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.

该项目将开发野生动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宿主-寄生虫系统，其中需要复杂的模型来解决基本的应用问题。虽然这两个应用问题领域(英国雀类滴虫病和英国农民采用综合有害生物管理)涉及不同的利益攸关方、不同的数据问题和不同的流行病学，但它们因方法和建模挑战的共性而联系在一起。适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；以及评估不同级别的反馈。前两章将涉及使用数学模型来理解自2005年以来一直在进行的雀类滴虫病暴发，并已导致英国绿雀和斑雀数量的急剧下降。人口和疾病发病率数据的可获得性允许为这一疾病系统开发和拟合详细的数学模型。我们寻求解决的核心研究问题是：(1)我们能否从常规收集的野生动物数据集中推断疾病动态的空间模式，(2)是什么机制推动了季节性。首先，将开发一个确定性的、依赖时间的疾病模型。模型开发方法的一个关键方面将是与BTO和IoZ专家合作，构建相关生物参数的函数表示，如接触率、疾病密度相关性以及人口出生率和存活率。该模型将适用于人口和疾病报告数据，使用似然优化方法(最大似然法和MCMC)，使我们能够推断感染的机制和途径。然后，该模型和相应的拟合将被扩展以包括数据的空间维度。接下来的章节将集中讨论农业疾病。这将涉及将农民行为模型与作物疾病模型相结合，以研究减少对化学杀虫剂的依赖并增加农民对有害生物综合管理(IPM)的接受的战略。目前，杀虫剂的使用量继续上升；这在很大程度上是因为农民认为替代疾病管理战略，如病虫害综合管理(IPM)，难度大、成本高。为了鼓励农民采用IPM，英国政府提供了一项激励计划，主要是按年支付。行为模型可用于通过评估各种IPM采用结果对疾病系统的影响来指导激励计划。该项目旨在回答的两个关键问题是(1)不同程度的IPM在控制疾病方面的成功程度，以及(2)最初需要多少农民采用IPM才能成功地控制疾病。这个问题最初可以使用确定性的常微分方程模型来解决；调查不同初始条件的结果，以及决定疾病系统和农民行为系统之间相互作用的不同参数值。后来的方法论方法可以扩展到包括随机框架，它探索局部或基于个体的动态。其目的是通过观察谷类作物的黄锈病来开始这项工作。这种疾病系统的模型已经存在，并且有许多已知有效的明确的控制策略。一种这样的策略涉及使用抗病作物品种，这对控制黄锈病很重要，但农民发现这一点具有挑战性，因为需要经常更换品种，因为抗药性可以很快克服。