DETECT

remote sensing
random forest
lars
bayesian network
neural network
svm
Executed for

Institut Pasteur de Guyane

Published

December 31, 2015

Goal

DEngue Transmission and Emergence Control using Tele epidemiology.

Plus d’info

Activities

  • Systematic review
  • Comparison of machine learning algorithms with R : : Lasso, Incremental Forward Stagewise, Forward Stepwise (lars), ElasticNet (elasticnet), random forests (randomForest), Gradient Boosting Machine (gbm), Bayesian networks (bnlearn), SVM (svm), Neural networks (nnet)
  • Comparison of selected variables and predictive performances on raw data as well as raw data after processing the correlations
  • Drafting article

Article

Bailly, Sarah, Vanessa Machault, Samuel Beneteau, Philippe Palany, Camille Fritzell, Romain Girod, Jean-Pierre Lacaux, Philippe Quénel, and Claude Flamand. 2024. “Spatiotemporal Modeling of Aedes Aegypti Risk: Enhancing Dengue Virus Control Through Meteorological and Remote Sensing Data in French Guiana.” Pathogens 13 (9). https://doi.org/10.3390/pathogens13090738.

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