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Dengue is an arbovirus transmitted by vectors as Aedes mosquitoes, and is considered one of the most dangerous diseases transmitted by these ones, since it diffusion may be considered so fast leading to death (when manifested in hemorrhagic dengue way mainly). In Brazil, the amount of dengue cases has been growing up in a terrible way, presenting cyclic outbreaks, and epidemics occurrences. Therefore, the government investments has been increasing aiming decrease it, seeking understanding the variables that contribute to vector development, as well as, the use of forecast computational tools. In this context, suitable data treatment is required, and shows up the disease incidence dynamic. Thus, dengue data cases in temporal series way have been recurrently used. From this data, it is possible perform analytics methods that allows dengue cases forecast and subsequent prevention cases manifestation of this disease. One of the most efficient technique presented in literature is temporal series analyses with intelligent systems (as Artificial Neural Network) and signals analyses technique (as Discrete Wavelet Transform) approach. This technique called ensemble DWT-ANN approach (Discrete Wavelet Transform with Artificial Neural Network), has showing up bests results than another classical based on statistics methods only. Thus, the efficiency of ensemble DWT-ANN in dengue cases forecast is investigated, analyzing data from Salvador City, by mensal dengue cases evidenced between the years 2000 to 2008, this data was obtained in SINAN (Sistema de Informações de Agravos de Notificação) website.

interfaces científicas. saúde e ambiente

PROGNÓSTICO DA INCIDÊNCIA DE CASOS DE DENGUE NA CIDADE DE SALVADOR – BAHIA, UTILIZANDO A TRANSFORMADA WAVELET DISCRETA EM CONJUNÇÃO COM REDES NEURAIS ARTIFICIAIS