Anopheles number prediction on environmental and climate variables using Lasso and stratified two levels cross validation

TL;DR

This study predicts Anopheles mosquito numbers using environmental and climate data, employing Lasso for variable selection and stratified cross-validation, resulting in improved accuracy and efficiency over traditional methods.

Contribution

Introduces a novel combination of Lasso-based variable selection and stratified two-level cross-validation for mosquito number prediction.

Findings

Better variable selection and prediction accuracy than B-GLM.

Reduced computational time compared to existing methods.

Effective identification of relevant environmental factors.

Abstract

This paper deals with prediction of anopheles number using environmental and climate variables. The variables selection is performed by an automatic machine learning method based on Lasso and stratified two levels cross validation. Selected variables are debiased while the predictionis generated by simple GLM (Generalized linear model). Finally, the results reveal to be qualitatively better, at selection, the prediction,and the CPU time point of view than those obtained by B-GLM method.