Title:
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Predicting seasonal influenza transmission using functional regression models with temporal dependence
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Author:
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Oviedo de la Fuente, Manuel; Febrero Bande, Manuel; Muñoz Gracia, María del Pilar; Domínguez García, Angela
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Abstract:
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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. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Medicina comunitària i salut pública -Influenza--Epidemiology--Mathematical models. -Grip -Epidemiologia -- Models matemàtics |
Rights:
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Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/ |
Document type:
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Article - Published version Article |
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