Local drivers of Rift Valley fever outbreaks in Mauritania: A one health approach combining ecological, vector, host and livestock movement data
PLOS Neglected Tropical Diseases·
- DOI
- 10.1371/journal.pntd.0013553
- PMID
- 41026792
- PMCID
- PMC12510638
- OpenAlex
- W4414650814
- Study type
- Journal article
- Publisher
- Public Library of Science (PLoS)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The findings can inform targeted active surveillance and early warning systems for RVFV infections in Mauritania, enabling timely control measures to prevent outbreaks and reduce human and animal losses.
Structured evidence summary
Research question
The study aimed to identify drivers and ecological suitability for Rift Valley fever virus infections and to map areas at risk for outbreaks in humans and animals in Mauritania.
Study design
A high-resolution spatiotemporal modeling study using geolocated data from 2019 to 2023. The analysis incorporated confirmed human cases (viral RNA detection), animal cases (serology or viral RNA), mosquito samples with detected virus, and negative results as background data to contrast environments with and without cases.
Population and setting
The study covered Mauritania, focusing on human populations, livestock, and mosquito vectors across different geographic regions of the country from 2019 to 2023.
Main findings
Precipitation in the current and preceding month, along with average daily temperature of the current month, were the main drivers of RVFV infection. August through October were the most favorable months for infection, with highest outbreak potential in southern and western regions during the wet season. Some northern areas showed elevated outbreak potential year-round despite overall reduction during dry seasons.
Public-health relevance
The findings can inform targeted active surveillance and early warning systems for RVFV infections in Mauritania, enabling timely control measures to prevent outbreaks and reduce human and animal losses.
Important limitations
This summary relies on the supplied single-article abstract and metadata. Full assessment of study limitations, including model assumptions, data quality, validation methods, and generalizability, requires review of the complete published paper.
GIDS interpretation
This article is discoverable through classifier links for Rift Valley fever, Mauritania, One Health approaches, surveillance systems, outbreak investigation, transmission dynamics, and climate-environment factors. These links provide context for literature retrieval but do not indicate an active disease 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 9 auditable classifier relationships to diseases, places, topics, and study design.