@InProceedings{LoweBaStGrCoCaBa:2009:ClDePr,
               author = "Lowe, Rachel and Bailey, Trevor C. and Stephenson, David B. and 
                         Graham, Richard and Coelho, Caio Augusto dos Santos and Carvalho, 
                         Marilia S{\'a} and Barcellos, Christovam",
          affiliation = "{} and {} and {} and {} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Funda{\c{c}}{\~a}o Oswaldo Cruz}",
                title = "Climate-based dengue predictions for Brazil",
                 year = "2009",
         organization = "International Conference: GeoInformatics for Environmental 
                         Surveillance, (Fourth StatGIS '2009).",
             keywords = "dengue transmission, climatic, forecasts, Amazon region.",
             abstract = "The purpose of this study is to assess the potential for using 
                         seasonal climate forecasts in an early warning system (EWS) for 
                         dengue fever incidence in Brazil. Data at heterogeneous spatial 
                         scales were combined in a negative binomial model using dengue 
                         fever data at the microregion level for the period January 2001- 
                         April 2008, gridded observed climate data with time lags relevant 
                         to dengue transmission and other socio-economic and environmental 
                         covariates. The same model was then refitted replacing observed 
                         climate with seasonal climate forecasts of the same variables 
                         issued 5 months previous to the dengue month of interest. 
                         Predictions from both models were tested by using the first 7 
                         years as a training dataset to predict the first 4 months of 2008 
                         when a dengue epidemic occurred in Brazil. Both models were able 
                         to capture high dengue incidence along the densely populated 
                         eastern coast of Brazil and low incidence in the South. The models 
                         did not perform so well in the Amazon region. We conclude that 
                         seasonal climate forecasts could have potential value in the 
                         context of a dengue EWS to predict the climatic conditions that 
                         may influence dengue incidence up to 5 months ahead of an epidemic 
                         in Brazil.",
  conference-location = "Milos island, Greece",
      conference-year = "17-19 jun.",
             language = "en",
           targetfile = "STATGIS09-Rachel-Lowe-finalversion-140509.pdf",
        urlaccessdate = "27 abr. 2024"
}