Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale
PLOS Neglected Tropical Diseases·
- DOI
- 10.1371/journal.pntd.0009683
- PMID
- 34424896
- PMCID
- PMC8425568
- OpenAlex
- W3196281824
- Study type
- Journal article
- Publisher
- Public Library of Science (PLoS)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The authors frame GIS-MCE suitability maps as an updatable tool to support surveillance and prevention of future Ebola outbreaks as new knowledge becomes available.
Structured evidence summary
Research question
The study examines where environmental, climatic, and anthropogenic conditions may be suitable for Ebola virus spillover to humans at a landscape scale across regions with prior emergence.
Study design
A knowledge-driven Geographic Information System-based multicriteria evaluation (GIS-MCE) was applied, combining literature-derived risk factors into geographical data layers to produce suitability maps, with sensitivity analysis to test robustness.
Population and setting
Guinea, the Republic of the Congo, and Gabon were the focus regions, selected as areas where Ebola viruses had previously emerged.
Main findings
Suitability maps displayed marked spatial and temporal variability at fine regional scales. Reported spillover events were located in areas of intermediate to high suitability, and sensitivity analysis indicated the maps were robust.
Public-health relevance
The authors frame GIS-MCE suitability maps as an updatable tool to support surveillance and prevention of future Ebola outbreaks as new knowledge becomes available.
Important limitations
The authors explicitly note important remaining gaps in knowledge about factors associated with Ebola virus spillover risk. Additionally, this summary is limited to the supplied single-article abstract/metadata and requires the original paper for decision-grade interpretation.
GIDS interpretation
The work provides a landscape-scale suitability mapping approach that could complement Ebola-related discoverability efforts, but the abstract does not connect the model output to any active surveillance signal.
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 10 auditable classifier relationships to diseases, places, topics, and study design.