Ochieng Alfred O, Nanyingi Mark, Kipruto Edwin, Ondiba Isabella M, Amimo Fred A, Oludhe Christopher, Olago Daniel O, Nyamongo Isaac K, Estambale Benson B A
Department of Biological Sciences, Jaramogi Oginga Odinga University of Science and Technology, Bondo, Kenya.
Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, USA.
Infect Ecol Epidemiol. 2016 Nov 17;6:32322. doi: 10.3402/iee.v6.32322. eCollection 2016.
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).
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.
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.
Model results predicted potential suitable areas with high success rates for , and . 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 and . Model performance was statistically significant.
Soil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species.
裂谷热（RVF）是一种媒介传播的人畜共患病，对人类健康和动物生产力有影响。在此，我们探索使用媒介存在模型来预测气候变化情景下裂谷热媒介物种的分布，以证明裂谷热病毒（RVFV）地理传播的可能性。
评估气候变化对肯尼亚巴林戈县裂谷热媒介分布的影响，旨在绘制裂谷热疫情空间预测的风险地图。
该研究使用媒介存在数据和生态位建模（MaxEnt）算法来预测气候变化对巴林戈县裂谷热媒介栖息地适宜性和空间分布的影响。物种出现的数据来自研究区域内成年蚊子和幼虫的纵向采样。我们使用当前（2000年）和未来（2050年）的生物气候数据库对媒介分布进行建模。
模型结果预测了[具体物种1]、[具体物种2]和[具体物种3]潜在适宜区域的成功率很高。在当前气候条件下，低地被发现非常适合所有物种。未来气候条件表明[具体物种1]和[具体物种2]的空间分布会增加。模型性能具有统计学意义。
土壤类型、最干燥季度的降水量、降水季节性和等温性对这四个物种显示出最高的预测潜力。
