DOI: 10.57760/sciencedb.o00130.04900
title: Analysis of Early Warning Technology for School Infectious Disease Outbreaks Based on Student Medical Consultations and Absence Monitoring, along with Its Effectiveness
title: 基于学生就诊和缺勤监测的学校传染病疫情预警技术及其效能分析

description: Objective Analyze the correlation between student infectious disease consultations and absenteeism monitoring data, and evaluate the capability of different monitoring data in epidemic early warning.Methods Thirteen schools were selected as monitoring points to collect data on infectious disease consultations, absenteeism due to illness, and clustered outbreaks. Spearman's rank correlation analysis was used to compare the correlation between time series data, and the moving percentile method was employed to set early warning thresholds. The early warning effectiveness of each data source was evaluated through indicators such as positive predictive value and sensitivity.Results From 2015 to 2019, a total of 2,565 student cases were reported, primarily involving influenza, varicella, and mumps; 23,407 cases of absenteeism due to illness were reported, mainly due to fever, common cold, and cough; and 90 clustered outbreaks in schools were reported, mainly involving influenza, herpes pharyngitis, and hand-foot-and-mouth disease. There was a lagged correlation between infectious disease consultation and absenteeism monitoring data (r=0.31, P&lt;0.05), with the latter being able to detect outbreaks earlier. The infectious disease consultation monitoring triggered 385 early warnings, absenteeism monitoring triggered 491, series connection triggered 298, and parallel connection triggered 876. The infectious disease consultation monitoring for students alone had the highest positive predictive value (66.49%), the series connection had the highest specificity (98.42%), and the parallel connection had the highest sensitivity and Youden index (100.00% and 80.52%).Conclusion The monitoring of infectious disease consultations among students lags behind the monitoring of absenteeism due to illness. However, the parallel early warning technology combining both can accurately and rapidly detect outbreaks, and timely intervention can prevent the epidemic from spreading.
description: Objective Analyze the correlation between student infectious disease consultations and absenteeism monitoring data, and evaluate the capability of different monitoring data in epidemic early warning.Methods Thirteen schools were selected as monitoring points to collect data on infectious disease consultations, absenteeism due to illness, and clustered outbreaks. Spearman's rank correlation analysis was used to compare the correlation between time series data, and the moving percentile method was employed to set early warning thresholds. The early warning effectiveness of each data source was evaluated through indicators such as positive predictive value and sensitivity.Results From 2015 to 2019, a total of 2,565 student cases were reported, primarily involving influenza, varicella, and mumps; 23,407 cases of absenteeism due to illness were reported, mainly due to fever, common cold, and cough; and 90 clustered outbreaks in schools were reported, mainly involving influenza, herpes pharyngitis, and hand-foot-and-mouth disease. There was a lagged correlation between infectious disease consultation and absenteeism monitoring data (r=0.31, P&lt;0.05), with the latter being able to detect outbreaks earlier. The infectious disease consultation monitoring triggered 385 early warnings, absenteeism monitoring triggered 491, series connection triggered 298, and parallel connection triggered 876. The infectious disease consultation monitoring for students alone had the highest positive predictive value (66.49%), the series connection had the highest specificity (98.42%), and the parallel connection had the highest sensitivity and Youden index (100.00% and 80.52%).Conclusion The monitoring of infectious disease consultations among students lags behind the monitoring of absenteeism due to illness. However, the parallel early warning technology combining both can accurately and rapidly detect outbreaks, and timely intervention can prevent the epidemic from spreading.