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Dynamic modelling of foot-and-mouth disease (FMD) epidemiology and persistence in endemic areas

流行地区口蹄疫（FMD）流行病学和持续性的动态模型

基本信息

批准号：
1805111
负责人：
金额：
--
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1805111
关键词：
Dynamic modelling foot mouth disease

项目摘要

Increasingly it is clear that to understand the ecology and epidemiology of pathogens we also need to understand and incorporate their evolution in models. This is particularly critical for rapidly evolving viruses such as foot-and-mouth disease in animals or influenza in humans (Roche, Drake, & Rohani, 2011). This is important because simple differential equation models that assume random mixing and fixed pathogen characteristics are likely to be biased and over estimate the impact of controls such as vaccination and movement restrictions. Furthermore, most current animal disease models are largely defined at a herd level and rarely include within herd dynamics or account for the free ranging movements seen in communal grazing areas such as in Africa. Therefore to understand how disease control might work at large geographical scales such as in sub-Saharan Africa (SSA) it is important to understand the how pathogens are evolving to evade the immune system of a host and how that intersects with host immunity waning or the accumulation of new naïve animals in the population through births. Understanding of these effects will become essential when attempting large scale disease eradication as currently being considered by the Gates Foundation, FAO and OIE for foot-and-mouth disease (Kitching et al., 2007). Unlike smallpox and rinderpest, where there was a single vaccine that produced life long immunity against all strains, in the case of foot-and-mouth disease there are multiple serotypes (6 currently known to circulate in SSA) and strains and current vaccines induce short lived immunity with little or no cross protection resulting in the need for regular vaccine matching. The overlap between epidemiological and evolutionary time scales, the observed immune drift in the livestock populations, and the high mutation rate of RNA viruses make it essential to integrate a large strain space into models. Consequently, the classic modeling approaches, based on the familiar SIR framework, are not readily amenable for this purpose because the resulting state space increases rapidly with the number of strains and, consequently, becomes too cumbersome for meaningful analysis. Therefore using FMD as an example this project will develop an agent based modelling framework. The rationale of this kind of modelling is to explore individual heterogeneity, allowing us to track infection history. This framework will include movement networks and virus evolution layers over laid on the transmission model to explore these dynamics in silico to develop the theoretical bases from which specific control options can be explored. These type of models are now possible because of the availability of cheap processing power.The project will focus on two key and interlinked questions in RNA viral epidemiology using FMD as the exemplar: i) at what scale(s) do viruses persist in an endemic setting and ii) what are the drivers of persistence (local or long-range livestock movement, virus evolution, waning host immunity, population size, carrier state etc)? The project builds on the supervisors long experience of working on FMD in Africa and in modelling disease spread (Bronsvoort, 2003). In addition we have on going work in Cameroon where much of the original work on endemic FMD was carried out, describing livestock movements through markets and using GPS collars to map within, between herd and transhumance movements. Previous phylogenetic analyses of FMD spread have informed analyses of the spatial spread of the virus and evolutionary changes but have not explicitly incorporated these in consideration of local spread. Furthermore, recent work on identifying when past exposure to an infection will protect against newly emerging strains is likely to be critical to viral persistence at some scale but remains to be fully understood and incorporated in models of viral spread.

越来越清楚的是，要了解病原体的生态学和流行病学，我们还需要了解它们的进化并将其纳入模型。这对于快速进化的病毒，如动物的口蹄疫或人类的流感，尤其重要(Roche，Drake，&Rohani，2011)。这一点很重要，因为假设随机混合和固定病原体特征的简单微分方程模型可能存在偏见，并高估了疫苗接种和行动限制等控制措施的影响。此外，目前大多数动物疾病模型在很大程度上是在畜群水平上定义的，很少包括在畜群动态中，也很少考虑在非洲等公共放牧区看到的自由活动。因此，为了了解疾病控制如何在大的地理范围内发挥作用，例如在撒哈拉以南非洲(SSA)，重要的是了解病原体如何进化以逃避宿主的免疫系统，以及这如何与宿主免疫力减弱或通过出生在种群中积累新的幼稚动物相交叉。在盖茨基金会、粮农组织和世界动物卫生组织目前正在考虑的口蹄疫大规模根除行动中，对这些影响的了解将变得至关重要(Kitching等人，2007年)。与天花和牛瘟不同，在天花和牛瘟中，只有一种疫苗对所有毒株产生终身免疫，而在口蹄疫病例中，有多种血清型(目前已知在SSA中传播6种)和毒株，目前的疫苗诱导短期免疫，很少或没有交叉保护，因此需要定期匹配疫苗。流行病学和进化时间尺度之间的重叠，在牲畜种群中观察到的免疫漂移，以及RNA病毒的高突变率，使得将大株空间整合到模型中是必要的。因此，基于熟悉的SIR框架的经典建模方法不容易满足这一目的，因为所产生的状态空间随着应变的数量而迅速增加，因此变得过于繁琐，难以进行有意义的分析。因此，以口蹄疫为例，本项目将开发一个基于代理的建模框架。这种建模的基本原理是探索个体的异质性，使我们能够追踪感染历史。这一框架将包括覆盖在传播模型上的运动网络和病毒演化层，以探索这些动态变化，从而为探索具体的控制方案奠定理论基础。这些类型的模型现在是可能的，因为有了廉价的处理能力。该项目将以口蹄疫为样本，重点研究核糖核酸病毒流行病学中的两个关键且相互关联的问题：i)病毒在流行环境中持续存在的规模(S)，以及ii)持续存在的驱动因素是什么(本地或远程牲畜迁徙、病毒进化、宿主免疫力减弱、种群规模、携带者状态等)？该项目建立在监督者在非洲从事口蹄疫工作和建立疾病传播模型方面的长期经验的基础上(Bronsvoort，2003)。此外，我们还在喀麦隆继续开展工作，在那里开展了许多关于地方性口蹄疫的原始工作，描述了牲畜通过市场的流动，并使用GPS项圈在牛群和运输之间绘制地图。以前对口蹄疫传播的系统发育分析已经了解了病毒的空间传播和进化变化的分析，但没有明确地将这些纳入考虑局部传播的情况。此外，最近关于确定过去的感染暴露何时将保护新出现的病毒株的工作可能对病毒在一定程度上的持久性至关重要，但仍有待于充分了解并纳入病毒传播的模型。