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Rift Valley Fever Risk Map Model and Seroprevalence in Selected Wild Ungulates and Camels from Kenya

Overview of attention for article published in PLOS ONE, June 2013
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Title
Rift Valley Fever Risk Map Model and Seroprevalence in Selected Wild Ungulates and Camels from Kenya
Published in
PLOS ONE, June 2013
DOI 10.1371/journal.pone.0066626
Pubmed ID
Authors

Seth C. Britch, Yatinder S. Binepal, Mark G. Ruder, Henry M. Kariithi, Kenneth J. Linthicum, Assaf Anyamba, Jennifer L. Small, Compton J. Tucker, Leonard O. Ateya, Abuu A. Oriko, Stephen Gacheru, William C. Wilson

Abstract

Since the first isolation of Rift Valley fever virus (RVFV) in the 1930s, there have been multiple epizootics and epidemics in animals and humans in sub-Saharan Africa. Prospective climate-based models have recently been developed that flag areas at risk of RVFV transmission in endemic regions based on key environmental indicators that precede Rift Valley fever (RVF) epizootics and epidemics. Although the timing and locations of human case data from the 2006-2007 RVF outbreak in Kenya have been compared to risk zones flagged by the model, seroprevalence of RVF antibodies in wildlife has not yet been analyzed in light of temporal and spatial predictions of RVF activity. Primarily wild ungulate serum samples from periods before, during, and after the 2006-2007 RVF epizootic were analyzed for the presence of RVFV IgM and/or IgG antibody. Results show an increase in RVF seropositivity from samples collected in 2007 (31.8%), compared to antibody prevalence observed from 2000-2006 (3.3%). After the epizootic, average RVF seropositivity diminished to 5% in samples collected from 2008-2009. Overlaying maps of modeled RVF risk assessments with sampling locations indicated positive RVF serology in several species of wild ungulate in or near areas flagged as being at risk for RVF. Our results establish the need to continue and expand sero-surveillance of wildlife species Kenya and elsewhere in the Horn of Africa to further calibrate and improve the RVF risk model, and better understand the dynamics of RVFV transmission.

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Mendeley readers

The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Kenya 2 1%
United Kingdom 1 <1%
Germany 1 <1%
Canada 1 <1%
Unknown 154 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 34 21%
Researcher 28 18%
Student > Ph. D. Student 26 16%
Student > Doctoral Student 8 5%
Student > Bachelor 8 5%
Other 24 15%
Unknown 31 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 25%
Veterinary Science and Veterinary Medicine 26 16%
Medicine and Dentistry 18 11%
Immunology and Microbiology 7 4%
Biochemistry, Genetics and Molecular Biology 7 4%
Other 25 16%
Unknown 36 23%