Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events.
Katharina C Wollenberg Valero, Raphael D Isokpehi, Noah E Douglas, Seenith Sivasundaram, Brianna Johnson, Kiara Wootson, Ayana McGill
Author Information
Katharina C Wollenberg Valero: School of Environmental Sciences, University of Hull, Cottingham Road, Kingston upon Hull, HU67RX, UK. k.wollenberg-valero@hull.ac.uk.
Raphael D Isokpehi: Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
Noah E Douglas: Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
Seenith Sivasundaram: Department of Mathematics and Physics, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
Brianna Johnson: Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
Kiara Wootson: Department of Mathematics and Physics, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
Ayana McGill: Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
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.
