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@Article{LoweCBCCCRBSR:2016:EvPrDe,
               author = "Lowe, Rachel and Coelho, Caio Augusto dos Santos and Barcellos, 
                         Christovam and Carvalho, Marilia S{\'a} and Cat{\~a}o, Rafael de 
                         Castro and Coelho, Giovanini E. and Ramalho, Walter Massa and 
                         Bailey, Trevor C. and Stephenson, David B. and Rodo, Xavier",
          affiliation = "{Institut Catal{\`a}} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Funda{\c{c}}{\~a}o Oswaldo Cruz} and 
                         {Funda{\c{c}}{\~a}o Oswaldo Cruz} and {Institut Catal{\`a}} and 
                         {Minist{\'e}rio da Sa{\'u}de} and {Universidade de 
                         Bras{\'{\i}}lia (UnB)} and {University of Exeter} and 
                         {University of Exeter} and {Institut Catal{\`a}}",
                title = "Evaluating probabilistic dengue risk forecasts from a prototype 
                         early warning system for Brazil",
              journal = "Elife",
                 year = "2016",
               volume = "5",
                pages = "e11285",
                month = "Feb.",
             abstract = "Recently, 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.",
                  doi = "10.7554/eLife.11285",
                  url = "http://dx.doi.org/10.7554/eLife.11285",
                 issn = "2050-084X",
             language = "en",
           targetfile = "Lowe_evaluationg.pdf",
        urlaccessdate = "04 dez. 2020"
}


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