Predicting seasonal influenza transmission using functional regression models with temporal dependence
Predicting seasonal influenza transmission using functional regression models with temporal dependence
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DOI：
10.1371/journal.pone.0194250
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发表时间：
2018-04-25
期刊：
影响因子：
3.7
通讯作者：
Dominguez, Angela
中科院分区：
文献类型：
--
作者：
Oviedo de la Fuente, Manuel;Febrero-Bande, Manuel;Dominguez, Angela
本文提出了一种新的方法，利用气象信息来预测流感在加利西亚（西班牙）的发病率。它将多元框架中的广义最小二乘（GLS）方法推广到具有相关误差的函数回归模型。当流感发病率的近期历史很容易无法获得（例如，由于与健康信息提供者的通信延迟），并且预测必须通过校正残差的时间依赖性和使用更容易获得的变量来构建时，这些类型的模型是有用的。模拟研究表明，GLS估计提供更好的估计与回归模型的参数比他们做的经典模型。从预测的角度来看，它们获得了非常好的结果，并且与流感发病率的经典时间序列方法具有竞争力。还提出了一种迭代版本的GLS估计（称为iGLS），可以帮助建模复杂的依赖结构。在构建模型时，采用距离相关性测度R选择相关信息来预测流感率混合多变量和函数变量。这类模型对于卫生管理人员提前分配资源以管理流感流行非常有用。
This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure R was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.