ArticlePLoS neglected tropical diseases2017
Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach.
Article in PLoS neglected tropical diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Mechanistic models of Rift Valley fever virus transmission: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Article
- The Temperature-Associated Effects of Rift Valley Fever Virus Infections in Mosquitoes and Climate-Driven Epidemics: A Review.Viruses · 2025Review
- The socioeconomic impacts of Rift Valley fever: A rapid review.PLoS neglected tropical diseases · 2024Review
- Rift Valley fever in West Africa: A zoonotic disease with multiple socio-economic consequences.One health (Amsterdam, Netherlands) · 2023Review
- Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information.Frontiers in veterinary science · 2023Article
- Modelling the persistence and control of Rift Valley fever virus in a spatially heterogeneous landscape.Nature communications · 2021Article
- The role of livestock movements in the spread of Rift Valley fever virus in animals and humans in Mayotte, 2018-19.PLoS neglected tropical diseases · 2021Article
- Farm-Level Risk Factors of Increased Abortion and Mortality in Domestic Ruminants during the 2010 Rift Valley Fever Outbreak in Central South Africa.Pathogens (Basel, Switzerland) · 2020Article
- Rift Valley Fever - assessment of effectiveness of surveillance and control measures in the EU.EFSA journal. European Food Safety Authority · 2020Article
- Estimation of Rift Valley fever virus spillover to humans during the Mayotte 2018-2019 epidemic.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Rift Valley fever in northern Senegal: A modelling approach to analyse the processes underlying virus circulation recurrence.PLoS neglected tropical diseases · 2020Article
- Rift Valley Fever: risk of persistence, spread and impact in Mayotte (France).EFSA journal. European Food Safety Authority · 2020Article
- Review
- Culicoides Latreille in the sun: faunistic inventory of Culicoides species (Diptera: Ceratopogonidae) in Mayotte (Comoros Archipelago, Indian Ocean).Parasites & vectors · 2019Article
- Livestock trade network: potential for disease transmission and implications for risk-based surveillance on the island of Mayotte.Scientific reports · 2018Article
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
Rift Valley fever (RVF) is a major zoonotic and arboviral hemorrhagic fever. The conditions leading to RVF epidemics are still unclear, and the relative role of climatic and anthropogenic factors may vary between ecosystems. Here, we estimate the most likely scenario that led to RVF emergence on the island of Mayotte, following the 2006-2007 African epidemic. We developed the first mathematical model for RVF that accounts for climate, animal imports and livestock susceptibility, which is fitted to a 12-years dataset. RVF emergence was found to be triggered by the import of infectious animals, whilst transmissibility was approximated as a linear or exponential function of vegetation density. Model forecasts indicated a very low probability of virus endemicity in 2017, and therefore of re-emergence in a closed system (i.e. without import of infected animals). However, the very high proportion of naive animals reached in 2016 implies that the island remains vulnerable to the import of infectious animals. We recommend reinforcing surveillance in livestock, should RVF be reported is neighbouring territories. Our model should be tested elsewhere, with ecosystem-specific data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.