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Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11

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Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11. / Métras, Raphaëlle; Jewell, Christopher Parry; Porphyre, Thibaud et al.
In: Scientific Reports, Vol. 5, 9492, 25.03.2015.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Métras, R, Jewell, CP, Porphyre, T, Thompson, P, Pfeiffer, D, Collins, L & White, R 2015, 'Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11', Scientific Reports, vol. 5, 9492. https://doi.org/10.1038/srep09492

APA

Métras, R., Jewell, C. P., Porphyre, T., Thompson, P., Pfeiffer, D., Collins, L., & White, R. (2015). Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11. Scientific Reports, 5, Article 9492. https://doi.org/10.1038/srep09492

Vancouver

Métras R, Jewell CP, Porphyre T, Thompson P, Pfeiffer D, Collins L et al. Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11. Scientific Reports. 2015 Mar 25;5:9492. doi: 10.1038/srep09492

Author

Métras, Raphaëlle ; Jewell, Christopher Parry ; Porphyre, Thibaud et al. / Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11. In: Scientific Reports. 2015 ; Vol. 5.

Bibtex

@article{0f0fd308c06d455088ed382e41e4593c,
title = "Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11",
abstract = "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.",
author = "Rapha{\"e}lle M{\'e}tras and Jewell, {Christopher Parry} and Thibaud Porphyre and Peter Thompson and Dirk Pfeiffer and Lisa Collins and Richard White",
year = "2015",
month = mar,
day = "25",
doi = "10.1038/srep09492",
language = "English",
volume = "5",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Risk factors associated with Rift Valley fever epidemics in South Africa in 2008–11

AU - Métras, Raphaëlle

AU - Jewell, Christopher Parry

AU - Porphyre, Thibaud

AU - Thompson, Peter

AU - Pfeiffer, Dirk

AU - Collins, Lisa

AU - White, Richard

PY - 2015/3/25

Y1 - 2015/3/25

N2 - 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.

AB - 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.

U2 - 10.1038/srep09492

DO - 10.1038/srep09492

M3 - Journal article

VL - 5

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 9492

ER -