@Article{LoweBaStGrCoSáBa:2011:ToEaWa,
               author = "Lowe, Rachel and Bailey, Trevor C. and Stephenson, David B. and 
                         Graham, R. J. and Coelho, Caio Augusto dos Santos and S{\'a} 
                         Carvalho, Marilia and Barcellos, Christovam",
          affiliation = "School of Engineering, Mathematics and Physical Sciences, 
                         University of Exeter, Harrison Building, North Park Road and 
                         School of Engineering, Mathematics and Physical Sciences, 
                         University of Exeter, Harrison Building, North Park Road and 
                         School of Engineering, Mathematics and Physical Sciences, 
                         University of Exeter, Harrison Building, North Park Road and Met 
                         Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and Oswaldo 
                         Cruz Foundation, Health Information Research Laboratory, 
                         LIS/ICICT/Fiocruz, Av. Brasil, Manguinhos, Rio de Janeiro and 
                         Oswaldo Cruz Foundation, Health Information Research Laboratory, 
                         LIS/ICICT/Fiocruz, Av. Brasil, Manguinhos, Rio de Janeiro",
                title = "Spatio-temporal modelling of climate-sensitive disease risk: 
                         Towards an early warning system for dengue in Brazil",
              journal = "Computers and Geosciences",
                 year = "2011",
               volume = "37",
               number = "3 Special Issue",
                pages = "371--381",
                month = "Mar.",
             keywords = "dengue fever, epidemic, prediction, seasonal climate forecasts, 
                         spatio-temporal model.",
             abstract = "This 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.",
                  doi = "10.1016/j.cageo.2010.01.008",
                  url = "http://dx.doi.org/10.1016/j.cageo.2010.01.008",
                 issn = "0098-3004",
             language = "en",
           targetfile = "Lowe_Spatio-temporal.pdf",
        urlaccessdate = "27 abr. 2024"
}