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4 referências similares encontradas (inclusive a original) buscando em 22 dentre 22 Arquivos.
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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34P/3LF3CTS
Repositóriosid.inpe.br/mtc-m21b/2016/04.05.16.07
Última Atualização2016:04.05.16.09.51 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m21b/2016/04.05.16.07.45
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DOI10.7554/eLife.11285
ISSN2050-084X
Chave de CitaçãoLoweCBCCCRBSR:2016:EvPrDe
TítuloEvaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil
Ano2016
MêsFeb.
Data de Acesso16 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2840 KiB
2. Contextualização
Autor 1 Lowe, Rachel
 2 Coelho, Caio Augusto dos Santos
 3 Barcellos, Christovam
 4 Carvalho, Marilia Sá
 5 Catão, Rafael de Castro
 6 Coelho, Giovanini E.
 7 Ramalho, Walter Massa
 8 Bailey, Trevor C.
 9 Stephenson, David B.
10 Rodo, Xavier
Grupo 1
 2 DOP-CPT-INPE-MCTI-GOV-BR
Afiliação 1 Institut Català
 2 Instituto Nacional de Pesquisas Espaciais (INPE)
 3 Fundação Oswaldo Cruz
 4 Fundação Oswaldo Cruz
 5 Institut Català
 6 Ministério da Saúde
 7 Universidade de Brasília (UnB)
 8 University of Exeter
 9 University of Exeter
10 Institut Català
Endereço de e-Mail do Autor 1 rachel.lowe@ic3.cat
 2 caio.coelho@cptec.inpe.br
RevistaElife
Volume5
Páginase11285
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Tipo de Versãopublisher
ResumoRecently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.
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Arquivo AlvoLowe_evaluationg.pdf
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Repositório Espelhourlib.net/www/2011/03.29.20.55
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Lista de Itens Citando
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn keywords label lineage mark nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
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1. Identificação
Tipo de ReferênciaePrint (Electronic Source)
Sitemtc-m16d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP7W/37GKF42
Repositóriosid.inpe.br/mtc-m19@80/2010/05.18.12.09   (acesso restrito)
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Chave de CitaçãoLoweBaStGrCoCaBa::ToEaWa
TítuloSpatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil
Data da Última Atualização2010-05-19
Data de Acesso16 maio 2024
Tipo de SuporteOn-line
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho815 KiB
2. Contextualização
Autor1 Lowe, Rachel
2 Baileya, Trevor C.
3 Stephensona, David B.
4 Grahamb, Richard J.
5 Coelhoc, Caio. A. S.
6 Carvalhod, Marilia. S´a
7 Barcellos, Christovam
Grupo1
2
3
4
5 DOP-CPT-INPE-MCT-BR
Afiliação1
2
3
4
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Publicação AlternativaComputers and Geosciences
ProdutorInstituto Nacional de Pesquisas Espaciais
CidadeSão José dos Campos
Estágio da Publicação Alternativasubmitted
Histórico (UTC)2010-05-18 12:48:41 :: deicy -> administrator ::
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2021-01-03 02:01:58 :: administrator -> deicy ::
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoem andamento
Transferível1
Palavras-Chavedengue fever
prediction
epidemic
spatio-temporal model
seasonal climate forecasts
ResumoThis paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5◦ × 2.5◦ longitude-latitude grid with time lags relevant to dengue transmission, an El Nino Southern Oscillation index and other relevant socio-economic and environmental variables. A Negative-Binomial model formulation is adopted in this model selection to allow for extra- Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM - generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.
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6. Notas
Campos Vaziosaccessyear archivingpolicy archivist contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi e-mailaddress edition electronicmailaddress format isbn issn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url versiontype year
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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m16d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP7W/38DDKL8
Repositóriosid.inpe.br/mtc-m19/2010/10.11.19.13
Última Atualização2010:10.11.19.21.58 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m19/2010/10.11.19.13.06
Última Atualização dos Metadados2021:01.02.22.17.21 (UTC) administrator
Chave SecundáriaINPE--PRE/
DOI10.1016/j.cageo.2010.01.008
ISSN0098-3004
Chave de CitaçãoLoweBaStGrCoSáBa:2011:ToEaWa
TítuloSpatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil
ProjetoLeverhulme Trust[F/00 144/AT]; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)[2005/05210-7, 2006/02497-6]
Ano2011
MêsMar.
Data de Acesso16 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho1600 KiB
2. Contextualização
Autor1 Lowe, Rachel
2 Bailey, Trevor C.
3 Stephenson, David B.
4 Graham, R. J.
5 Coelho, Caio Augusto dos Santos
6 Sá Carvalho, Marilia
7 Barcellos, Christovam
Grupo1
2
3
4
5 DOP-CPT-INPE-MCT-BR
Afiliação1 School of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road
2 School of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road
3 School of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road
4 Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Oswaldo Cruz Foundation, Health Information Research Laboratory, LIS/ICICT/Fiocruz, Av. Brasil, Manguinhos, Rio de Janeiro
7 Oswaldo Cruz Foundation, Health Information Research Laboratory, LIS/ICICT/Fiocruz, Av. Brasil, Manguinhos, Rio de Janeiro
Endereço de e-Mail do Autor1
2
3
4
5 caio.coelho@cptec.inpe.br
RevistaComputers and Geosciences
Volume37
Número3 Special Issue
Páginas371-381
Nota SecundáriaA2_CIÊNCIA_DA_COMPUTAÇÃO B4_CIÊNCIAS_BIOLÓGICAS_II B1_ENGENHARIAS_I B1_GEOCIÊNCIAS A2_INTERDISCIPLINAR
Histórico (UTC)2011-07-25 15:50:10 :: valdirene -> administrator :: 2010 -> 2011
2011-07-25 15:50:15 :: administrator -> valdirene :: 2011
2011-07-25 15:51:09 :: valdirene -> administrator :: 2011
2011-07-25 16:38:02 :: administrator -> valdirene :: 2011
2011-11-04 12:07:35 :: valdirene -> banon :: 2011
2011-11-23 14:07:11 :: banon -> administrator :: 2011
2021-01-02 22:17:21 :: administrator -> valdirene :: 2011
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãofinaldraft
Palavras-Chavedengue fever
epidemic
prediction
seasonal climate forecasts
spatio-temporal model
ResumoThis paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2 . 5° × 2 . 5° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM-generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil. © 2010 Elsevier Ltd. All rights reserved.
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URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP7W/38DDKL8
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP7W/38DDKL8
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Repositório Espelhosid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Unidades Imediatamente Superiores8JMKD3MGPCW/43SQKNE
Lista de Itens Citandosid.inpe.br/bibdigital/2021/01.02.22.14 1
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX.
Acervo Hospedeirosid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress resumeid rightsholder schedulinginformation secondarydate session shorttitle sponsor subject tertiarymark tertiarytype url
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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP5W34M/3HE6FDM
Repositóriosid.inpe.br/mtc-m21b/2014/11.18.23.58.31   (acesso restrito)
Última Atualização2014:12.16.13.01.17 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m21b/2014/11.18.23.58.32
Última Atualização dos Metadados2021:01.02.22.17.08 (UTC) administrator
DOI10.1016/S1473-3099(14)70781-9
ISSN1473-3099
Rótuloscopus 2014-11 LoweBCBCGJRCSR:2014:EaWaMo
Chave de CitaçãoLoweBCBCGJRCSR:2014:EaWaMo
TítuloDengue outlook for the World Cup in Brazil: An early warning model framework driven by real-time seasonal climate forecasts
Ano2014
MêsJuly
Data de Acesso16 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2764 KiB
2. Contextualização
Autor 1 Lowe, R.
 2 Barcellos, C.
 3 Coelho, Caio Augusto dos Santos
 4 Bailey, T. C.
 5 Coelho, G. E.
 6 Graham, R.
 7 Jupp, T.
 8 Ramalho, W. M.
 9 Carvalho, M. S.
10 Stephenson, D. B.
11 Rodó, X.
Grupo 1
 2
 3 DOP-CPT-INPE-MCTI-GOV-BR
Afiliação 1 Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima (IC3), Barcelona, Spain
 2 Fundação Oswaldo Cruz (FIOCRUZ), Rio de Janeiro, RJ, Brazil
 3 Instituto Nacional de Pesquisas Espaciais (INPE)
 4 Exeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
 5 Coordenação Geral do Programa Nacional de Controle da Dengue, Ministério da Saúde, Brasília, DF, Brazil
 6 Met Office Hadley Centre, Exeter, Devon, United Kingdom
 7 Exeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
 8 Faculdade de Ceilândia, Universidade de Brasília, Brasília, DF, Brazil
 9 Fundação Oswaldo Cruz (FIOCRUZ), Rio de Janeiro, RJ, Brazil
10 Exeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
11 Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima (IC3), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
Endereço de e-Mailmarcelo.pazos@inpe.br
RevistaLancet Infectious Diseases
Volume14
Número7
Páginas619-626
Nota SecundáriaA1_MEDICINA_II A1_CIÊNCIAS_BIOLÓGICAS_III A1_SAÚDE_COLETIVA A1_CIÊNCIAS_BIOLÓGICAS_II A1_MEDICINA_I
Histórico (UTC)2021-01-02 22:17:08 :: administrator -> marciana :: 2014
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chavealertness
article
Brazil
climate change
conceptual framework
dengue
disease transmission
epidemic
epidemiological monitoring
forecasting
high risk population
human
incidence
information processing
population density
priority journal
real time seasonal climate forecast
risk assessment
risk factor
seasonal variation
spatiotemporal analysis
Bayes Theorem
Brazil
Climate
Dengue
Forecasting
Humans
Risk
Seasons
Soccer
ResumoBackground: With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. Methods: We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. Findings: Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (phigh = 19%), Fortaleza (phigh = 46%), and Natal (phigh=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). Interpretation: This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. Funding: European Commission's Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro. © 2014 Elsevier Ltd.
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Unidades Imediatamente Superiores8JMKD3MGPCW/43SQKNE
Lista de Itens Citando
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Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel electronicmailaddress format isbn lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarytype url
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