Analysis and forecasting of the epidemiological characteristics of mumps in Zaozhuang City between 2010 and 2017
10.16462/j.cnki.zhjbkz.2019.02.012
- Author:
Xi-wang SUN
1
;
Juan-juan YU
;
Hong ZHANG
;
Xiao HU
;
Fu-guo LI
Author Information
1. Department of Immunization program, Zaozhuang Center for Disease Control and Prevention Zaozhuang 277101, China
- Publication Type:Research Article
- Keywords:
Mumps;
Epidemiological characteristics;
Analysis of time series;
Prediction
- From:
Chinese Journal of Disease Control & Prevention
2019;23(2):180-184
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the epidemiological characteristics of mumps in Zaozhuang city from 2010 to 2017, and establish a predictive model to predict prevalence trend, so as to provide scientific basis for the prevention and control of mumps. Methods Mumps cases between 2010 and 2017 screened from Infectious Diseases Report Information Management System of Chinese Information System of Disease Prevention and Control were analyzed by the descriptive epidemiologic method. The time series analysis method was applied to model and predict the trend of disease. Results A total of 6 353 mumps cases were reported in Zaozhuang city from 2010 to 2017. The average annual incidence was 19.75/100 000. The incidence of men was higher than that of women. The seasonal indices for December to next January and April to July were all above 1. The incidence rates of Shizhong and Yicheng were higher than in other areas. The incidences aged from 3 to 14 accounted for 76.85 % of the total number of cases, while students, scattered children and childcare accounted for 86.11%. The established time series model was ARIMA (1, 0, 0) * (0, 0, 0)12. The mean absolute percentage error of the predictive values based on the model was 39.99% and the number of cases predicted by the model in 2018 was 471. The predictive incidence rate was 11.13/100 000. Conclusions Primary and secondary school students and scattered children are the main population for the prevention and control of mumps. The model obtains better forecasting results and can be used for short-term prediction and dynamic analysis of mumps.
