Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach
The study highlights the significant influence of environmental factors on the seasonal spread of Leptospira, particularly in tropical and subtropical regions. These findings are crucial for public health planning, providing insights for targeted policies to reduce leptospirosis, while advanced machine learning models serve as a robust tool for improving disease surveillance, and risk assessment, which ultimately supports the development of an early warning system.
Authors
- Cyrille Goarant (ORCID: https://orcid.org/0000-0002-0690-9464)
- Nazha Selmaoui‐Folcher (ORCID: https://orcid.org/0000-0003-1667-3819)
- Christine Laporte‐Magoni (ORCID: https://orcid.org/0000-0002-9286-7503)
- Philippe Fournier‐Viger (ORCID: https://orcid.org/0000-0002-7680-9899)
- Romane Scherrer (ORCID: https://orcid.org/0000-0002-3020-6427)
- Rodrigue Govan (ORCID: https://orcid.org/0000-0002-4087-7056)
- Roman Thibeaux
- Pierre Genthon
- Baptiste Fougeron
Institutions
- University of New Caledonia (NC)
- Centre National de la Recherche Scientifique (FR)
- Pacific Community (NC)
- Shenzhen University (CN)
- Laboratoire HydroSciences Montpellier (FR)
- Institute of Software (CN)
- Institut Pasteur de Nouvelle Calédonie (NC)
- Institut de Recherche pour le Développement (FR)
Publication Details
- Journal
- PLoS neglected tropical diseases
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1371/journal.pntd.0012755
- Citations
- 4
- Primary Topic
- Leptospirosis research and findings
- Type
- article
- Field-Weighted Citation Impact
- 8.19
Funders
- Institut de Recherche pour le Développement
- Agence Nationale de la Recherche