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[Construction of early warning model of influenza-like illness in Zhejiang Province based on support vector machine].
Lu, Han-ti; Li, Fu-dong; Lin, Jun-fen; He, Fan; Shen, Yi.
Afiliação
  • Lu HT; Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou 310058, China.
  • Li FD; The Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China.
  • Lin JF; The Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China.
  • He F; The Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China.
  • Shen Y; Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou 310058, China.
Zhejiang Da Xue Xue Bao Yi Xue Ban ; 44(6): 653-8, 2015 11.
Article em Zh | MEDLINE | ID: mdl-26822048
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vigilância de Evento Sentinela / Influenza Humana / Máquina de Vetores de Suporte Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: Zh Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vigilância de Evento Sentinela / Influenza Humana / Máquina de Vetores de Suporte Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: Zh Ano de publicação: 2015 Tipo de documento: Article