Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches

Abstract

Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.

Figure 1.

Study sites (left) and distribution of observed aquatic habitats (2003–2018) and pseudohabitats in the four study sites in western Kenya (right).

Figure 2.

Flowchart of the modeling and habitat prediction process. GBM = gradient-boosted machine; kNN = kth nearest neighbor tree; Logistic = logistic regression; SVM = support vector machine; XGB = extreme gradient boosted.

Figure 3.

Accuracy of predictions of different models. (A) Prediction of aquatic habitats by different models. (B) Prediction of larva-positive habitats by different models. (C) Ensembled models for the prediction of aquatic habitats. (D) Ensembled model for the prediction of larva-positive habitats.

Figure 4.

Predicted probability (top panels) and mean squared error (MSE) (bottom panels) of aquatic habitats (left panels) and Anopheles larva-positive habitats (right panels).