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In this study, we propose an automatic learning method for variablesselection based on Lasso in epidemiology context. One of the aim of thisapproach is to overcome the pretreatment of experts in medicine andepidemiology on collected data. These pretreatment consist in recoding somevariables and to choose some interactions based on expertise. The approachproposed uses all available explanatory variables without treatment andgenerate automatically all interactions between them. This lead to highdimension. We use Lasso, one of the robust methods of variable selection inhigh dimension. To avoid over fitting a two levels cross-validation is used.Because the target variable is account variable and the lasso estimators arebiased, variables selected by lasso are debiased by a GLM and used to predictthe distribution of the main vector of malaria which is Anopheles. Results showthat only few climatic and environmental variables are the mains factorsassociated to the malaria risk exposure.

S\'election de variables par le GLM-Lasso pour la pr\'ediction du risque palustre