Fig 1.
Framework of random forest algorithm.
1000 random bootstrap samples were drawn from the data, and an unpruned decision tree is fitted to each bootstrap sample. At each node, a small subset of the covariates was chosen at random to optimize the split. The predicted risk rank is obtained by averaging the prediction of all trees.
Table 1.
Overview of the risk factors used for dengue risk mapping.
Fig 2.
Partial dependence plot of the risk factors showing how dengue burden varies with one variable when all other variables are held constant at their average values.
Fig 3.
Variable importance plot showing population density, dengue burden and breeding percentage having stronger predictive power than other variables.
Fig 4.
Risk grouping of a small area with 2016 dengue cases (black circles) overlaid.
The risk groups are color-coded, with RG 4 (highest risk) as red and RG 1 (lowest risk) in light yellow. The figure was created using R software with base layer obtained from https://landsatlook.usgs.gov/.
Fig 5.
The predicted percentile ranks and its 80% prediction interval for 2014 to 2016 (left to right).
In each panel, the green circles indicate the predicted percentile ranks that fall within the prediction intervals and the red circles indicate the predicted percentile ranks that fall outside the prediction intervals.
Table 2.
Summary statistics of dengue case count in grids for all risk groups in 2014–2016.
Fig 6.
Stratification of clusters (2014–2016) by risk groups and the number of serotypes present in cluster.
Table 3.
The median transmission duration, cluster size and growth rate of clusters with different number of serotypes present in Singapore (2014–2016), and separated by lowa and highb risk areas.