Predictive study on school absences due to illness with seasonal exponential smoothing method
10.16462/j.cnki.zhjbkz.2019.07.020
- Author:
Rong-yan GU
1
;
Ling ZHANG
;
Xiao-xiao SONG
;
Yan LI
;
Le CAI
;
Wen-long CUI
;
Wei LIU
Author Information
1. Department of Institute of health, School of public Health, Kunming Medical University, Kunming 650500, China
- Publication Type:Research Article
- Keywords:
Exponential smoothing method;
Time series;
School absences due to illness;
Prediction
- From:
Chinese Journal of Disease Control & Prevention
2019;23(7):845-849，855
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a suitable exponential smoothing prediction model for school absentees due to illness, to discuss its application value for predicting school absences due to illness, and to provide a basis for early warning of absence due to illness. Methods Numbers of schools absences by year and month due to illness in 30 primary schools from November 2015 to June 2017 were collected from symptom monitoring system of border county, southern Yunnan and Simple seasonal model, Winters addition model and Winters multiplication model were used to build simulation. The data of July 2017 to December 2017 were used for model validation. The three models were overall compared and evaluated through indicator analysis, statistical analysis and residual diagram analysis. The best model was selected to predict school absences due to illness from January 2018 to March 2018. Results Simple seasonal model, Winters addition model and Winters multiplication model were used to fit the variation trend of number of school absences due to illness in time series. The root mean square error (RMSE) of three models were 445.11, 420.99 and 258.75; R2adj were 0.72, 0.72 and 0.77; R2 were 0.92, 0.93 and 0.98; P values of Ljung-Box Q were 0.54, 0.43 and 0.21. As for prediction method linear trend, Alpha were 0.999, 1.000 and 0.298. The average relative error between predicted value and actual value was 9.62%, 21.90% and 7.52%. Conclusion Winters multiplication model has practical value to predict school absence due to illness and provide scientific basis for early identification of abnormal signals.
