RT Journal Article
T1 Predicting seasonal influenza transmission using functional regression models with temporal dependence
A1 Oviedo de la Fuente, Manuel
A1 Febrero Bande, Manuel
A1 Muñoz Gracia, María del Pilar
A1 Domínguez García, Ángela
K1 Influenza
K1 Influenza A virus
K1 Mathematical functions
K1 Covariance
K1 Humidity
K1 Influenza viruses
K1 Simulation and modeling
K1 Modeling
AB 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  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
PB Public Library of Science
YR 2018
FD 2018-04-25
LK http://hdl.handle.net/10347/18397
UL http://hdl.handle.net/10347/18397
LA eng
NO Oviedo de la Fuente M, Febrero-Bande M, Muñoz MP, Domínguez À (2018) Predicting seasonal influenza transmission using functional regression models with temporal dependence. PLoS ONE 13(4): e0194250. https://doi.org/10.1371/journal.pone.0194250
NO This study was funded by the Catalan Agency for the Management of Grants for University Research (AGAUR Grant number 2014/ SGR 1403) and cofunded by the Spanish Ministry of Economy and Competitiveness (Grant numbers MTM2013-41383-P and MTM2016-76969-P) and European Regional Development Fund (ERDF)
DS Minerva
RD 23 abr 2026