Spatial modelling for population replacement of mosquito vectors at continental scale
Fig 1
The forward selection process for the neural network model that estimates the relative abundance of the two subspecies.
For each round of selection, the validation loss (a measure of how well the model predicts to novel data—lower is better) is shown with a different colour for each predictor. The width of the violin plots reflect the frequency of the validation loss across the 500 individual neural network runs, with the mean shown as a large dot. The forward selection process begins (Round 1) by calculating the validation loss for ten models that each include just a single different predictor. The predictor leading to the “best” model with the lowest mean validation loss is then accepted (Round 1: Mean Annual Temperature). The selection process continues (Round 2), calculating validation loss for nine models including Round 1’s accepted predictor, and one new predictor. Similarly, the predictor leading to the best model is then accepted (Round 2: Latitude). This process is repeated until accepting a new predictor no longer improves the model. In our analysis, the selection process stopped after Round 4.