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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
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Yun HUANG, Jiya DAI, Runing GUO, Weiyi CHEN, Xiaoliang CHEN, Meiling YANG, Wenjia LIANG, Jianhua HUANG, Yan LI
Chinese Journal of Public Health | 2026, 42(5) : 615 - 621
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Chinese Journal of Public Health | 2026, 42(5): 615-621
• Survey and Report •
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
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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
Published: 2026-05-10 doi: 10.11847/zgggws1148699
Outline
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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
Year 2026 volume 42 Issue 5
90
27
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Article Info
doi: 10.11847/zgggws1148699
  • Receive Date:2025-08-19
  • Online Date:2026-10-23
  • Published:2026-05-10
Article Data
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History
  • Received:2025-08-19
  • Revised:2025-09-16
  • Accepted:2026-03-09
Affiliations
    Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China

Corresponding:

HUANG Jianhua, E-mail:
LI Yan, E-mail:
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表12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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