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1. Identity statement
Reference TypeePrint (Electronic Source)
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/37GKF42
Repositorysid.inpe.br/mtc-m19@80/2010/05.18.12.09   (restricted access)
Last Update2010:05.18.12.09.52 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19@80/2010/05.18.12.09.51
Metadata Last Update2021:01.03.02.01.58 (UTC) administrator
Citation KeyLoweBaStGrCoCaBa::ToEaWa
TitleSpatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil
Last Update Date2010-05-19
Access Date2024, Apr. 28
Type of MediumOn-line
Secondary TypePRE PI
Number of Files1
Size815 KiB
2. Context
Author1 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
Group1
2
3
4
5 DOP-CPT-INPE-MCT-BR
Affiliation1
2
3
4
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Alternate PublicationComputers and Geosciences
ProducerInstituto Nacional de Pesquisas Espaciais
CitySão José dos Campos
Stage of Alternate Publicationsubmitted
History (UTC)2010-05-18 12:48:41 :: deicy -> administrator ::
2021-01-03 01:56:07 :: administrator -> marciana ::
2021-01-03 01:58:43 :: marciana -> administrator ::
2021-01-03 02:01:58 :: administrator -> deicy ::
3. Content and structure
Is the master or a copy?is the master
Content Stagework-in-progress
Transferable1
Keywordsdengue fever
prediction
epidemic
spatio-temporal model
seasonal climate forecasts
AbstractThis 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.
AreaMET
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDOP > Spatio-temporal modelling of...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target Filev1.pdf
User Groupadministrator
deicy
Visibilityshown
Read Permissiondeny from all
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/43SQKNE
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsaccessyear 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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