Literature detail

Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events.

Katharina C Wollenberg Valero1 Raphael D Isokpehi2 Noah E Douglas2 Seenith Sivasundaram3 Brianna Johnson2 Kiara Wootson3 Ayana McGill2
Affiliations 3 institutions
  1. School of Environmental Sciences, University of Hull, Cottingham Road, Kingston upon Hull, HU67RX, UK. [email protected].
  2. Department of Natural Sciences, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
  3. Department of Mathematics and Physics, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL, USA.
PMID 29134435 2018 Ecohealth eng ppublish
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Article

Publication summary

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.

Climate Climate change Emerging infectious disease Normalized Difference Vegetation Index Phenology Spillover Animals Climate Change Disease Outbreaks Disease Reservoirs Disease Transmission, Infectious Ebolavirus Ecosystem Hemorrhagic Fever, Ebola Humans Seasons

Structured evidence records

Evidence records

2 total
1 records
Extraction confidence 0.80
Key finding

Plant phenology and seasonal ecological conditions in the host ecosystem were identified as major predictors of Ebola virus spillover to humans and animals.

Virus
Host
Not specified
Location
Supporting text

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. Phenology variables proved to be overall better predictors than climate variables.

Method
multiple regression; neural network-based time series regression; data compilation of climate and phenology variables
Geographic raw
Africa
1 records
Extraction confidence 0.95
Key finding

Ebola virus spillover events have occurred from animal reservoirs to humans, and environmental variables can help predict these events.

Virus
Location
Supporting text

We outline a proof of concept that temporal fluctuations of ecological and environmental variables are able to predict such events of Ebola virus spillover to humans and animals.

Method
multiple regression; neural network-based time series regression
Study design
ecological modeling
Transmission direction
animal-to-human
Geographic raw
Africa