Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction

Detalhes bibliográficos
Autor(a) principal: Sousa, Larissa Braz [UNESP]
Data de Publicação: 2015
Outros Autores: Von Zuben, Claudio J. [UNESP], Von Zuben, Fernando J.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-319-21819-9_8
http://hdl.handle.net/11449/232454
Resumo: Based solely on the dengue confirmed-cases of six densely populated urban areas in Brazil, distributed along the country, we propose in this paper regularized linear and nonlinear autoregressive models for one-week ahead prediction of the future behaviour of each time series. Though exhibiting distinct temporal behaviour, all the time series were properly predicted, with a consistently better performance of the nonlinear predictors, based on MLP neural networks. Additional local information associated with environmental conditions will possibly improve the performance of the predictors. However, without including such local environmental variables, such as temperature and rainfall, the performance was proven to be acceptable and the applicability of the methodology can then be directly extended to endemic areas around the world characterized by a poor monitoring of environmental conditions. For tropical countries, predicting the short-term evolution of dengue confirmed-cases may represent a decisive feedback to guide the definition of effective sanitary policies.
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spelling Regularized linear and nonlinear autoregressive models for dengue confirmed-cases predictionDengue time seriesMLP neural networkRegularized linear predictorRegularized nonlinear predictorBased solely on the dengue confirmed-cases of six densely populated urban areas in Brazil, distributed along the country, we propose in this paper regularized linear and nonlinear autoregressive models for one-week ahead prediction of the future behaviour of each time series. Though exhibiting distinct temporal behaviour, all the time series were properly predicted, with a consistently better performance of the nonlinear predictors, based on MLP neural networks. Additional local information associated with environmental conditions will possibly improve the performance of the predictors. However, without including such local environmental variables, such as temperature and rainfall, the performance was proven to be acceptable and the applicability of the methodology can then be directly extended to endemic areas around the world characterized by a poor monitoring of environmental conditions. For tropical countries, predicting the short-term evolution of dengue confirmed-cases may represent a decisive feedback to guide the definition of effective sanitary policies.Zoology Department on Bioscience Institute, Sao Paulo State UniversityDepartment of Computer Engineering and Industrial Automation, University of CampinasZoology Department on Bioscience Institute, Sao Paulo State UniversityUniversidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Sousa, Larissa Braz [UNESP]Von Zuben, Claudio J. [UNESP]Von Zuben, Fernando J.2022-04-29T16:25:34Z2022-04-29T16:25:34Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject132-143http://dx.doi.org/10.1007/978-3-319-21819-9_8Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9252, p. 132-143.1611-33490302-9743http://hdl.handle.net/11449/23245410.1007/978-3-319-21819-9_82-s2.0-84943639597Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2022-04-29T16:25:34Zoai:repositorio.unesp.br:11449/232454Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:33:26.381607Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
title Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
spellingShingle Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
Sousa, Larissa Braz [UNESP]
Dengue time series
MLP neural network
Regularized linear predictor
Regularized nonlinear predictor
title_short Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
title_full Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
title_fullStr Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
title_full_unstemmed Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
title_sort Regularized linear and nonlinear autoregressive models for dengue confirmed-cases prediction
author Sousa, Larissa Braz [UNESP]
author_facet Sousa, Larissa Braz [UNESP]
Von Zuben, Claudio J. [UNESP]
Von Zuben, Fernando J.
author_role author
author2 Von Zuben, Claudio J. [UNESP]
Von Zuben, Fernando J.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Estadual de Campinas (UNICAMP)
dc.contributor.author.fl_str_mv Sousa, Larissa Braz [UNESP]
Von Zuben, Claudio J. [UNESP]
Von Zuben, Fernando J.
dc.subject.por.fl_str_mv Dengue time series
MLP neural network
Regularized linear predictor
Regularized nonlinear predictor
topic Dengue time series
MLP neural network
Regularized linear predictor
Regularized nonlinear predictor
description Based solely on the dengue confirmed-cases of six densely populated urban areas in Brazil, distributed along the country, we propose in this paper regularized linear and nonlinear autoregressive models for one-week ahead prediction of the future behaviour of each time series. Though exhibiting distinct temporal behaviour, all the time series were properly predicted, with a consistently better performance of the nonlinear predictors, based on MLP neural networks. Additional local information associated with environmental conditions will possibly improve the performance of the predictors. However, without including such local environmental variables, such as temperature and rainfall, the performance was proven to be acceptable and the applicability of the methodology can then be directly extended to endemic areas around the world characterized by a poor monitoring of environmental conditions. For tropical countries, predicting the short-term evolution of dengue confirmed-cases may represent a decisive feedback to guide the definition of effective sanitary policies.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2022-04-29T16:25:34Z
2022-04-29T16:25:34Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-319-21819-9_8
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9252, p. 132-143.
1611-3349
0302-9743
http://hdl.handle.net/11449/232454
10.1007/978-3-319-21819-9_8
2-s2.0-84943639597
url http://dx.doi.org/10.1007/978-3-319-21819-9_8
http://hdl.handle.net/11449/232454
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9252, p. 132-143.
1611-3349
0302-9743
10.1007/978-3-319-21819-9_8
2-s2.0-84943639597
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 132-143
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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