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

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作者
Raphaëlle Métras
Chris Jewell
Thibaud Porphyre
Peter N. Thompson
Dirk U. Pfeiffer
Lisa M. Collins
Richard G. White
机构
[1] Royal Veterinary College,Veterinary Epidemiology, Economics and Public Health Group, Department of Production and Population Health
[2] Centre for the Mathematical Modelling of Infectious Diseases and Faculty of Epidemiology and Population Health,Epidemiology Section, Department of Production Animal Studies
[3] London School of Hygiene and Tropical Medicine,undefined
[4] Institute of Fundamental Sciences,undefined
[5] Massey University,undefined
[6] Centre for Immunity,undefined
[7] Infection and Evolution,undefined
[8] University of Edinburgh,undefined
[9] Ashworth Laboratories,undefined
[10] University of Pretoria,undefined
[11] School of Life Sciences,undefined
[12] University of Lincoln,undefined
[13] Tuberculosis Modelling Group,undefined
[14] London School of Hygiene and Tropical Medicine,undefined
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摘要
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.
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