2020—2021年重庆市南岸区中小学生因病缺勤监测预警模型研究

Research on early warning model of primary and secondary school students’ absenteeism due to illness in Nan’an District of Chongqing City from 2020 to 2021

目的 了解重庆市南岸区中小学生晨午检异常症状及因病缺勤情况的分布特征,建立预警模型,降低学校传染病疫情发生风险。方法 对“重庆市南岸区学校传染病症状监测系统”中2020年4月27日—2021年4月30日学生因病缺勤及症状监测资料进行描述性分析,并利用R软件对因病缺勤日报监测数据进行SARIMA模型拟合。结果 2020年4月27日—2021年4月30日共监测学生141 190人,因病缺勤总上报数88 374人次,总因病缺勤率为0.305%;小学、初中、高中、职高学生因病缺勤率分别为 0.405%、0.191%、0.127%、0.283%,差异有统计学意义(χ^(2)=12 370.737,P<0.001);以发热、咳嗽、咽痛为主的呼吸道相关症状(30.700%)是引起学生因病缺勤的主要症状,其次是消化道相关症状(8.846%);统合考虑AIC值、RMES以及MAPE 值,得出ARIMA(2,1,1)×(1,1,1)为最佳预测模型。结论 学校传染病症状监测系统在一定程度上可以通过监测数据反映传染病相关症状在学校的流行状况,SARIMA预测模型可用于监测预警,为学校传染病的防控提供科学依据。

Objective To understand the distribution characteristics of abnormal symptoms of morning and afternoon check-ups and absenteeism due to illness among primary and secondary school students in Nan’an District, Chongqing City, and establish an early warning model to reduce the risk of infectious disease outbreaks in schools.Methods The data of students’ absence due to illness and symptom monitoring from April 27, 2020 to April 30, 2021 in the "School infectious disease symptom monitoring system in Nan’an District, Chongqing" were collected and analyzed descriptively, and the monitoring data of absence due to illness were fitted by SARIMA model by using R software.Results From April 27, 2020, to April 30, 2021, a total of 141 190 students were monitored, the total number of reported absences due to illness was 88 374,and the overall illness-induced absenteeism rate was 0.305%. The illness-induced absence rates of students in different school age groups were 0.405% in primary schools, 0.191% in junior high schools, 0.127% in senior high schools, and 0.283% in vocational high schools, respectively, and the difference was statistically significant(χ^(2)= 12370.737, P < 0.001).The respiratory symptoms related to fever, cough, and sore throat were the most frequent symptoms that caused absence(30.700%), followed by gastrointestinal symptoms(8.846%). ARIMA(2, 1, 1) ×(1, 1, 1)was selected as the optimal prediction model considering the AIC value, RMES and MAPE value.Conclusions To a certain extent, the school infectious disease symptom monitoring system can reflect the prevalence of infectious disease-related symptoms in schools. The SARIMA forecasting model can be used for monitoring and early warning and provide a scientific basis for the prevention and control of school infectious diseases.