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Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach.

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.
PMID
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Authors

Mayor MeshTerms
Keywords
Journal Title plos neglected tropical diseases
Publication Year Start




PMID- 28732006
OWN - NLM
STAT- Publisher
DA  - 20170721
LR  - 20170721
IS  - 1935-2735 (Electronic)
IS  - 1935-2727 (Linking)
VI  - 11
IP  - 7
DP  - 2017 Jul 21
TI  - Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling
      approach.
PG  - e0005767
LID - 10.1371/journal.pntd.0005767 [doi]
AB  - 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.
FAU - Metras, Raphaelle
AU  - Metras R
AUID- ORCID: http://orcid.org/0000-0002-2646-196X
AD  - Centre for the Mathematical Modelling of Infectious Diseases, Department of
      Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine,
      London, United Kingdom.
FAU - Fournie, Guillaume
AU  - Fournie G
AD  - Veterinary Epidemiology, Economics and Public Health group, Department of
      Pathobiology and Population Sciences, The Royal Veterinary College, Hatfield,
      United Kingdom.
FAU - Dommergues, Laure
AU  - Dommergues L
AD  - GDS Mayotte-Cooperative Agricole des Eleveurs Mahorais, Coconi, Mayotte, France.
FAU - Camacho, Anton
AU  - Camacho A
AD  - Centre for the Mathematical Modelling of Infectious Diseases, Department of
      Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine,
      London, United Kingdom.
AD  - Epicentre, Paris, France.
FAU - Cavalerie, Lisa
AU  - Cavalerie L
AD  - Centre de cooperation internationale en recherche agronomique pour le
      developpement (CIRAD) UMR ASTRE, Cyroi platform, Sainte Clotilde, La Reunion,
      France.
AD  - Institut National de Recherche Agronomique (INRA) UMR 1309 ASTRE, Montpellier,
      France.
AD  - Bureau de la Sante Animale, Direction Generale de l'Alimentation, Paris, France.
AD  - Universite de La Reunion, Saint Denis, France.
FAU - Merot, Philippe
AU  - Merot P
AD  - Direction de l'Alimentation, de l'Agriculture et de la Foret de Mayotte,
      Mamoudzou, France.
FAU - Keeling, Matt J
AU  - Keeling MJ
AD  - WIDER, Warwick University, Coventry, United Kingdom.
AD  - Life Sciences, Warwick University, Coventry, United Kingdom.
AD  - Mathematics Institute, Warwick University, Coventry, United Kingdom.
FAU - Cetre-Sossah, Catherine
AU  - Cetre-Sossah C
AD  - Centre de cooperation internationale en recherche agronomique pour le
      developpement (CIRAD) UMR ASTRE, Cyroi platform, Sainte Clotilde, La Reunion,
      France.
AD  - Institut National de Recherche Agronomique (INRA) UMR 1309 ASTRE, Montpellier,
      France.
FAU - Cardinale, Eric
AU  - Cardinale E
AD  - Centre de cooperation internationale en recherche agronomique pour le
      developpement (CIRAD) UMR ASTRE, Cyroi platform, Sainte Clotilde, La Reunion,
      France.
AD  - Institut National de Recherche Agronomique (INRA) UMR 1309 ASTRE, Montpellier,
      France.
FAU - Edmunds, W John
AU  - Edmunds WJ
AD  - Centre for the Mathematical Modelling of Infectious Diseases, Department of
      Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine,
      London, United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20170721
PL  - United States
TA  - PLoS Negl Trop Dis
JT  - PLoS neglected tropical diseases
JID - 101291488
EDAT- 2017/07/22 06:00
MHDA- 2017/07/22 06:00
CRDT- 2017/07/22 06:00
PHST- 2017/03/02 [received]
PHST- 2017/06/30 [accepted]
AID - 10.1371/journal.pntd.0005767 [doi]
AID - PNTD-D-17-00312 [pii]
PST - aheadofprint
SO  - PLoS Negl Trop Dis. 2017 Jul 21;11(7):e0005767. doi:
      10.1371/journal.pntd.0005767.