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Wetlands, wild Bovidae species richness and sheep density delineate risk of Rift Valley fever outbreaks in the African continent and Arabian Peninsula.

Abstract Rift Valley fever (RVF) is an emerging, vector-borne viral zoonosis that has significantly impacted public health, livestock health and production, and food security over the last three decades across large regions of the African continent and the Arabian Peninsula. The potential for expansion of RVF outbreaks within and beyond the range of previous occurrence is unknown. Despite many large national and international epidemics, the landscape epidemiology of RVF remains obscure, particularly with respect to the ecological roles of wildlife reservoirs and surface water features. The current investigation modeled RVF risk throughout Africa and the Arabian Peninsula as a function of a suite of biotic and abiotic landscape features using machine learning methods. Intermittent wetland, wild Bovidae species richness and sheep density were associated with increased landscape suitability to RVF outbreaks. These results suggest the role of wildlife hosts and distinct hydrogeographic landscapes in RVF virus circulation and subsequent outbreaks may be underestimated. These results await validation by studies employing a deeper, field-based interrogation of potential wildlife hosts within high risk taxa.
PMID
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Authors

Mayor MeshTerms

Disease Outbreaks

Wetlands

Keywords
Journal Title plos neglected tropical diseases
Publication Year Start




PMID- 28742814
OWN - NLM
STAT- MEDLINE
DA  - 20170725
DCOM- 20170731
LR  - 20170731
IS  - 1935-2735 (Electronic)
IS  - 1935-2727 (Linking)
VI  - 11
IP  - 7
DP  - 2017 Jul
TI  - Wetlands, wild Bovidae species richness and sheep density delineate risk of Rift 
      Valley fever outbreaks in the African continent and Arabian Peninsula.
PG  - e0005756
LID - 10.1371/journal.pntd.0005756 [doi]
AB  - Rift Valley fever (RVF) is an emerging, vector-borne viral zoonosis that has
      significantly impacted public health, livestock health and production, and food
      security over the last three decades across large regions of the African
      continent and the Arabian Peninsula. The potential for expansion of RVF outbreaks
      within and beyond the range of previous occurrence is unknown. Despite many large
      national and international epidemics, the landscape epidemiology of RVF remains
      obscure, particularly with respect to the ecological roles of wildlife reservoirs
      and surface water features. The current investigation modeled RVF risk throughout
      Africa and the Arabian Peninsula as a function of a suite of biotic and abiotic
      landscape features using machine learning methods. Intermittent wetland, wild
      Bovidae species richness and sheep density were associated with increased
      landscape suitability to RVF outbreaks. These results suggest the role of
      wildlife hosts and distinct hydrogeographic landscapes in RVF virus circulation
      and subsequent outbreaks may be underestimated. These results await validation by
      studies employing a deeper, field-based interrogation of potential wildlife hosts
      within high risk taxa.
FAU - Walsh, Michael G
AU  - Walsh MG
AUID- ORCID: http://orcid.org/0000-0002-6441-3635
AD  - Marie Bashir Institute for Infectious Diseases and Biosecurity, University of
      Sydney, Westmead, New South Wales, Australia.
AD  - Westmead Institute for Medical Research, University of Sydney, Westmead, New
      South Wales, Australia.
FAU - Willem de Smalen, Allard
AU  - Willem de Smalen A
AD  - School of Public Health, University of Sydney, Camperdown, New South Wales,
      Australia.
FAU - Mor, Siobhan M
AU  - Mor SM
AD  - Marie Bashir Institute for Infectious Diseases and Biosecurity, University of
      Sydney, Westmead, New South Wales, Australia.
AD  - School of Veterinary Science, Faculty of Science, University of Sydney,
      Camperdown, New South Wales, Australia.
LA  - eng
PT  - Journal Article
DEP - 20170725
PL  - United States
TA  - PLoS Negl Trop Dis
JT  - PLoS neglected tropical diseases
JID - 101291488
SB  - IM
MH  - Africa/epidemiology
MH  - Animals
MH  - Arabia/epidemiology
MH  - *Disease Outbreaks
MH  - Humans
MH  - Machine Learning
MH  - Population Density
MH  - Rift Valley Fever/*epidemiology
MH  - Risk Assessment
MH  - Ruminants/*growth & development
MH  - Spatial Analysis
MH  - *Wetlands
EDAT- 2017/07/26 06:00
MHDA- 2017/08/02 06:00
CRDT- 2017/07/26 06:00
PHST- 2016/12/07 [received]
PHST- 2017/06/29 [accepted]
AID - 10.1371/journal.pntd.0005756 [doi]
AID - PNTD-D-16-02210 [pii]
PST - epublish
SO  - PLoS Negl Trop Dis. 2017 Jul 25;11(7):e0005756. doi:
      10.1371/journal.pntd.0005756. eCollection 2017 Jul.