RT Journal Article
SR Electronic
T1 Phenology supports the eco-environmental hypothesis for Ebola spillover events
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 158568
DO 10.1101/158568
A1 Katharina C. Wollenberg Valero
A1 Raphael D. Isokpehi
A1 Noah E. Douglas
A1 Seenith Sivasundaram
A1 Brianna Johnson
A1 Kiara Wootson
A1 Ayana McGill
YR 2017
UL http://biorxiv.org/content/early/2017/07/05/158568.abstract
AB Ebola virus disease outbreaks in mammals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are not clearly understood. Filoviridae 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 eco-environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We newly compiled a dataset of climate and 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 networks 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 more phenology monitoring to supply data for predictive modelling efforts.Summary Since the 1970s, the Ebola virus recurrently emerges from the Afrotropics. As viruses cannot survive outside their host species, the transfer from species that naturally harbor it to other susceptible species via so-called spillover events might involve the host’s ecosystem. Consequently, properties of the ecosystem could be used to predict future spillovers. In this paper, we provide a proof of concept for the idea that plant characteristics such as flowering, fruiting and greening can be used for predicting spillover events. If confirmed, this might serve as a future cost-effective method to locally monitor conditions favorable for spillovers.