Wollenberg Valero Katharina C, Isokpehi Raphael D, Douglas Noah E, Sivasundaram Seenith, Johnson Brianna, Wootson Kiara, McGill Ayana
School of Environmental Sciences, University of Hull, Cottingham Road, Kingston upon Hull, HU67RX, UK.
Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
Ecohealth. 2018 Sep;15(3):497-508. doi: 10.1007/s10393-017-1288-z. Epub 2017 Nov 13.
Ebola virus disease outbreaks in animals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are little explored. Filoviridae viruses have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report, we outline a proof of concept that temporal fluctuations of a set of ecological and environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We compiled a data set of climate and plant phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural network-based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for cost-effective transect surveys to supply phenology data for predictive modelling efforts.
动物(包括人类和大猩猩)身上的埃博拉病毒病疫情始于未知宿主物种的零星宿主转换。导致此类溢出事件的因素鲜有研究。丝状病毒科病毒有广泛的天然宿主,一旦离开宿主就不稳定。因此,涉及病毒颗粒跨物种物理转移的溢出事件可能直接受到宿主生态和环境条件的推动。在本报告中,我们概述了一个概念验证,即描述宿主生态系统动态的一组生态和环境变量的时间波动能够预测埃博拉病毒向人类和动物的此类溢出事件。我们汇编了一个关于气候和植物物候变量以及人类和动物中埃博拉病毒病溢出情况的数据集。我们通过多元回归和基于神经网络的时间序列回归确定了溢出的关键生物和非生物条件。结果证明,物候变量总体上比气候变量更能预测。非洲物候变量尚未作为一个全面的在线资源提供。鉴于物候对于预测未来埃博拉溢出事件可能性的潜在重要性,我们的结果凸显了开展具有成本效益的样带调查以提供用于预测建模工作的物候数据的必要性。