摘要
目的对2010—2019年鞍山市流行性出血热每季度发病率和流行病学特征进行分析并建立指数平滑法预测模型,为制定鞍山市流行性出血热防控提供依据。方法采用Excel 2010建立2010—2019年鞍山市流行性出血热数据库,通过SPSS 25.0构建时间序列图和指数平滑预测模型,并对2019年该市流行性出血热发病率做出预测。结果2010—2019年鞍山市出血热累计报告病例394例,年均发病率是1.09/10万,男女性别比例是3.24∶1。发病以青壮年为主,25岁以下人群出血热发病率是0.27/10万,25~60岁人群出血热发病率是1.34/10万,60岁以上人群出血热发病率是1.41/10万。流行性出血热城市年均发病率是0.33/10万,农村年均发病率是1.70/10万。采用指数平滑法构建的最优模型是简单季节性模型,拟合R^(2)=0.803,实际观测值和拟合值的绝对误差是0.04,相对误差是3.25%。结论指数平滑法模型能较好地预测鞍山市2019年流行性出血热发病趋势,为防控工作提供依据。
Objective To analyze the surveillance data of epidemic hemorrhagic fever(EHF)and construct the exponential smoothing prediction model in Anshan City from 2010 to 2019,and to provide scientific basis for the prevention and treatment of EHF.Methods The database of epidemic hemorrhagic fever in Anshan City from 2010 to 2019 was established with Excel 2010.The time series diagram and exponential smoothing prediction model were constructed with SPSS 25.0,and the incidence of epidemic hemorrhagic fever in Anshan city in 2019 was predicted.Results There were cumulative 394 cases of hemorrhagic fever reported in Anshan from 2010 to 2019,with the annual incidence rate of 1.09/100000 and a male to female ratio of 3.24∶1.The patients were mainly among young adults.The average incidences of EHF were 0.27/100000 in the age group younger than 25,1.34/100000 in the group aged 25-60,and 1.41/100000 in the age group older than 60.The annual incidences of EHF were 0.33/100000 in the urban and 1.70/100000 in rural areas.The optimal model constructed by exponential smoothing was a simple seasonal model:R^(2)=0.803,absolute error=0.04,relative error=3.25%.Conclusion Exponential smoothing model could simulate the incidence trend of EHF in Anshan City in 2019,and provide basis for prevention and control work.
作者
尹晔
张宪策
YIN Ye;ZHANG Xian-ce(Anshan Center for Disease Control and Prevention y Anshan 114000,China)
出处
《中华卫生杀虫药械》
CAS
2021年第6期544-547,共4页
Chinese Journal of Hygienic Insecticides and Equipments
基金
辽宁省自然科学基金指导计划(编号:20180550895)。
关键词
流行性出血热
指数平滑法
预测
epidemic hemorrhagic fever
exponential smoothing
predict