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数学模型在了解绵羊传染性海绵状脑病的流行病学和控制中的作用:综述。

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

机构信息

Institute for Animal Health, Pirbright Laboratory, Ash Road, Pirbright, Surrey GU24 0NF, United Kingdom.

出版信息

Vet Res. 2010 Jul-Aug;41(4):42. doi: 10.1051/vetres/2010014. Epub 2010 Feb 23.

Abstract

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.

摘要

在研究绵羊传染性海绵状脑病(TSE),特别是羊瘙痒病的流行病学时,人们广泛利用数学建模来处理观察的不完整性和理清传播的复杂性。重要的是,这些建模方法可以将临床疾病的发病率与潜在的感染流行率联系起来,从而克服了研究这些疾病的主要困难之一。模型已被用于调查单个羊群和区域范围内的羊瘙痒病的流行病学;评估不同控制策略的效果,特别是基于朊病毒蛋白(PrP)基因型的选择性育种计划;解释羊瘙痒病监测的结果;并为监测计划的设计提供信息。此外,在评估绵羊中可能存在的牛海绵状脑病对人类健康的风险时,数学建模也发挥了重要作用。在这里,我们回顾了在开发和分析绵羊 TSE 流行病学和控制的数学模型时所采用的各种方法,并评估了它们对我们对这些疾病的理解的影响。我们还确定了需要进一步研究的领域,讨论了未来的挑战并指出了数据差距。

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