Comprehensive evaluation of the epidemic warning and monitoring system for infectious disease aggregation in schools or daycare institutions with multi source data
10.16835/j.cnki.1000-9817.2023.11.026
- VernacularTitle:多源数据的学校和托幼机构传染病聚集性疫情预警监测系统综合评价
- Author:
LIANG Jieya, QIN Chuoheng, ZHOU Mengxi
1
Author Information
1. Emergency Management Department, Nanhai District Centre for Disease Control and Prevention, Foshan (528200) , Guangdong Province, China
- Publication Type:Journal Article
- Keywords:
Child day care centers；Disease outbreaks；Communicable diseases；Population surveillance
- From:
Chinese Journal of School Health
2023;44(11):1713-1715
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To comprehensively evaluate the early warning monitoring system (WMS) for infectious disease aggregation in schools or daycare institutions with multisource data, to identify the advantages and disadvantages of the system, and to provide a basis for optimizing its warning function and exploring further integration of other data sources.
Methods:The infectious disease warning data from the Chinese infectious disease Automated alert and Response System(ARS), the Student Health Monitoring System (SHMS) in Foshan City, Guangdong Province, and WMS were collected from January 2021 to July 2023. The indicators such as sensitivity, specificity, Youden index, positive predictive value, early warning and median timeliness were used to comprehensively evaluate the early warning monitoring system.
Results:The ARS was not sensitive to common infectious disease warnings in schools or daycare institutions. The median timeliness of the SHMS and the WMS was 1 day. The sensitivity of SHMS and the WMS for early warning of hand foot mouth disease, infectious diarrhea, influenza like cases, chickenpox and other infectious diseases were more than 70%, while the sensitivity for novel coronavirus infection were only 10.42% and 64.58% . The Youden index and positive predictors of the WMS were both the highest.
Conclusion:The WMS can timely and effectively warn schools or daycare institutions of clustered epidemics, improve the positive predictive value, but the data sources are still insufficient, and it is necessary to continuously increase the data sources in future exploration to improve the warning ability.
