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Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya

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dc.contributor.author Ochieng, Alfred O.
dc.contributor.author Nanyingi, Mark
dc.contributor.author Kipruto, Edwin
dc.contributor.author Ondiba, Isabella M.
dc.contributor.author Amimo, Fred A.
dc.contributor.author Oludhe, Christopher
dc.contributor.author Olago, Daniel O.
dc.contributor.author Nyamongo, Isaac K.
dc.contributor.author Estambale, Benson B. A.
dc.date.accessioned 2017-11-06T08:33:49Z
dc.date.available 2017-11-06T08:33:49Z
dc.date.issued 2016-11-17
dc.identifier.citation Infection Ecology and Epidemiology 2016, en_US
dc.identifier.issn 2000-8686
dc.identifier.uri http://hdl.handle.net/123456789/235
dc.description.abstract Background: Rift Valley fever (RVF) is a vector-borne zoonotic disease that has an impact on human health and animal productivity. Here, we explore the use of vector presence modelling to predict the distribution of RVF vector species under climate change scenario to demonstrate the potential for geographic spread of Rift Valley fever virus (RVFV). Objectives: To evaluate the effect of climate change on RVF vector distribution in Baringo County, Kenya, with an aim of developing a risk map for spatial prediction of RVF outbreaks. Methodology: The study used data on vector presence and ecological niche modelling (MaxEnt) algorithm to predict the effect of climatic change on habitat suitability and the spatial distribution of RVF vectors in Baringo County. Data on species occurrence were obtained from longitudinal sampling of adult mosquitoes and larvae in the study area. We used present (2000) and future (2050) Bioclim climate databases to model the vector distribution. Results: Model results predicted potential suitable areas with high success rates for Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis. Under the present climatic conditions, the lowlands were found to be highly suitable for all the species. Future climatic conditions indicate an increase in the spatial distribution of Cx. quinquefasciatus and M. africana. Model performance was statistically significant. Conclusion: Soil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species. en_US
dc.language.iso en en_US
dc.publisher Taylor and Francis en_US
dc.relation.ispartofseries Infection Ecology & Epidemiology;
dc.subject Rift Valley fever; ecological niche modelling; climate change; Baringo County en_US
dc.title Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya en_US
dc.type Article en_US

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