Chen Jian, Zhang Lei, Lu Shuai, Jiang Chen-yan, Hu Jia-yu, Jiang Qing-wu, Wu Fan
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.
Zhonghua Liu Xing Bing Xue Za Zhi. 2013 Apr;34(4):404-8.
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.
当前研究课题的目的是利用上海流感样病例（ILI）分析与预测的数据，测试广义相加模型（GAM）。通过收集2006年至2010年的气象数据以及ILI数据，我们基于GAM建立了几个非线性回归候选模型。这些模型考虑的因素包括：对气象数据的非线性依赖，即周平均气温、周平均（最高）气温差以及ILI。AIC（赤池信息准则）涉及两个简化模型，用于进一步的分析和预测。最后，数值例子表明所提出的模型能够揭示气象数据与ILI之间的联系。GAM可用于拟合上海ILI的发病频率和气象因素。所提出的模型能够准确分析ILI的发病情况，这意味着GAM可能适用于那些与气象相关疾病的预测和分析。
