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Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11
Rift Valley fever (RVF) is a zoonotic and vector-borne disease, mainly present in Africa, which represents a threat to human health, animal health and production. South Africa has experienced three major RVF epidemics (1950–51, 1973–75 and 2008–11). Due to data scarcity, no previous study has quanti...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372659/ https://www.ncbi.nlm.nih.gov/pubmed/25804974 http://dx.doi.org/10.1038/srep09492 |
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author | Métras, Raphaëlle Jewell, Chris Porphyre, Thibaud Thompson, Peter N. Pfeiffer, Dirk U. Collins, Lisa M. White, Richard G. |
author_facet | Métras, Raphaëlle Jewell, Chris Porphyre, Thibaud Thompson, Peter N. Pfeiffer, Dirk U. Collins, Lisa M. White, Richard G. |
author_sort | Métras, Raphaëlle |
collection | PubMed |
description | Rift Valley fever (RVF) is a zoonotic and vector-borne disease, mainly present in Africa, which represents a threat to human health, animal health and production. South Africa has experienced three major RVF epidemics (1950–51, 1973–75 and 2008–11). Due to data scarcity, no previous study has quantified risk factors associated with RVF epidemics in animals in South Africa. Using the 2008–11 epidemic datasets, a retrospective longitudinal study was conducted to identify and quantify spatial and temporal environmental factors associated with RVF incidence. Cox regressions with a Besag model to account for the spatial effects were fitted to the data. Coefficients were estimated by Bayesian inference using integrated nested Laplace approximation. An increase in vegetation density was the most important risk factor until 2010. In 2010, increased temperature was the major risk factor. In 2011, after the large 2010 epidemic wave, these associations were reversed, potentially confounded by immunity in animals, probably resulting from earlier infection and vaccination. Both vegetation density and temperature should be considered together in the development of risk management strategies. However, the crucial need for improved access to data on population at risk, animal movements and vaccine use is highlighted to improve model predictions. |
format | Online Article Text |
id | pubmed-4372659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-43726592015-04-06 Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 Métras, Raphaëlle Jewell, Chris Porphyre, Thibaud Thompson, Peter N. Pfeiffer, Dirk U. Collins, Lisa M. White, Richard G. Sci Rep Article Rift Valley fever (RVF) is a zoonotic and vector-borne disease, mainly present in Africa, which represents a threat to human health, animal health and production. South Africa has experienced three major RVF epidemics (1950–51, 1973–75 and 2008–11). Due to data scarcity, no previous study has quantified risk factors associated with RVF epidemics in animals in South Africa. Using the 2008–11 epidemic datasets, a retrospective longitudinal study was conducted to identify and quantify spatial and temporal environmental factors associated with RVF incidence. Cox regressions with a Besag model to account for the spatial effects were fitted to the data. Coefficients were estimated by Bayesian inference using integrated nested Laplace approximation. An increase in vegetation density was the most important risk factor until 2010. In 2010, increased temperature was the major risk factor. In 2011, after the large 2010 epidemic wave, these associations were reversed, potentially confounded by immunity in animals, probably resulting from earlier infection and vaccination. Both vegetation density and temperature should be considered together in the development of risk management strategies. However, the crucial need for improved access to data on population at risk, animal movements and vaccine use is highlighted to improve model predictions. Nature Publishing Group 2015-03-25 /pmc/articles/PMC4372659/ /pubmed/25804974 http://dx.doi.org/10.1038/srep09492 Text en Copyright © 2015, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Métras, Raphaëlle Jewell, Chris Porphyre, Thibaud Thompson, Peter N. Pfeiffer, Dirk U. Collins, Lisa M. White, Richard G. Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 |
title | Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 |
title_full | Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 |
title_fullStr | Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 |
title_full_unstemmed | Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 |
title_short | Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11 |
title_sort | risk factors associated with rift valley fever epidemics in south africa in 2008–11 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372659/ https://www.ncbi.nlm.nih.gov/pubmed/25804974 http://dx.doi.org/10.1038/srep09492 |
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