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Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events
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. S...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245028/ https://www.ncbi.nlm.nih.gov/pubmed/29134435 http://dx.doi.org/10.1007/s10393-017-1288-z |
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author | Wollenberg Valero, Katharina C. Isokpehi, Raphael D. Douglas, Noah E. Sivasundaram, Seenith Johnson, Brianna Wootson, Kiara McGill, Ayana |
author_facet | Wollenberg Valero, Katharina C. Isokpehi, Raphael D. Douglas, Noah E. Sivasundaram, Seenith Johnson, Brianna Wootson, Kiara McGill, Ayana |
author_sort | Wollenberg Valero, Katharina C. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10393-017-1288-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6245028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-62450282018-12-04 Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events Wollenberg Valero, Katharina C. Isokpehi, Raphael D. Douglas, Noah E. Sivasundaram, Seenith Johnson, Brianna Wootson, Kiara McGill, Ayana Ecohealth Original Contribution 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10393-017-1288-z) contains supplementary material, which is available to authorized users. Springer US 2017-11-13 2018 /pmc/articles/PMC6245028/ /pubmed/29134435 http://dx.doi.org/10.1007/s10393-017-1288-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Contribution Wollenberg Valero, Katharina C. Isokpehi, Raphael D. Douglas, Noah E. Sivasundaram, Seenith Johnson, Brianna Wootson, Kiara McGill, Ayana Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events |
title | Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events |
title_full | Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events |
title_fullStr | Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events |
title_full_unstemmed | Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events |
title_short | Plant Phenology Supports the Multi-emergence Hypothesis for Ebola Spillover Events |
title_sort | plant phenology supports the multi-emergence hypothesis for ebola spillover events |
topic | Original Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245028/ https://www.ncbi.nlm.nih.gov/pubmed/29134435 http://dx.doi.org/10.1007/s10393-017-1288-z |
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