Evidence map›Paper›PMID 26822048›Full record

ArticleZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2015

[Construction of early warning model of influenza-like illness in Zhejiang Province based on support vector machine].

Han-ti Lu, Fu-dong Li, Jun-fen Lin, Fan He, Yi Shen

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Article in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Han-ti LuDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou 310058, China.
Fu-dong LiThe Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China.
Jun-fen LinThe Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China.
Fan HeThe Center for Disease Control and Prevention of Zhejiang Province, Hangzhou 310051, China.
Yi ShenDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou 310058, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo construct a forecasting model of influenza-like illness in Zhejiang Province.

methodsThe 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.

resultsCorrelation 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.

conclusionSupport vector machine is suitable for early warning of influenza-like illness.

Indexed as

Sentinel SurveillanceSupport Vector MachineChinaForecastingHumansInfluenza, Human

Identifiers

PMID26822048
PMCPMC10400843

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