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浙江大学学报（医学版）
浙江大学学报（医学版）  2015, Vol. 44 Issue (6): 653-658    DOI: 10.3785/j.issn.1008-9292.2015.11.09
原著     
基于支持向量机的浙江省流感样病例预警模型研究
卢汉体1, 李傅冬2, 林君芬2, 何凡2, 沈毅1
1. 浙江大学公共卫生学院流行病与卫生统计学系, 浙江 杭州 310058;
2. 浙江省疾病预防控制中心, 浙江 杭州 310051
Construction of early warning model of influenza-like illness in Zhejiang Province based on support vector machine
LU Han-ti1, LI Fu-dong2, LIN Jun-fen2, HE Fan2, SHEN Yi1
1. Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou 310058, China;
2. The Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China
全文: PDF(709 KB)  
摘要: 

目的:建立浙江省流感样病例预警模型,为流感疫情的早期发现提供科学依据。方法:收集整理2012年1月2日至2013年12月29日期间104周浙江省11家哨点医院门急诊中流感相关疾病病例数、各类气象因素以及流感病原阳性率,与同期流感样病例数作相关分析,寻找出流感样病例发生的相关因素。通过交叉检验选取最优参数,采用支持向量机方法建立流感样病例预警模型,并利用历史数据对模型进行验证。结果:相关性分析显示有8个因素与流感样病例相关。模型的最优参数为:C=3,ε=0.009,γ=0.4,验证结果显示流感样病例预警模型的同级预报正确率为50.0%,相差一级的预报正确率为96.7%。结论:支持向量机方法适用于流感样病例的预警。
关键词 ： 流感, 人/流行病学,  人工智能,  模型, 统计学,  预测/方法    
Abstract：

Objective: To construct a forecasting model of influenza-like illness in Zhejiang Province. Methods: The number of influenza-like cases and related pathogens among outpatients and emergency patients were obtained from 11 sentinel hospitals in Zhejiang Province during 2012 to 2013 (total 104 weeks), and corresponding meteorological factors were also collected. The epidemiological characteristics of influenza during the period were then analyzed. Linear correlation and rank correlation analyses were conducted to explore the association between influenza-like illness and related factors. Optimal parameters were selected by cross validation. Support vector machine was used to construct the forecasting model of influenza-like illness in Zhejiang Province and verified by the historical data. Results: Correlation analysis indicated that 8 factors were associated with influenza-like illness occurred in one week. The results of cross validation showed that the optimal parameters were C=3, ε=0.009 and γ=0.4. The results of influenza-like illness forecasting model after verification revealed that support vector machine had the accuracy of 50.0% for prediction with the same level, while it reached 96.7% for prediction within the range of one level higher or lower. Conclusion: Support vector machine is suitable for early warning of influenza-like illness.
Key words： Influenza, human/epidemiology    Artificial intelligence    Models, statistical    Forecasting/methods
收稿日期: 2015-05-04 出版日期: 2015-11-12
CLC:  R18  
基金资助:

浙江省医药卫生科技计划(2014KYB056)
通讯作者: 沈毅(1956-),男,硕士,副教授,硕士生导师,主要从事统计学在医学研究中的应用;E-mail:shenyi@zju.edu.cn;何凡(1979-),男,硕士,副主任医师,主要从事公共卫生监测与疾病控制研究;E-mail:fhe@cdc.zj.cn     E-mail: shenyi@zju.edu.cn;shenyi@zju.edu.cn.
作者简介: 卢汉体(1989-),男,硕士研究生,主要从事统计学在医学研究中的应用;E-mail:luhanti@126.com;http://orcid.org/0000-0001-9358-4225
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引用本文:

卢汉体等. 基于支持向量机的浙江省流感样病例预警模型研究[J]. 浙江大学学报（医学版）, 2015, 44(6): 653-658.
LU Han-ti, LI Fu-dong, LIN Jun-fen, HE Fan, SHEN Yi. Construction of early warning model of influenza-like illness in Zhejiang Province based on support vector machine. Journal of ZheJiang University(Medical Science), 2015, 44(6): 653-658.

链接本文:

http://www.zjujournals.com/xueshu/med/CN/10.3785/j.issn.1008-9292.2015.11.09      或      http://www.zjujournals.com/xueshu/med/CN/Y2015/V44/I6/653

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