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Article(id=1320438241187184976, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438168613138789, articleNumber=null, orderNo=null, doi=10.11847/zgggws1148699, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755532800000, receivedDateStr=2025-08-19, revisedDate=1757952000000, revisedDateStr=2025-09-16, acceptedDate=1772985600000, acceptedDateStr=2026-03-09, onlineDate=1792746609255, onlineDateStr=2026-10-23, pubDate=1778342400000, pubDateStr=2026-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792746609255, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792746609255, creator=13701087609, updateTime=1792746609255, updator=13701087609, issue=Issue{id=1320438168613138789, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='5', pageStart='513', pageEnd='640', issueExtLink='null', onlineDate='null', pubDate='1778342400000', pubDateStr='2026-05-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792746591952, creator='13701087609', updateTime=1792748055079, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444305479684446, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438168613138789, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444305479684447, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438168613138789, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=615, endPage=621, ext={EN=ArticleExt(id=1320438242843935058, articleId=1320438241187184976, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Application of R language in active detection of infectious disease clusters in schools within the Notifiable Disease Surveillance and Reporting System: a case study of Guangdong province, columnId=1320427686514770171, journalTitle=Chinese Journal of Public Health, columnName=Survey and Report, runingTitle=null, highlight=null, articleAbstract=
Objective

To develop an R program for processing complex work unit entries in the Notifiable Disease Surveillance and Reporting System, enabling active searching of school-based cluster epidemics.

Methods

Taking the data of acute hemorrhagic conjunctivitis, pertussis, and other infectious diarrhea diseases in Guangdong province, 2024 as an example, we designed an active searching algorithm in R 4.1.0. The program standardizes unstructured school names via text mining algorithms, counts cases by normalized school name, actively search for school-based cluster epidemics with the fixed threshold method, and compare them with the epidemics reported in the Public Health Emergency Management Information System (PHEMIS).

Results

When being aligned with PHEMIS reporting thresholds, the R program showed the positive predictive value of 100.00%. Among the reported school-based cluster epidemics, 67.65% (23/34) were detected by the R program. The R program identified 60.87% (14/23) cluster epidemics later than PHEMIS but achieved synchronized reporting of 39.13% (9/23) cluster epidemics. Moreover, the R program revealed 11 clusters meeting PHEMIS criteria but not reported. When the thresholds were lowered, more potential cluster epidemics were detected, and 2 signals were earlier than PHEMIS alerts.

Conclusions

This active searching framework can serve as a powerful supplement to PHEMIS, demonstrating operational value for early detection and containment of school-based cluster epidemics.

, authors=Yun HUANG, Jiya DAI, Runing GUO, Weiyi CHEN, Xiaoliang CHEN, Meiling YANG, Wenjia LIANG, Jianhua HUANG, Yan LI, authorsList=Yun HUANG, Jiya DAI, Runing GUO, Weiyi CHEN, Xiaoliang CHEN, Meiling YANG, Wenjia LIANG, Jianhua HUANG, Yan LI, authorCompany=null, correspAuthors=Jianhua HUANG, Yan LI, authorNote=null, correspAuthorsNote=
HUANG Jianhua, E-mail:
LI Yan, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Public Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320438244014145877, articleId=1320438241187184976, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=主动搜索网络直报系统学校传染病聚集性疫情的R语言应用：以广东省为例, columnId=1320427686770622717, journalTitle=中国公共卫生, columnName=调查报告与分析, runingTitle=null, highlight=null, articleAbstract=
目的

编写R语言程序处理传染病网络直报系统中填写格式复杂的“患者工作单位”信息，主动搜索学校聚集性疫情。

方法

以2024年广东省急性出血性结膜炎、百日咳及其他感染性腹泻病传染病报告卡数据为例，应用R语言4.1.0软件编写主动搜索程序，对“患者工作单位”予以文本分析并整理成规范化学校名称，按学校名称计数，以固定阈值法主动搜索学校聚集性疫情，并与突发公共卫生事件管理信息系统（以下简称“突发网”）报告的疫情进行对比。

结果

按照突发网报告标准设置搜索阈值时，主动搜索程序的阳性预测值达100.00%；突发网报告的学校聚集性疫情67.65%（23/34）可以被主动搜索程序识别；60.87%（14/23）的聚集性疫情识别时间晚于突发网报告时间，但是39.13%（9/23）的聚集性疫情识别时间已经与突发网报告时间同步；并且额外识别11起应报未报突发网的疫情。当降低搜索阈值时，主动搜索程序可以识别更多可能需要报告突发网的疫情，并且2起聚集性疫情的识别时间可以早于突发网报告时间。

结论

主动搜索程序可作为突发网的有力补充，为学校传染病疫情的早防早控提供技术支撑。

, authors=黄云, 代吉亚, 郭汝宁, 陈唯一, 陈小晾, 杨美玲, 梁文佳, 黄建华*, *, 李艳*, *, authorsList=黄云, 代吉亚, 郭汝宁, 陈唯一, 陈小晾, 杨美玲, 梁文佳, 黄建华, 李艳, authorCompany=null, correspAuthors=黄建华, 李艳, authorNote=

黄云（1992—），助理研究员，博士，研究方向：传染病报告管理

, correspAuthorsNote=
黄建华，E-mail：
李艳，E-mail：
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School-based cluster epidemics detected by the R program

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周起算日期周结束日期学校例数
2024-01-012024-01-074403 A实验学校20
2024-01-312024-02-064401 A幼儿58
2024-02-202024-02-264413 A学校33
2024-02-242024-03-014401 B中学23
........................
), ArticleFig(id=1320438258253808028, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438241187184976, language=CN, label=表1, caption=

输出的应该被报告突发网的学校聚集性疫情

, figureFileSmall=null, figureFileBig=null, tableContent=
周起算日期周结束日期学校例数
2024-01-012024-01-074403 A实验学校20
2024-01-312024-02-064401 A幼儿58
2024-02-202024-02-264413 A学校33
2024-02-242024-03-014401 B中学23
........................
), ArticleFig(id=1320438258346082717, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438241187184976, language=EN, label=Tab.2, caption=

Number of school-based cluster epidemics reported in PHEMIS and detected by the R program

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常见传染病突发网报告学校
疫情数/起		阈值/
例		搜索发现学校
疫情数/起		搜索发现且报告突发网
疫情数/起		搜索发现但未报告突发网
疫情数/起		已报告突发网但未搜索
到疫情数/起
急性出血性结膜炎 1201 10 0
102 11 0
百日咳 3 5 12 2 10 1
3193 2191 1
其他感染性腹泻病3020 2120110
10 9821 77 9
), ArticleFig(id=1320438258471911838, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438241187184976, language=CN, label=表2, caption=

突发网报告及主动搜索程序识别的学校常见传染病聚集性疫情起数

, figureFileSmall=null, figureFileBig=null, tableContent=
常见传染病突发网报告学校
疫情数/起		阈值/
例		搜索发现学校
疫情数/起		搜索发现且报告突发网
疫情数/起		搜索发现但未报告突发网
疫情数/起		已报告突发网但未搜索
到疫情数/起
急性出血性结膜炎 1201 10 0
102 11 0
百日咳 3 5 12 2 10 1
3193 2191 1
其他感染性腹泻病3020 2120110
10 9821 77 9
), ArticleFig(id=1320438258551603615, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438241187184976, language=EN, label=Tab.3, caption=

School-based cluster epidemics reported in PHEMIS and detected by the R program in Guangdong province, 2024

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常见传染病聚集性疫情涉及学校阈值/例直报系统首次发现时间突发网创建时间时间差/d
急性出血性结膜炎惠州市A小学202024-06-292024-06-281
百日咳深圳市A小学 52024-01-20——
珠海市A一小2024-03-30——
深圳市B小学2024-04-28——
深圳市C小学2024-04-17——
深圳市D中心小学2024-05-05——
广州市A幼儿园2024-05-212024-05-165
阳江市A学校2024-05-10——
珠海市B学校2024-05-15——
广州市B幼儿园2024-05-17——
深圳市E幼儿中心2024-06-092024-06-045
韶关市A幼儿园2024-06-11——
广州市C幼儿园—2024-06-13—
阳江市B学校2024-06-16——
其他感染性腹泻病深圳市A实验学校202024-01-072024-01-052
广州市A幼儿园2024-02-012024-02-010
惠州市A学校2024-02-262024-02-233
深圳市B幼儿园—2024-02-29—
广州市B中学2024-03-012024-02-291
深圳市C小学—2024-03-01—
广州C职业技术学校2024-03-172024-03-161
珠海市A学校—2024-04-12—
梅州市A中学2024-04-142024-04-140
惠州市B学校2024-05-142024-05-140
广州市D中学—2024-05-30—
肇庆市A幼儿园2024-06-292024-06-272
清远市A幼儿园2024-09-062024-09-060
江门市A小学—2024-09-12—
深圳市D学校2024-09-132024-09-121
茂名市A小学2024-09-152024-09-141
清远市B学校2024-09-212024-09-210
广州市E实验小学2024-10-122024-10-102
广州市F幼儿园—2024-10-15—
韶关市A中学2024-10-252024-10-250
惠州市C幼儿园—2024-10-31—
广州市G学校2024-11-032024-11-012
清远市C小学2024-11-122024-11-120
阳江市A小学2024-11-222024-11-211
广州市H学校2024-12-072024-12-061
东莞市A中学2024-12-07——
惠州市D小学—2024-12-19—
广州市I实验学校—2024-12-23—
江门市B小学—2024-12-26—
韶关市B中心小学2024-12-272024-12-270
韶关市C中学2024-12-272024-12-270
), ArticleFig(id=1320438258664849824, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438241187184976, language=CN, label=表3, caption=

2024年广东省突发网报告及直报系统搜索到的学校常见传染病聚集性疫情

, figureFileSmall=null, figureFileBig=null, tableContent=
常见传染病聚集性疫情涉及学校阈值/例直报系统首次发现时间突发网创建时间时间差/d
急性出血性结膜炎惠州市A小学202024-06-292024-06-281
百日咳深圳市A小学 52024-01-20——
珠海市A一小2024-03-30——
深圳市B小学2024-04-28——
深圳市C小学2024-04-17——
深圳市D中心小学2024-05-05——
广州市A幼儿园2024-05-212024-05-165
阳江市A学校2024-05-10——
珠海市B学校2024-05-15——
广州市B幼儿园2024-05-17——
深圳市E幼儿中心2024-06-092024-06-045
韶关市A幼儿园2024-06-11——
广州市C幼儿园—2024-06-13—
阳江市B学校2024-06-16——
其他感染性腹泻病深圳市A实验学校202024-01-072024-01-052
广州市A幼儿园2024-02-012024-02-010
惠州市A学校2024-02-262024-02-233
深圳市B幼儿园—2024-02-29—
广州市B中学2024-03-012024-02-291
深圳市C小学—2024-03-01—
广州C职业技术学校2024-03-172024-03-161
珠海市A学校—2024-04-12—
梅州市A中学2024-04-142024-04-140
惠州市B学校2024-05-142024-05-140
广州市D中学—2024-05-30—
肇庆市A幼儿园2024-06-292024-06-272
清远市A幼儿园2024-09-062024-09-060
江门市A小学—2024-09-12—
深圳市D学校2024-09-132024-09-121
茂名市A小学2024-09-152024-09-141
清远市B学校2024-09-212024-09-210
广州市E实验小学2024-10-122024-10-102
广州市F幼儿园—2024-10-15—
韶关市A中学2024-10-252024-10-250
惠州市C幼儿园—2024-10-31—
广州市G学校2024-11-032024-11-012
清远市C小学2024-11-122024-11-120
阳江市A小学2024-11-222024-11-211
广州市H学校2024-12-072024-12-061
东莞市A中学2024-12-07——
惠州市D小学—2024-12-19—
广州市I实验学校—2024-12-23—
江门市B小学—2024-12-26—
韶关市B中心小学2024-12-272024-12-270
韶关市C中学2024-12-272024-12-270
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主动搜索网络直报系统学校传染病聚集性疫情的R语言应用：以广东省为例
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黄云 , 代吉亚 , 郭汝宁 , 陈唯一 , 陈小晾 , 杨美玲 , 梁文佳 , 黄建华 *, * , 李艳 *, *
中国公共卫生 | 调查报告与分析 2026,42(5): 615-621
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中国公共卫生 |调查报告与分析 2026 , 42 (5) : 615 -621
主动搜索网络直报系统学校传染病聚集性疫情的R语言应用：以广东省为例
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黄云, 代吉亚, 郭汝宁, 陈唯一, 陈小晾, 杨美玲, 梁文佳, 黄建华*, * , 李艳*, *
作者信息
  • 广东省疾病预防控制中心，广州 511430
通讯作者:
黄建华，E-mail：
李艳，E-mail：
作者简介:

黄云（1992—），助理研究员，博士，研究方向：传染病报告管理

Application of R language in active detection of infectious disease clusters in schools within the Notifiable Disease Surveillance and Reporting System: a case study of Guangdong province
Yun HUANG, Jiya DAI, Runing GUO, Weiyi CHEN, Xiaoliang CHEN, Meiling YANG, Wenjia LIANG, Jianhua HUANG , Yan LI
Affiliations
  • Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
    • Corresponding Author:
    HUANG Jianhua, E-mail:
    LI Yan, E-mail:
出版时间: 2026-05-10 doi: 10.11847/zgggws1148699
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摘要
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目的

编写R语言程序处理传染病网络直报系统中填写格式复杂的“患者工作单位”信息，主动搜索学校聚集性疫情。

方法

以2024年广东省急性出血性结膜炎、百日咳及其他感染性腹泻病传染病报告卡数据为例，应用R语言4.1.0软件编写主动搜索程序，对“患者工作单位”予以文本分析并整理成规范化学校名称，按学校名称计数，以固定阈值法主动搜索学校聚集性疫情，并与突发公共卫生事件管理信息系统（以下简称“突发网”）报告的疫情进行对比。

结果

按照突发网报告标准设置搜索阈值时，主动搜索程序的阳性预测值达100.00%；突发网报告的学校聚集性疫情67.65%（23/34）可以被主动搜索程序识别；60.87%（14/23）的聚集性疫情识别时间晚于突发网报告时间，但是39.13%（9/23）的聚集性疫情识别时间已经与突发网报告时间同步；并且额外识别11起应报未报突发网的疫情。当降低搜索阈值时，主动搜索程序可以识别更多可能需要报告突发网的疫情，并且2起聚集性疫情的识别时间可以早于突发网报告时间。

结论

主动搜索程序可作为突发网的有力补充，为学校传染病疫情的早防早控提供技术支撑。

传染病网络直报系统  /  学校  /  聚集性疫情  /  R语言
Objective

To develop an R program for processing complex work unit entries in the Notifiable Disease Surveillance and Reporting System, enabling active searching of school-based cluster epidemics.

Methods

Taking the data of acute hemorrhagic conjunctivitis, pertussis, and other infectious diarrhea diseases in Guangdong province, 2024 as an example, we designed an active searching algorithm in R 4.1.0. The program standardizes unstructured school names via text mining algorithms, counts cases by normalized school name, actively search for school-based cluster epidemics with the fixed threshold method, and compare them with the epidemics reported in the Public Health Emergency Management Information System (PHEMIS).

Results

When being aligned with PHEMIS reporting thresholds, the R program showed the positive predictive value of 100.00%. Among the reported school-based cluster epidemics, 67.65% (23/34) were detected by the R program. The R program identified 60.87% (14/23) cluster epidemics later than PHEMIS but achieved synchronized reporting of 39.13% (9/23) cluster epidemics. Moreover, the R program revealed 11 clusters meeting PHEMIS criteria but not reported. When the thresholds were lowered, more potential cluster epidemics were detected, and 2 signals were earlier than PHEMIS alerts.

Conclusions

This active searching framework can serve as a powerful supplement to PHEMIS, demonstrating operational value for early detection and containment of school-based cluster epidemics.

Notifiable Disease Surveillance and Reporting System  /  school  /  cluster epidemics  /  R language
黄云, 代吉亚, 郭汝宁, 陈唯一, 陈小晾, 杨美玲, 梁文佳, 黄建华, 李艳. 主动搜索网络直报系统学校传染病聚集性疫情的R语言应用：以广东省为例. 中国公共卫生, 2026 , 42 (5) : 615 -621 . DOI: 10.11847/zgggws1148699
Yun HUANG, Jiya DAI, Runing GUO, Weiyi CHEN, Xiaoliang CHEN, Meiling YANG, Wenjia LIANG, Jianhua HUANG, Yan LI. Application of R language in active detection of infectious disease clusters in schools within the Notifiable Disease Surveillance and Reporting System: a case study of Guangdong province[J]. Chinese Journal of Public Health, 2026 , 42 (5) : 615 -621 . DOI: 10.11847/zgggws1148699
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中国疾病预防控制信息系统包含多个子系统，其中，监测报告管理系统可用于报告、订正、查询法定传染病及部分非法定传染病的病例信息，又常被称为传染病网络直报系统；突发公共卫生事件管理信息系统（以下简称“突发网”）可用于报告、查询可能构成或已发生的突发公共卫生事件相关信息，包括传染病聚集性疫情、食物中毒事件等。突发网的信息主要来源于疾控机构识别或者医疗卫生机构、学校、托幼机构等集体单位的主动报告[1–2]，后者属于被动监测，这一运作模式可能导致一些达到报告标准的突发公共卫生事件相关信息被漏报或存在报告滞后性，从而导致某些疫情未及时得到控制而蔓延。有研究指出，突发网报告的传染病类疫情中约60%发生在学校[3]。报告传染病时“患者工作单位”非必填，但学生群体是需要重点关注的脆弱群体，传染病信息报告管理规范中要求“患者为学生或幼托儿童须填报其所在学校/幼托机构全称及班级名称”[4]，这使得基于网络直报系统“患者工作单位”信息主动搜索学校聚集性疫情成为可行的途径。然而，“患者工作单位”以任意文本方式填写，中文、数字、符号等各种字符形式混杂、很难统计，致使学校聚集性病例的筛查分析变得较为复杂。且较多疾控机构尚未利用网络直报系统早期发现传染病暴发[1–2,5]，中国疾病预防控制中心设计的传染病自动预警系统以县（区）或街道（镇）为单位探测空间聚集性疫情[6–7]，尚未涉及集体单位聚集性疫情的探测。有研究者曾整理网络直报系统中“患者工作单位”信息，但需要人工核实和确认数据[8]，占用大量人力，尤其是在某种传染病流行季时难以实行。另有研究者通过建立省级大数据计算中心，采用机器学习重新组织历史传染病报告卡中的工作单位信息生成学校信息基础数据库，再采用机器学习将新的传染病报告卡中的工作单位信息与基础数据库对比，从而生成规范的工作单位信息[9]。这种方式需要大量算力以更新维护基础数据库，难以推广应用。本研究采用开源统计编程R语言软件，编写程序对任意时间段内的传染病报告卡数据实现逐周滚动分析，对“患者工作单位”进行文本分析并整理成规范的学校名称，主动搜索超过周病例数阈值的学校聚集性疫情，为学校传染病疫情的早防早控提供技术支撑。
1　资料与方法
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1.1　资料来源
在中国疾病预防控制信息系统的监测报告管理系统中，按照现住址和审核日期下载2024年广东省学校常见传染病报告卡数据。本研究以急性出血性结膜炎、百日咳和其他感染性腹泻病作为分析实例。
1.2　文本分析方法（图1）
定位“患者工作单位”字符串中幼儿园、小学、中学、大学等表示学校的词语的起始位置，分4种常见的学校命名情形，再分别提取起始位置前后不同个数的字符作为学校名称。因本研究以广东省的数据作为分析实例，为避免不同地市存在同一名称的学校，提取现住地址国标前4位，并结合第二步提取的学校名称作为处理后的最终学校名称（如，4401**小学）。
1.3　统计分析
应用R语言4.1.0软件进行统计学分析。调用主动搜索学校聚集性疫情的程序detect，输入某个时期某病种传染病报告卡数据、周病例数阈值2个参数值，其中传染病报告卡数据仅需要包含患者工作单位、现住地址国标、县区审核时间、地市审核时间和省市审核时间5列，周病例数阈值按照《广东省卫生健康委办公室关于进一步做好突发公共卫生事件相关信息报告工作的通知》（依申请公开文件）中学校应报告的各病种聚集性疫情标准，急性出血性结膜炎、百日咳和其他感染性腹泻病的突发网报告标准分别为1周内同一学校发现≥20、≥5、≥20例病例，此外在此基础上适当调低阈值（≥10、≥3、≥10例病例）以探究主动搜索程序的灵敏度变化，然后将每周的传染病报告卡数据按照处理后的最终学校名称计数，频数超过周病例数阈值的传染病报告卡数据导出待核实，并输出应该报告或可能需要报告突发网的学校聚集性疫情。最后，将主动搜索程序识别的学校聚集性疫情与突发网报告的疫情进行对比，探究主动搜索程序是否可以早于突发网进行预警，以及能否识别应报未报突发网的疫情。
R语言程序如下：
library(stringr)
library(lubridate)
#编写主动搜索学校聚集性疫情的函数
detect<-function(data,threshold){
data$县区审核时间<-gsub("[^0-9/:]", "", data$县区审核时间)
data$县区审核时间<-parse_date_time(data$县区审核时间, orders=c("Y/m/d H:M","Y/m/d H:M:S","Y-m-d H:M","Y-m-d H:M:S"))
data$地市审核时间<-gsub("[^0-9/:]", "", data$地市审核时间)
data$地市审核时间<-parse_date_time(data$地市审核时间, orders=c("Y/m/d H:M","Y/m/d H:M:S","Y-m-d H:M","Y-m-d H:M:S"))
for(i in 1:nrow(data)){
if(is.na(data$县区审核时间[i])){
data$县区审核时间[i]<-data$地市审核时间[i]
}else{next}}
data$县区审核时间<-as.Date(data$县区审核时间)
days_diff<-difftime(max(data$县区审核时间),min(data$县区审核时间), units="days")
SBR<-data.frame()
for(j in 0:(days_diff-6)){
day_start<-min(data$县区审核时间)+j
day_end<-day_start+6
oneweekdata<-data[which(data$县区审核时间>=day_start & data$县区审核时间<day_end+1),]
oneweekdata$school<-NA
for(i in 1:nrow(oneweekdata)){
tem1<-as.data.frame(str_locate(oneweekdata$患者工作单位[i],c("托儿所","幼儿园","幼儿中心","小学","附小","一小","二小","三小","四小","五小","六小","七小","八小","九小","中学","附中","一中","二中","三中","四中","五中","六中","七中","八中","九中","高中","学校","大学")))
if(all(is.na(tem1$start))){next}else{
tem2<-tem1$start[!is.na(tem1$start)][1]
if(tem2-2<0){next}else{
tem3<-substring(oneweekdata$患者工作单位[i],tem2-2,tem2-1)
if(tem3 %in% c("实验","试验","中心","机关","初级","高级","职业","技工","国际","花园","小学","中学","大学","第一","第二","第三","第四","第五","第六","第七","第八","第九")){
if(tem2-4<0){next}else{
tem_p<-substring(oneweekdata$患者工作单位[i],tem2-4,tem2-3)
if(tem_p %in% c("实验","试验","中心","机关","初级","高级","职业","技工","国际","小学","中学","大学","第一","第二","第三","第四","第五","第六","第七","第八","第九")){
if(tem2-6<0){next}else{tem3<-substring(oneweekdata$患者工作单位[i],tem2-6,tem2+1)}}else
{tem3<-substring(oneweekdata$患者工作单位[i],tem2-4,tem2+1)}
}}else if(tem3 %in% c("中文","英文")){
if(tem2-5<0){next}else{
tem_p<-substring(oneweekdata$患者工作单位[i],tem2-5,tem2-4)
if(tem_p %in% c("第二","第三","附属")){
if(tem2-7<0){next}else{tem3<-substring(oneweekdata$患者工作单位[i],tem2-7,tem2+1)}
}else{tem3<-substring(oneweekdata$患者工作单位[i],tem2-5,tem2+1)}
}}else if(tem3 %in% c("附属","技术")){
if(tem2-6<0){next}else{
tem3<-substring(oneweekdata$患者工作单位[i],tem2-6,tem2+1)}
}else{tem3<-substring(oneweekdata$患者工作单位[i],tem2-2,tem2+1)}}}
tem4<-paste0(substring(oneweekdata$现住地址国标[i],1,4),tem3)
oneweekdata$school[i]<-tem4}
tem5<-as.data.frame(table(oneweekdata$school))
colnames(tem5)<-c("term","freq")
tem5_sorted<-tem5[order(tem5$freq,decreasing= TRUE),]
tem5_sorted<-tem5_sorted[tem5_sorted$freq>=threshold,]
if(nrow(tem5_sorted)==0){next}else{
tem6<-data.frame(day_start=day_start,day_end=day_end,institution=tem5_sorted$term,freq=tem5_sorted$freq)
rownames(tem6)<-NULL
SBR<-rbind(SBR,tem6)
SBC<-data.frame()
for(k in 1:nrow(tem5_sorted)){
tem7<-oneweekdata[which(oneweekdata$school==tem5_sorted$term[k]),]
rownames(tem7)<-NULL
SBC<-rbind(SBC,tem7)}
#输出应该被核实是否为学校聚集性疫情的传染病报告卡数据
write.csv(SBC,paste0("Should_be_Checked_",day_start,"_",format(day_end, "%m"),"-",format(day_end, "%d"),".csv"),row.names= F)}}
#输出应该被报告突发网的学校聚集性疫情
write.csv(SBR,"Should_be_Reported.csv",row.names = F)}
#调用函数，输入某个时期某病种传染病报告卡数据、周病例数阈值，下面以同一学校一周内报告某病种20例及以上为例：
detect(data=data,threshold=20)
2　结　果
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2.1　输出的应该被报告突发网的学校聚集性疫情格式（表1）
输出的应该被报告或者可能需要报告突发网的学校聚集性疫情被放在一个名为Should_be_Reported.csv的excel文件中，共包含4列，分别为周起算日期、周结束日期、聚集性疫情涉及的学校名称和该周该学校涉及的某病种病例数。
2.2　突发网报告与网络直报系统搜索到的学校聚集性疫情对比
2.2.1　急性出血性结膜炎
2024年广东省在突发网报告1起发生在学校的急性出血性结膜炎聚集性疫情，当周病例数阈值设置为20时，主动搜索程序识别到同1起疫情（表2，灵敏度100.00%），但识别时间晚于突发网报告时间1 d（表3）；当周病例数阈值调低为10时，主动搜索程序额外识别出1起可能需要报告突发网的急性出血性结膜炎疫情。
2.2.2　百日咳
2024年广东省在突发网报告3起发生在学校的百日咳聚集性疫情，当周病例数阈值设置为5时，其中2起疫情被主动搜索程序识别（灵敏度66.67%），但识别时间均晚了5 d，另1起未能被搜索到；此外，主动搜索程序额外识别出10起达到突发网报告标准但未被报告的疫情。当周病例数阈值调低为3时，主动搜索程序除1起未能识别，额外识别出191起可能需要报告突发网的百日咳疫情，且广州市A幼儿园百日咳疫情的识别时间提前到2024年5月9日，早于突发网报告时间（5月16日）7 d。
2.2.3　其他感染性腹泻病
2024年广东省在突发网报告30起发生在学校的其他感染性腹泻病聚集性疫情，当周病例数阈值设置为20时，其中20起疫情被主动搜索程序识别（灵敏度66.67%），有9起识别时间与突发网报告时间同一天，另10起未能被搜索到，此外，主动搜索程序额外识别出1起达到突发网报告标准但未被报告的疫情；当周病例数阈值调低为10时，主动搜索程序除9起未能搜索到，额外识别出77起可能需要报告突发网的其他感染性腹泻病疫情，且广州C职业技术学校其他感染性腹泻病疫情的识别时间提前到2024年3月14日，早于突发网报告时间（3月16日）2 d。
综上，按照突发网报告标准设置搜索阈值时，突发网报告的34起疫情（1起急性出血性结膜炎、3起百日咳和30起其他感染性腹泻病）中有23起（1起急性出血性结膜炎、2起百日咳和20起其他感染性腹泻病）被主动搜索程序识别，总灵敏度67.65%（23/34）。针对23起搜索发现且报告突发网的疫情，9起识别时间与突发网报告时间同步（占39.13%，9/23），14起识别时间晚于突发网报告时间（占60.87%，14/23），主动搜索程序中位滞后时间为1 d。11起搜索发现但未报告突发网疫情（10起百日咳和1起其他感染性腹泻病），11起已报告突发网但未被搜索到疫情（1起百日咳和10起其他感染性腹泻病）。另外，主动搜索识别的1起急性出血性结膜炎、12起百日咳和21起其他感染性腹泻病学校聚集性疫情，经核实原始报告卡的患者工作单位信息，确实为同一学校聚集性疫情，即阳性预测值达100.00%。
3　讨　论
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本研究结果显示，按照突发网报告标准设置搜索阈值时，主动搜索程序的阳性预测值达100.00%，显著高于将相似度达90.00%的学校名称归为一类的机器学习方法，其阳性预测值仅为30.56%[9]，略高于佛山市南海区传染病聚集性事件预警监测系统的96.63%[10]。突发网报告的学校聚集性疫情67.65%可以被主动搜索程序识别，略低于佛山市南海区传染病聚集性事件预警监测系统的80.18%，但是该系统对其他感染性腹泻病、流感等病种灵敏度极低甚至不响应[10]。60.87%聚集性疫情的识别时间晚于突发网报告时间，39.13%聚集性疫情的识别时间与突发网报告时间同步，表明主动搜索程序按照突发网报告标准设置搜索阈值时并不能实现提早预警。此外，主动搜索程序可以额外识别11起应报未报突发网的疫情，表明其可以作为突发网的重要补充。当降低搜索阈值时，主动搜索程序可识别更多可能需要报告突发网的疫情，并且2起聚集性疫情的识别时间早于突发网报告时间。使用主动搜索程序时，鉴于部分病例不会选择就医即也不会有传染病报告卡的现实情况，可以通过适当调低搜索阈值来提高灵敏度与及时性，在周病例数达到自定义阈值时便提醒相关学校做好病例监测、搜索和干预等工作，从而达到控制疫情规模的目的。
本研究也发现11起已报告突发网但未被搜索到的疫情，原因是基于传染病网络直报系统主动搜索学校聚集性疫情的方式一方面受病例就医与否的影响，另一方面受医生诊断、报卡以及患者工作单位误填与否的影响，不可避免地会出现聚集性疫情识别不全的问题。医疗卫生机构、学校、幼托机构等集体单位在日常传染病监测过程中发现未达到突发网报告标准的小规模聚集性病例时，有时存在主动搜索病例的行为，进而会发现部分有症状但未上报或者未就医的病例，从而能够更多、更早地发现需要上报的聚集性疫情，因而集体单位上报的被动监测方式仍然很重要。此外，主动搜索程序的局限性在于，“**大学附属实验中学”与“**大学附中”这类缩写与全称并存的情况，或者“**市第一实验小学”与“**市一小实验部”等更灵活的命名，会被程序识别为不同学校，从而遗漏一些聚集性疫情信号。如前所述，机器学习方法将高相似度的学校名称归为一类却大大降低预警的阳性预测值[9]，而阳性预测值低会导致后续信息核实和分析研判的工作量增大，相关人员对预警的接受度降低[11]。
综上所述，主动搜索程序可作为突发网的有力补充，该R程序可以本地部署来实现实时监测，为手动搜索网络直报系统聚集性疫情的疾控同行大大提高工作效率，适宜推广给各级疾控机构使用。鉴于学生跨区上学现象十分普遍，而传染病报告卡以县级行政区域进行属地化管理，因而主动搜索程序更适合地市级以上疾控机构使用，县区疾控机构使用可能会遗漏一些聚集性疫情。当适当降低搜索阈值时，主动搜索程序可以早于突发网发现疫情，从而实现学校传染病疫情的早防早控。

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2026年第42卷第5期
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doi: 10.11847/zgggws1148699
  • 接收时间：2025-08-19
  • 首发时间：2026-10-23
  • 出版时间：2026-05-10
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  • 收稿日期：2025-08-19
  • 修回日期：2025-09-16
  • 录用日期：2026-03-09
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    广东省疾病预防控制中心，广州 511430

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黄建华，E-mail：
李艳，E-mail：
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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