%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Kouwaye, B.
%A Rossi, F.
%A Fonton, N.
%A Garcia, André
%A Dossou-Gbété, S.
%A Hounkonnou, M. N.
%A Cottrell, Gilles
%T Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm
%D 2017
%L fdi:010071344
%G ENG
%J PLOS One
%@ 1932-6203
%K BENIN
%M ISI:000414088900052
%N 10
%P e0187234 [14 ]
%R 10.1371/journal.pone.0187234
%U https://www.documentation.ird.fr/hor/fdi:010071344
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers17-11/010071344.pdf
%V 12
%W Horizon (IRD)
%X Recent studies have highlighted the importance of local environmental factors to determine the fine-scale heterogeneity of malaria transmission and exposure to the vector. In this work, we compare a classical GLM model with backward selection with different versions of an automatic LASSO-based algorithm with 2-level cross-validation aiming to build a predictive model of the space and time dependent individual exposure to the malaria vector, using entomological and environmental data from a cohort study in Benin. Although the GLM can outperform the LASSO model with appropriate engineering, the best model in terms of predictive power was found to be the LASSO-based model. Our approach can be adapted to different topics and may therefore be helpful to address prediction issues in other health sciences domains.
%$ 052 ; 020