Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil
Rachel Lowea b✉, Caio A. S. Coelhoc, Christovam Barcellosd, Marília Sá Carvalhod, Rafael de Castro Catãoa b e, Giovanini Evelim Coelhof, Walter Massa Ramalhog, Trevor Baileyh, David B. Stephensonh, Xavier Rodóa b i
- aClimate Dynamics and Impacts Unit, Institut Català de Ciències del Clima, Barcelona, Spain
- bClimate Dynamics and Impacts Unit, Institut Catala `de Cie `ncies del Clima, Barcelona, Spain
- cCentro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, Brazil
- dFundação Oswaldo Cruz, Rio de Janeiro, Brazil
- eFaculdade de Ciências e Tecnologia, Universidade Estadual Paulista, Presidente Prudente, Brazil
- fCoordenação Geral do Programa Nacional de Controle da Dengue, Ministério da Saúde, Brasília, Brazil
- gFaculdade de Ceilândia, Universidade de Brasília, Brasília, Brazil
- hExeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- iInstitució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- ✉Corresponding author
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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.