Surveillance of dengue vectors using spatio-temporal Bayesian modeling

Detalhes bibliográficos
Autor(a) principal: Costa, Ana Carolina C.
Data de Publicação: 2015
Outros Autores: Codeço, Claudia T., Honório, Nildimar A., Pereira, Gláucio R., Pinheiro, Carmen Fátima N., Nobre, Aline A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/14301
Resumo: Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sérgio Arouca. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Rio de Janeiro, RJ, Brasil.
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spelling Costa, Ana Carolina C.Codeço, Claudia T.Honório, Nildimar A.Pereira, Gláucio R.Pinheiro, Carmen Fátima N.Nobre, Aline A.2016-05-17T11:29:06Z2016-05-17T11:29:06Z2015COSTA, Ana Carolina C. et al. Surveillance of dengue vectors using spatio-temporal Bayesian modeling. BMC Medical Informatics and Decision Making, v.15, n. 93, p.1-12, 2015.1472-6947https://www.arca.fiocruz.br/handle/icict/1430110.1186/s12911-015-0219-6engBioMed CentralVigilância entomológicaMétodos BayesianModelos espaço-temporaisDengueEntomological surveillanceDengueBayesian methodsSpatio-temporal modelsZero-inflated modelsINLASurveillance of dengue vectors using spatio-temporal Bayesian modelinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sérgio Arouca. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Transmissores de Hematozoários. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.Background At present, dengue control focuses on reducing the density of the primary vector for the disease, Aedes aegypti, which is the only vulnerable link in the chain of transmission. The use of new approaches for dengue entomological surveillance is extremely important, since present methods are inefficient. With this in mind, the present study seeks to analyze the spatio-temporal dynamics of A. aegypti infestation with oviposition traps, using efficient computational methods. These methods will allow for the implementation of the proposed model and methodology into surveillance and monitoring systems. Methods The study area includes a region in the municipality of Rio de Janeiro, characterized by high population density, precarious domicile construction, and a general lack of infrastructure around it. Two hundred and forty traps were distributed in eight different sentinel areas, in order to continually monitor immature Aedes aegypti and Aedes albopictus mosquitoes. Collections were done weekly between November 2010 and August 2012. The relationship between egg number and climate and environmental variables was considered and evaluated through Bayesian zero-inflated spatio-temporal models. Parametric inference was performed using the Integrated Nested Laplace Approximation (INLA) method.Results Infestation indexes indicated that ovipositing occurred during the entirety of the study period. The distance between each trap and the nearest boundary of the study area, minimum temperature and accumulated rainfall were all significantly related to the number of eggs present in the traps. Adjusting for the interaction between temperature and rainfall led to a more informative surveillance model, as such thresholds offer empirical information about the favorable climatic conditions for vector reproduction. Data were characterized by moderate time (0.29 – 0.43) and spatial (21.23 – 34.19 m) dependencies. The models also identified spatial patterns consistent with human population density in all sentinel areas. The results suggest the need for weekly surveillance in the study area, using traps allocated between 18 and 24 m, in order to understand the dengue vector dynamics. Conclusions Aedes aegypti, due to it short generation time and strong response to climate triggers, tend to show an eruptive dynamics that is difficult to predict and understand through just temporal or spatial models. The proposed methodology allowed for the rapid and efficient implementation of spatio-temporal models that considered zero-inflation and the interaction between climate variables and patterns in oviposition, in such a way that the final model parameters contribute to the identification of priority areas for entomological surveillance.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/14301/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALnildimar4_honorio_etal_IOC_2015.pdfnildimar4_honorio_etal_IOC_2015.pdfapplication/pdf3586921https://www.arca.fiocruz.br/bitstream/icict/14301/2/nildimar4_honorio_etal_IOC_2015.pdfa60a1ba3fa7710be19cd67213eeef9ceMD52TEXTnildimar4_honorio_etal_IOC_2015.pdf.txtnildimar4_honorio_etal_IOC_2015.pdf.txtExtracted 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dc.title.pt_BR.fl_str_mv Surveillance of dengue vectors using spatio-temporal Bayesian modeling
title Surveillance of dengue vectors using spatio-temporal Bayesian modeling
spellingShingle Surveillance of dengue vectors using spatio-temporal Bayesian modeling
Costa, Ana Carolina C.
Vigilância entomológica
Métodos Bayesian
Modelos espaço-temporais
Dengue
Entomological surveillance
Dengue
Bayesian methods
Spatio-temporal models
Zero-inflated models
INLA
title_short Surveillance of dengue vectors using spatio-temporal Bayesian modeling
title_full Surveillance of dengue vectors using spatio-temporal Bayesian modeling
title_fullStr Surveillance of dengue vectors using spatio-temporal Bayesian modeling
title_full_unstemmed Surveillance of dengue vectors using spatio-temporal Bayesian modeling
title_sort Surveillance of dengue vectors using spatio-temporal Bayesian modeling
author Costa, Ana Carolina C.
author_facet Costa, Ana Carolina C.
Codeço, Claudia T.
Honório, Nildimar A.
Pereira, Gláucio R.
Pinheiro, Carmen Fátima N.
Nobre, Aline A.
author_role author
author2 Codeço, Claudia T.
Honório, Nildimar A.
Pereira, Gláucio R.
Pinheiro, Carmen Fátima N.
Nobre, Aline A.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Costa, Ana Carolina C.
Codeço, Claudia T.
Honório, Nildimar A.
Pereira, Gláucio R.
Pinheiro, Carmen Fátima N.
Nobre, Aline A.
dc.subject.other.pt_BR.fl_str_mv Vigilância entomológica
Métodos Bayesian
Modelos espaço-temporais
Dengue
topic Vigilância entomológica
Métodos Bayesian
Modelos espaço-temporais
Dengue
Entomological surveillance
Dengue
Bayesian methods
Spatio-temporal models
Zero-inflated models
INLA
dc.subject.en.pt_BR.fl_str_mv Entomological surveillance
Dengue
Bayesian methods
Spatio-temporal models
Zero-inflated models
INLA
description Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sérgio Arouca. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Rio de Janeiro, RJ, Brasil.
publishDate 2015
dc.date.issued.fl_str_mv 2015
dc.date.accessioned.fl_str_mv 2016-05-17T11:29:06Z
dc.date.available.fl_str_mv 2016-05-17T11:29:06Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv COSTA, Ana Carolina C. et al. Surveillance of dengue vectors using spatio-temporal Bayesian modeling. BMC Medical Informatics and Decision Making, v.15, n. 93, p.1-12, 2015.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/14301
dc.identifier.issn.none.fl_str_mv 1472-6947
dc.identifier.doi.none.fl_str_mv 10.1186/s12911-015-0219-6
identifier_str_mv COSTA, Ana Carolina C. et al. Surveillance of dengue vectors using spatio-temporal Bayesian modeling. BMC Medical Informatics and Decision Making, v.15, n. 93, p.1-12, 2015.
1472-6947
10.1186/s12911-015-0219-6
url https://www.arca.fiocruz.br/handle/icict/14301
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/14301/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/14301/2/nildimar4_honorio_etal_IOC_2015.pdf
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