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

Métras R, Jewell C, Porphyre T, Thompson PN, Pfeiffer DU, Collins LM, White RG - Sci Rep (2015)

Bottom Line: Cox regressions with a Besag model to account for the spatial effects were fitted to the data.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.However, the crucial need for improved access to data on population at risk, animal movements and vaccine use is highlighted to improve model predictions.

View Article: PubMed Central - PubMed

Affiliation: 1] Veterinary Epidemiology, Economics and Public Health Group, Department of Production and Population Health, Royal Veterinary College, Hatfield, United Kingdom [2] Centre for the Mathematical Modelling of Infectious Diseases and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

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.

No MeSH data available.


Related in: MedlinePlus

Rift Valley fever 2008–11 epidemics in South Africa.(A) Epidemic curve (daily number of RVF affected farms). Risk maps of the fitted hazard ratio for the (B) 2008, (C) first 2009, (D) second 2009, (E) 2010 and (F) 2011 outbreaks. For each outbreak wave, dots represent the location of affected farms, and crosses the location of previously affected farms. These maps were created using the software ArcGIS version 10.1.
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f1: Rift Valley fever 2008–11 epidemics in South Africa.(A) Epidemic curve (daily number of RVF affected farms). Risk maps of the fitted hazard ratio for the (B) 2008, (C) first 2009, (D) second 2009, (E) 2010 and (F) 2011 outbreaks. For each outbreak wave, dots represent the location of affected farms, and crosses the location of previously affected farms. These maps were created using the software ArcGIS version 10.1.

Mentions: Between 2008 and 2011, South Africa experienced five RVF outbreak waves: one in 2008, two in 2009, one in 2010 and one in 2011 (Figure 1A). The 2008 and 2009 waves were small (24, 20 and 19 affected farms were reported, respectively) and spatially contained, whereas the 2010 and 2011 waves resulted in 471 and 124 affected farms, respectively, together affecting almost the whole country (Figure 1B–F). In a previous study3, three of these five outbreak waves (first 2009 wave, 2010 and 2011) exhibited space-time interaction, which was interpreted as an indication of underlying short and long distance transmission mechanisms. Further research is required to explain the variation in the relative importance of different spatio-temporal transmission processes during those epidemics. Only few studies or field observations have been published on these epidemics and are summarized hereinafter. In 2008, cases were preceded by a period of heavy rainfall4, but like other outbreaks in the eastern part of the country, did not develop into a large epidemic. Little published information is available on the 2009 waves, and no unusual environmental conditions were found to be related to the second 2009 wave, although it occurred in an area where flood irrigation was practiced5. In 2010 and 2011, RVF epidemics followed periods of heavy rains and localized flooding, and the virus spread almost throughout the whole country.


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

Métras R, Jewell C, Porphyre T, Thompson PN, Pfeiffer DU, Collins LM, White RG - Sci Rep (2015)

Rift Valley fever 2008–11 epidemics in South Africa.(A) Epidemic curve (daily number of RVF affected farms). Risk maps of the fitted hazard ratio for the (B) 2008, (C) first 2009, (D) second 2009, (E) 2010 and (F) 2011 outbreaks. For each outbreak wave, dots represent the location of affected farms, and crosses the location of previously affected farms. These maps were created using the software ArcGIS version 10.1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4372659&req=5

f1: Rift Valley fever 2008–11 epidemics in South Africa.(A) Epidemic curve (daily number of RVF affected farms). Risk maps of the fitted hazard ratio for the (B) 2008, (C) first 2009, (D) second 2009, (E) 2010 and (F) 2011 outbreaks. For each outbreak wave, dots represent the location of affected farms, and crosses the location of previously affected farms. These maps were created using the software ArcGIS version 10.1.
Mentions: Between 2008 and 2011, South Africa experienced five RVF outbreak waves: one in 2008, two in 2009, one in 2010 and one in 2011 (Figure 1A). The 2008 and 2009 waves were small (24, 20 and 19 affected farms were reported, respectively) and spatially contained, whereas the 2010 and 2011 waves resulted in 471 and 124 affected farms, respectively, together affecting almost the whole country (Figure 1B–F). In a previous study3, three of these five outbreak waves (first 2009 wave, 2010 and 2011) exhibited space-time interaction, which was interpreted as an indication of underlying short and long distance transmission mechanisms. Further research is required to explain the variation in the relative importance of different spatio-temporal transmission processes during those epidemics. Only few studies or field observations have been published on these epidemics and are summarized hereinafter. In 2008, cases were preceded by a period of heavy rainfall4, but like other outbreaks in the eastern part of the country, did not develop into a large epidemic. Little published information is available on the 2009 waves, and no unusual environmental conditions were found to be related to the second 2009 wave, although it occurred in an area where flood irrigation was practiced5. In 2010 and 2011, RVF epidemics followed periods of heavy rains and localized flooding, and the virus spread almost throughout the whole country.

Bottom Line: Cox regressions with a Besag model to account for the spatial effects were fitted to the data.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.However, the crucial need for improved access to data on population at risk, animal movements and vaccine use is highlighted to improve model predictions.

View Article: PubMed Central - PubMed

Affiliation: 1] Veterinary Epidemiology, Economics and Public Health Group, Department of Production and Population Health, Royal Veterinary College, Hatfield, United Kingdom [2] Centre for the Mathematical Modelling of Infectious Diseases and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

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.

No MeSH data available.


Related in: MedlinePlus