Drone-based geospatial prediction modeling identifies Fasciola hepatica infection risk in the Cusco Highlands of Peru

Fascioliasis is a neglected infectious disease affecting agricultural communities worldwide, with the Peruvian Andes among the most severely affected regions. Identifying fine-scale environmental risk patterns could support targeted surveillance and control. We aimed to develop predictive models of Fasciola hepatica infection in humans and sheep using drone-derived environmental indices in a rural Andean community.

We conducted a cross-sectional study in the Huayllapata community, Cusco, Peru. Demographic, socioeconomic, and georeferenced infection data were collected from households and livestock with fascioliasis diagnosed by stool microscopy. High-resolution multispectral and thermal drone surveys were performed in April 2023 to derive environmental, topographic, and climatic indices. Logistic regression, random forest (RF), XGBoost (XGB), and deep learning models were trained using literature-based or principal component analysis (PCA)-based variable selection strategies. Model performance was evaluated using standard and spatial cross validation approaches. Fine-scale probability surface maps were generated across the study area.

Human fascioliasis prevalence was 21.3% of households, while sheep prevalence reached 80%. Under standard cross validation, RF achieved the best performance for human infection using the literature-based approach (accuracy = 0.89, sensitivity = 0.99, specificity = 0.88), while XGB performed best using the PCA-based approach (accuracy = 0.85, sensitivity = 0.75, specificity = 0.85). For sheep infection, XGB achieved the highest performance (accuracy = 0.93, sensitivity = 0.65, specificity = 0.93) with literature-based variables and RF performed best under the PCA-based approach (accuracy = 0.85, sensitivity = 0.75, specificity = 0.86). Spatial cross-validation reduced accuracy and specificity across models but preserved high sensitivity. Probability maps revealed marked spatial heterogeneity in predicted risk within the community, with shifts in the location and magnitude of risk zones when spatial dependence was accounted for.

In this single Andean community, machine learning models integrating drone-derived environmental, topographic and climatic indices, successfully identified F. hepatica infection occurrence in humans and sheep. RF and XGB showed the most robust performance under spatial cross-validation, supporting the feasibility of UAV-based approaches for localized F. hepatica risk mapping.