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Title: Rift Valley fever risk map model and seroprevalence in selected wild ungulates and camels from Kenya

item Gibson, Seth
item BINEPAL, YATINDER - Kenya Agricultural Research Institute
item Ruder, Mark
item KARIITHI, HENRY - Kenya Agricultural Research Institute
item Linthicum, Kenneth - Ken
item ANYAMBA, ASSAPH - National Aeronautics And Space Administration (NASA)
item SMALL, J - National Aeronautics And Space Administration (NASA)
item TUCKER, C - National Aeronautics And Space Administration (NASA)
item ATEYA, LEONARD - Kenya Agricultural Research Institute
item ORIKO, ABUU - Kenya Agricultural Research Institute
item GACHERU, SIMON - Kenya Agricultural Research Institute
item Wilson, William

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2013
Publication Date: 6/28/2013
Citation: Britch, S.C., Binepal, Y.S., Ruder, M.G., Kariithi, H.M., Linthicum, K., Anyamba, A., Small, J.L., Tucker, C.J., Ateya, L.O., Oriko, A.A., Gacheru, S., Wilson, W.C. 2013. Rift Valley fever risk map model and seroprevalence in selected wild ungulates and camels from Kenya. PLoS One. 8(6):e66626. DOI: 10.1371/journal.pone.0066626.

Interpretive Summary: Rift Valley Fever (RVF) is an acute viral zoonosis endemic in Africa and Arabian Peninsula. The disease is caused by the mosquito-transmitted arbovirus, Rift Valley fever virus (RVFV) in the family Bunyaviridae. The virus was first identified in 1931 during investigations into the cause of sudden abortions on a farm in the Rift Valley province of Kenya. Since then, epidemics have been repeatedly reported (with outbreak intervals of 3-15 years) in much of sub-Saharan and North Africa as well as oubreaks outside of Africa (Saudi Arabia and Yemen). Humans infected by RVFV develop influenza-like symptoms ranging from fever to fatal encephalitis and hemorrhages. Domestic and wild animals show abortions (pregnant animals) and high mortality rates (young animals). Forecasting models and early warning systems can predict climatic conditions that are frequently associated with RVF outbreaks, which may eventually be used to implement effective and timely disease control measures. Previous studies have demonstrated that wild animals may play central roles in RVF outbreaks. With this in mind, we sought to examine temporal and spatial change patterns in RVF sero-prevalence in Kenyan wildlife and determine if there is a relationship between predicted RVF risk and wildlife sero-prevalence with the hope that the data presented will be useful in developing spatial models predicting high risk of exposure to RVFV in sub-Sahara Africa.

Technical 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.