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Predicting seasonal influenza transmission using functional regression models with temporal dependence

Publication Date: April 25, 2018
Authors: Manuel Oviedo De La Fuente, Manuel Febrero Bande, María Pilar Muñoz & Àngela Domínguez
Volume: 13
Issue: 4
Pages: e0194250
DOI: https://dx.plos.org/10.1371/journal.pone.0194250
Publisher URL: http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0194250
Scopus: 85045989421
Mendeley: http://www.mendeley.com/research/predicting-seasonal-influenza-transmission-using-regression-models-temporal-dependence

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Mendeley 517 May 06:20 UTC

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{"title"=>"Predicting seasonal influenza transmission using Regression Models with Temporal Dependence", "type"=>"journal", "authors"=>[{"first_name"=>"Manuel Oviedo", "last_name"=>"de la Fuente"}, {"first_name"=>"Manuel Febrero", "last_name"=>"Bande"}, {"first_name"=>"María Pilar", "last_name"=>"Muñoz"}, {"first_name"=>"Àngela", "last_name"=>"Domínguez"}], "year"=>2016, "identifiers"=>{"sgr"=>"85045989421", "arxiv"=>"1610.08718", "pui"=>"621788523", "scopus"=>"2-s2.0-85045989421", "doi"=>"10.1371/journal.pone.0194250", "issn"=>"19326203"}, "id"=>"f32630d4-afb9-3d4d-9002-9cbc75a43a49", "abstract"=>"In this manuscript, we use meteorological information in Galicia (Spain) to propose a novel approach to predict the incidence of influenza. Our approach extends the GLS methods in the multivariate framework to functional regression models with dependent errors. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model and obtain extremely good results from the predictive point of view. Thus they improve the classical linear approach. It proposes an iterative version of the GLS estimator (called iGLS) that can help to model complicated dependence structures, uses the distance correlation measure $\\mathcal{R}$ to select relevant information to predict influenza rate and applies the GLS procedure to the prediction of the influenza rate using readily available functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance for an epidemic outbreak.", "link"=>"http://www.mendeley.com/research/predicting-seasonal-influenza-transmission-using-regression-models-temporal-dependence", "reader_count"=>4, "reader_count_by_academic_status"=>{"Researcher"=>1, "Student > Postgraduate"=>1, "Student > Master"=>2}, "reader_count_by_user_role"=>{"Researcher"=>1, "Student > Postgraduate"=>1, "Student > Master"=>2}, "reader_count_by_subject_area"=>{"Nursing and Health Professions"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Mathematics"=>{"Mathematics"=>1}}, "group_count"=>1}

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Web of Science®27 May 20:57 UTC

{"id"=>"990354842152521728", "text"=>"Predicting seasonal influenza transmission using functional regression models with temporal dependence https://t.co/8kRxxlBrqi", "created_at"=>"2018-04-28T22:19:28Z", "user"=>"jmayer0716", "user_name"=>"Jonathan Mayer, PhD", "user_profile_image"=>"http://pbs.twimg.com/profile_images/971334017009119232/m-BC-ADT_normal.jpg"}

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