tetano
Editor, Senior Moderator
Zhonghua Liu Xing Bing Xue Za Zhi. 2013 Apr;34(4):404-8.
[Prediction of influenza-like illness in Shanghai based on the generalized additive method].
[Article in Chinese]
Chen J, Zhang L, Lu S, Jiang CY, Hu JY, Jiang QW, Wu F.
Source
Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China; School of Public Health, Key Laboratory of Public Health Safety in Ministry of Education, Fudan University.
Abstract
The aim of the current research topic was to test the generalized additive method (GAM), using data from the analysis and prediction on influenza-like illness (ILI) in Shanghai. Through collecting the meteorological data as well as the ILI from 2006 to 2010, we established several nonlinear regression candidate models based on the GAM. These models considered factors as: the nonlinear dependence on the meteorological data, i.e. weekly average temperature and weekly average (maximum) temperature differences and the ILI. The AIC (Akaike information criterion) involved two simplified models which were implemented for further analysis and prediction. Finally, numerical examples showed that the proposed models could shed light on the connection between the meteorological data and the ILI. GAM could be used to fit the frequencies of ILI and meteorological factors in Shanghai. The proposed models were able to accurately analyze the onset of ILI, implying that GAM might be suitable for the prediction and analysis of those meteorological correlative diseases.
PMID:
23937851
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/23937851
[Prediction of influenza-like illness in Shanghai based on the generalized additive method].
[Article in Chinese]
Chen J, Zhang L, Lu S, Jiang CY, Hu JY, Jiang QW, Wu F.
Source
Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China; School of Public Health, Key Laboratory of Public Health Safety in Ministry of Education, Fudan University.
Abstract
The aim of the current research topic was to test the generalized additive method (GAM), using data from the analysis and prediction on influenza-like illness (ILI) in Shanghai. Through collecting the meteorological data as well as the ILI from 2006 to 2010, we established several nonlinear regression candidate models based on the GAM. These models considered factors as: the nonlinear dependence on the meteorological data, i.e. weekly average temperature and weekly average (maximum) temperature differences and the ILI. The AIC (Akaike information criterion) involved two simplified models which were implemented for further analysis and prediction. Finally, numerical examples showed that the proposed models could shed light on the connection between the meteorological data and the ILI. GAM could be used to fit the frequencies of ILI and meteorological factors in Shanghai. The proposed models were able to accurately analyze the onset of ILI, implying that GAM might be suitable for the prediction and analysis of those meteorological correlative diseases.
PMID:
23937851
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/23937851