Research Article
Scaling Point-Based Habitat Observations of Anopheles stephensi Using XGBoost to Support Seek-and-Destroy Larval Source Management in Kisumu, Kenya
Odelia Asher*,
Rishil Shah,
Aarya Satardekar,
Anusha Parajuli,
Namit Choudhari,
James Jedrzejczyk,
Joseph Mwangangi,
Charles Mbogo,
Benjamin Jacob
Issue:
Volume 10, Issue 3, September 2026
Pages:
45-61
Received:
17 June 2026
Accepted:
1 July 2026
Published:
6 August 2026
DOI:
10.11648/j.aje.20261003.11
Downloads:
Views:
Abstract: The invasive malaria vector Anopheles stephensi poses an emerging challenge to malaria control in rapidly urbanizing regions of East Africa. This study evaluates the spatial distribution and potential spread of An. stephensi across Kisumu County by integrating field entomological observations with machine learning models applied to satellite-derived environmental data. The primary objective was to scale habitat signatures identified at confirmed georeferenced capture locations to the county level to support a targeted “Seek-and-Destroy” larval source management (S&D-LSM) strategy. Confirmed larval and adult capture points were used to extract environmental predictors from 10-m resolution visible and near-infrared (IR) bands derived from Sentinel-2 imagery. These spectral variables, along with derived indices representing vegetation structure, surface moisture, and urban disturbance, served as input features for predictive modeling. Three ensemble machine learning approaches—Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—were implemented to model the relationship between environmental signatures and observed mosquito presence. Each model was trained using capture locations and background samples to classify potential breeding habitats across the landscape. Model performance was evaluated using cross-validation and standard classification metrics, and the resulting predictions were used to generate habitat suitability maps for the entirety of Kisumu County. Ensemble tree methods effectively captured nonlinear relationships between spectral features and mosquito habitat conditions, enabling the identification of spatial clusters of high-probability breeding environments. XGBoost achieved the highest predictive performance (AUC = 0.96), demonstrating superior ability to quantify complex nonlinear relationships and interactions among environmental and anthropogenic An. stephensi predictors. The eigen-autocorrelation statistics revealed a Moran’s Index of -0.159, a variance of 0.005, a z-score of 0.019 and a p-value of 0.04. Areas of elevated suitability were frequently associated with dense settlement patterns, infrastructure corridors, and locations with intermittent water storage or container habitats. The resulting risk surfaces enabled vector control teams to prioritize surveillance and intervention efforts. By spatially targeting activities toward high-probability sites, the program operationalized an S&D-LSM approach focused on rapid detection and elimination of breeding habitats. This study demonstrates that integrating remote sensing with ensemble machine learning provides a scalable framework for monitoring invasive malaria vectors and supporting targeted urban malaria control interventions.
Abstract: The invasive malaria vector Anopheles stephensi poses an emerging challenge to malaria control in rapidly urbanizing regions of East Africa. This study evaluates the spatial distribution and potential spread of An. stephensi across Kisumu County by integrating field entomological observations with machine learning models applied to satellite-derive...
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