dc.creatorNobre, Aline Araujo
dc.creatorSchmidt, Alexandra Mello
dc.creatorLopes, Hedibert Freitas
dc.date2019-09-12T17:21:24Z
dc.date2019-09-12T17:21:24Z
dc.date2005
dc.date.accessioned2023-09-26T20:42:00Z
dc.date.available2023-09-26T20:42:00Z
dc.identifierNOBRE, Aline Araujo; SCHMIDT, Alexandra Mello; LOPES, Hedibert Freitas. Spatio‐temporal models for mapping the incidence of malaria in Pará. Environmetrics, v. 16, p. 291-304, 2005.
dc.identifier1180-4009
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/35552
dc.identifier10.1002/env.704
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8862260
dc.descriptionOur main aims in this article are: (i) to model the means by which rainfall affects malaria incidence in the state of Pará, one of Brazil's largest states; and (ii) to check for similarities along the counties in the state. We use state of the art spatial–temporal models which can, we believe, anticipate various kinds of interactions and relations that might be present in the data. We use the traditional Poisson–normal model where, at any given time, the incidences of malaria for any two counties are conditionally independent and Poisson distributed with log‐mean explained by rainfall and random effects terms. Our methodological contribution is in allowing some of the random effects variances to evolve with time according to a dynamic model. Additionally, the change of support problem caused by combining malaria counts (per county) with rainfall (per station) is partially solved by interpolating the whole state through a Gaussian process. Posterior inference and model comparison are computationally assessed via Markov chain Monte Carlo (MCMC) methods and deviance information criteria (DIC), respectively. Copyright © 2005 John Wiley & Sons, Ltd.
dc.formatapplication/pdf
dc.rightsrestricted access
dc.subjectBayesian kriging
dc.subjectChange of support
dc.subjectConditional autoregressive models
dc.subjectRelative risk
dc.subjectSpatio‐temporal interaction
dc.titleSpatio‐temporal models for mapping the incidence of malaria in Pará
dc.typeArticle


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