Spatial and epidemiological modelling for wildlife and agricultural health

野生动物和农业健康的空间和流行病学模型

• 批准号: 2737820
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2737820
• 关键词: Spatial; epidemiological; modelling; wildlife; agricultural

This project will develop spatial and epidemiological models for wildlife and agricultural diseases. It will use two distinct host-parasite systems where there is a need for complex models to address fundamental applied issues. Although the two applied problem areas (trichomonosis in UK finches, and uptake of Integrated Pest Management by UK farmers) involve different stakeholders, different data issues and different epidemiology, they are linked by a commonality of approach and modelling challenges. Key questions applied to both systems include: using model-fitting to understand the disease; assessing the outcomes of disease control policy; and assessing feedback across scales. The first two chapters will concern the use of mathematical modelling in understanding the finch trichomonosis outbreak, which has been ongoing since 2005, and has resulted in drastic declines in British greenfinch and chaffinch populations. The availability of population and disease incidence data allows forthe development and fitting of detailed mathematical models for this disease system. The core research questions which we seek to address are: (1) can we infer the spatial patterns in the disease dynamics from routinely collected wildlife data sets, (2) what mechanisms are driving seasonality. Initially, a deterministic time-dependent disease model will be developed. A key aspect of the model development methodology will be working with the BTO and IoZ experts in constructing a functional representation of the relevant biological parameters, such as contact rates, density dependence of disease, and population birth and survival rates. This model will be fitted to the population and disease reporting data using likelihood optimisation methods (maximum likelihood and MCMC), allowing us to infer mechanisms and routes of infection. This model and corresponding fitting will then be extended to include the spatial dimension of the data. The next chapters will focus on agricultural diseases. This will involve the integration of farmer-behaviour models with crop disease models, in order to investigate strategies to reduce dependency on chemical pesticides and increase the uptake of Integrated Pest Management (IPM) by farmers. Currently the use of pesticides continues to rise; in large part due to the perception by farmers that the alternative disease management strategies, such as Integrated PestManagement (IPM), are difficult and costly. In order to encourage the uptake of IPM by farmers, the UK government offers a scheme of incentives, primarily as payments per-year. Behavioural modellingcan be used to guide incentive schemes by evaluating the impact of various IPM adoption outcomes on the disease system. Two key questions which this project will aim to answer are (1) how successful would varying levels of uptake of IPM be at controlling disease, and (2) what fraction of farmers would need to initially take up IPM in order for it to be successful at controlling disease. This problem can be approached using a deterministic ODE model initially; investigating the outcomes from different initial conditions, and different parameter values dictating the interactions between the disease system and the farmer-behaviour system. Later methodological approaches can then be expanded to include stochastic frameworks, which explore localised or individual-based dynamics.The intention is to begin this work by looking at yellow rust in cereal crops. Models already exist for this disease system, and there are a number of well defined control strategies which are known to work. One such strategy involves the use of resistant crop varieties, which are important for yellow rust control, but which farmers find challenging because of the need to change varieties frequently since resistance can be quickly overcome.

该项目将开发用于野生动植物和农业疾病的空间和流行病学模型。它将使用两个不同的主寄生系统系统，其中需要复杂的模型来解决基本应用问题。尽管两个应用问题领域（英国雀科的三分法和英国农民对综合有害生物管理的吸收）涉及不同的利益相关者，不同的数据问题和不同的流行病学，但它们与方法的共同点和建模挑战的共同点联系在一起。应用于这两个系统的关键问题包括：使用模型拟合来了解疾病；评估疾病控制政策的结果；并评估跨尺度的反馈。前两章将涉及使用数学建模来理解自2005年以来一直在进行的Finch Trichomonisos爆发中，并导致英国绿菲奇和小型人群的大幅下降。人口和疾病发病率数据的可用性允许开发和拟合该疾病系统的详细数学模型。我们寻求解决的核心研究问题是：（1）我们可以从常规收集的野生动植物数据集中推断出疾病动态中的空间模式，（2）哪些机制驱动了季节性。最初，将开发确定的时间依赖性疾病模型。模型开发方法的一个关键方面将与BTO和IOZ专家合作，以构建相关生物学参数的功能表示，例如接触率，疾病的密度依赖性以及人口出生和存活率。该模型将使用似然优化方法（最大似然和MCMC）拟合到人口和疾病报告数据，从而使我们能够推断出机制和感染途径。然后将扩展该模型和相应的拟合，以包括数据的空间维度。接下来的章节将重点放在农业疾病上。这将涉及将农民 - 行为模型与农作物疾病模型的整合，以调查减少对化学农药依赖的策略，并增加农民对综合有害生物管理（IPM）的吸收。目前，农药的使用持续上升。在很大程度上，由于农民的看法是，替代性疾病管理策略（例如综合瘟疫管理（IPM））是困难而昂贵的。为了鼓励农民吸收IPM，英国政府提供了一项激励计划，主要是每年付款。通过评估各种IPM采用结果对疾病系统的影响，将使用行为模型来指导激励方案。该项目旨在回答的两个关键问题是（1）IPM在控制疾病方面的影响程度如何，以及（2）最初需要采取IPM的农民需要多少部分才能成功。控制疾病。最初可以使用确定的ODE模型来解决此问题。研究不同初始条件的结果，以及不同的参数值决定了疾病系统与农民行为系统之间的相互作用。然后可以扩展后来的方法论方法，包括随机框架，这些框架探索本地化或基于个体的动力学。目的是通过在谷物农作物中查看黄色生锈开始这项工作。该疾病系统已经存在模型，并且有许多已知有效的控制策略。一种这样的策略涉及使用抗性作物品种，这对于黄色生锈至关重要，但是由于需要迅速克服抗性，农民会经常改变品种，因为需要经常改变品种。