The role of mathematical modelling in understanding the epidemiology and control of sheep transmissible spongiform encephalopathies: a review

被引:7
作者
Gubbins, Simon [1 ]
Touzeau, Suzanne [2 ]
Hagenaars, Thomas J. [3 ]
机构
[1] Inst Anim Hlth, Pirbright Lab, Pirbright GU24 0NF, Surrey, England
[2] INRA, Math & Informat Appl UR341, F-78350 Jouy En Josas, France
[3] Cent Vet Inst Wageningen, Dept Virol, NL-8200 AB Lelystad, Netherlands
基金
英国生物技术与生命科学研究理事会;
关键词
epidemiology; transmissible spongiform encephalopathy; scrapie; mathematical modelling; sheep; PRION PROTEIN GENOTYPE; GREAT-BRITAIN; CLASSICAL SCRAPIE; NATURAL SCRAPIE; BRITISH SHEEP; POPULATION-DYNAMICS; NORWEGIAN SHEEP; BSE INFECTION; SUFFOLK SHEEP; PRP GENOTYPE;
D O I
10.1051/vetres/2010014
中图分类号
S85 [动物医学(兽医学)];
学科分类号
0906 ;
摘要
To deal with the incompleteness of observations and disentangle the complexities of transmission much use has been made of mathematical modelling when investigating the epidemiology of sheep transmissible spongiform encephalopathies (TSE) and, in particular, scrapie. Importantly, these modelling approaches allow the incidence of clinical disease to be related to the underlying prevalence of infection, thereby overcoming one of the major difficulties when studying these diseases. Models have been used to investigate the epidemiology of scrapie within individual flocks and at a regional level; to assess the efficacy of different control strategies, especially selective breeding programmes based on prion protein (PrP) genotype; to interpret the results of scrapie surveillance; and to inform the design of surveillance programmes. Furthermore, mathematical modelling has played an important role when assessing the risk to human health posed by the possible presence of bovine spongiform encephalopathy in sheep. Here, we review the various approaches that have been taken when developing and analysing mathematical models for the epidemiology and control of sheep TSE and assess their impact on our understanding of these diseases. We also identify areas that require further work, discuss future challenges and identify data gaps.
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页数:24
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