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