Drone-based geospatial prediction modeling identifies Fasciola hepatica infection risk in the Cusco Highlands of Peru
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.
Authors
- Melinda B. Tanabe (ORCID: https://orcid.org/0000-0003-3287-3327)
- Miguel M. Cabada (ORCID: https://orcid.org/0000-0003-2545-6197)
- Rodrigo A. Ore (ORCID: https://orcid.org/0000-0003-0416-3918)
- Bryan Fernandez-Camacho (ORCID: https://orcid.org/0009-0005-8573-2060)
- Jose L. Alccacontor-Munoz
- Antony Barja
- Maria L. Morales
- Luis C. Revilla
Institutions
- Universidad Peruana Cayetano Heredia (PE)
- The University of Texas Medical Branch at Galveston (US)
Publication Details
- Journal
- Infectious Diseases of Poverty
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1186/s40249-026-01420-1
- Primary Topic
- Helminth infection and control
- Type
- article
- Field-Weighted Citation Impact
- 0.00