):2360-2367.
[39] 蒋天宏,周正元,龚利强,等.基于大数据的传染病监测预警处置系统设计与分析[J].中国数字医学,2022,17(11):116-120.
[40] 付之鸥,鲍昌俊,李中杰,等.基于“大数据”的流感预警研究进展[J].中华流行病学杂志,2020,41(6):975-980.
[41] CUI J Z,ZHANG T,SHEN Y F,et al.Probability-based early warning for seasonal influenza in China:model development study[J/OL].JMIR Med Inform,2025,13[2026-03-28].https://doi.org/10.2196/73631.
[42] HAO R Z,LIU Y Q,SHEN W Z,et al.Surveillance of emerging infectious diseases for biosecurity[J].Sci China Life Sci,2022,65(8):1504-1516.
[43] 赵云霞,邵明义,陈晓琦,等.真实世界数据的真实性及其影响因素探讨[J].中医杂志,2021,62(4):303-306,311.
[44] 王金龙,陈涛,任翔,等.重要呼吸道传染病智慧化监测预警与有效应对策略探讨[J].病毒学报,2021,37(5):1175-1178.
[45] 张诚,夏天,毛丹,等.基于医疗健康大数据的重大传染病监测预警标准体系构建设想[J].预防医学情报杂志,2024,40(4):430-434.
[46] 赵坚,徐小卫,杨亚洲,等.基于大数据与5G技术的传染病智能监测预警处置系统设计与应用[J].医学信息学杂志,2023,44(5):14-19. |
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