Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
PLOS Neglected Tropical Diseases·
- DOI
- 10.1371/journal.pntd.0007629
- PMID
- 31412022
- PMCID
- PMC6693739
- OpenAlex
- W2967778218
- Study type
- Journal article
- Publisher
- Public Library of Science (PLoS)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The risk maps can inform public health authorities about geographic areas where human modification of the environment elevates cutaneous leishmaniasis transmission risk, supporting targeted surveillance and intervention strategies in the Neotropical moist forest biome.
Structured evidence summary
Research question
The study aimed to predict cutaneous leishmaniasis risk in the Neotropical moist forest biome by modeling human case occurrences in relation to anthropogenic, climatic, and environmental factors at two geographic scales.
Study design
Ecological niche modeling was applied using human cutaneous leishmaniasis case data to generate risk maps for the Neotropical moist forest biome and the French Guiana region.
Population and setting
Human cutaneous leishmaniasis cases from the Neotropical moist forest biome, including the Amazonian basin and surrounding forest ecosystems, with focused analysis on the French Guiana region.
Main findings
Risk maps with high statistical support identified areas of significant human environmental impact as the primary drivers of cutaneous leishmaniasis risk, with climatic and ecological factors playing secondary roles. Anthropogenic drivers were highlighted as important for disease risk assessment despite the predominantly sylvatic and peri-urban transmission cycle.
Public-health relevance
The risk maps can inform public health authorities about geographic areas where human modification of the environment elevates cutaneous leishmaniasis transmission risk, supporting targeted surveillance and intervention strategies in the Neotropical moist forest biome.
Important limitations
This summary is limited to the supplied single-article abstract and metadata. Full interpretation of model assumptions, data quality, spatial resolution, temporal coverage, and validation metrics requires access to the complete published paper.
GIDS interpretation
This peer-reviewed journal article contributes ecological niche modeling methods for leishmaniasis risk prediction and highlights anthropogenic environmental factors. It is discoverable through disease, geographic, and thematic classifiers but does not represent a surveillance alert or outbreak report.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 7 auditable classifier relationships to diseases, places, topics, and study design.