2010-2017年枣庄市流行性腮腺炎流行病学特征分析和模型预测

Analysis and forecasting of the epidemiological characteristics of mumps in Zaozhuang City between 2010 and 2017

目的分析2010-2017年枣庄市流行性腮腺炎流行病学特征,建立模型预测疾病发病趋势,为流行性腮腺炎的科学防控提供参考依据。方法运用描述流行病学方法对从《中国疾病预防控制信息系统的传染病报告信息管理系统》中筛选出来的2010-2017年流行性腮腺炎疫情资料进行分析,应用时间序列分析方法科学建模并预测疾病发病趋势。结果 2010-2017年枣庄市累计报告流行性腮腺炎病例6 353例,年平均报告发病率为19. 75/10万,发病率男性高于女性,4～7月、12～次年1月季节指数均大于1,峄城区和市中区发病率较高,3～14岁发病例数占发病总数的76. 85%,学生、散居儿童、托幼儿童占发病总数的86. 11%;建立时间序列模型为:求和自回归移动平均(autoregressive integrated moving average,ARIMA)模型,即ARIMA(1,0,0)×(0,0,0)12,模型预测平均绝对百分误差为39. 99%,模型预测2018年发病数为471例,发病率为11. 13/10万。结论中小学生和散居儿童是流行性腮腺炎的主要防控人群,流行性腮腺炎模型预测效果良好,可用于流行性腮腺炎发病短期预测和动态分析。

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.