Modelling and inference of tree pandemics in Great Britain

英国树木流行病的建模和推断

基本信息

批准号：
EP/V048511/1
负责人：
Andrew Baggaley
金额：
$ 25.78万
依托单位：
Newcastle University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV048511%2F1
关键词：
Modelling inference tree pandemics Great

项目摘要

The spread of contagious tree diseases threatens woodlands and urban trees in the UK and globally, and recent UK epidemics of ash and chestnut trees have been covered extensively in the news. The decimation of entire tree populations leads to dramatic ecological changes, and can also pose profound socio-economic challenges. Defra, the government Department for Environmental, Food and Rural Affairs have identified a lack of realistic models to describe and predict tree disease spread as a key gap in their ability to manage pests and diseases effectively. This could have a major influence on the planning and management of trees in both woodlands and urban settings. The proposed research will combine cutting edge techniques from applied mathematical modelling and statistical inference to develop a comprehensive modelling approach to predict tree disease in the UK. The framework we propose will allow the model to be trained on data of past outbreaks before being used to predict emerging pathogens such as sweet chestnut blight.

传染性树木疾病的传播威胁着英国和全球的林地和城市树，而最近的英国灰烬和栗树的流行病已经在新闻中广泛涵盖。整个树木种群的分解导致了巨大的生态变化，也可能构成深刻的社会经济挑战。政府环境，食品和农村事务部Defra已经确定了缺乏现实的模型来描述和预测树疾病的传播是其有效地管理害虫和疾病的能力的关键差距。这可能会对林地和城市环境中树木的规划和管理产生重大影响。拟议的研究将从应用的数学建模和统计推断中结合前沿技术，以开发一种全面的建模方法来预测英国的树疾病。我们提出的框架将允许模型在过去的爆发数据中进行培训，然后才能预测新兴的病原体，例如甜栗子疫病。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Accelerating Bayesian inference for stochastic epidemic models using incidence data

DOI：
10.1007/s11222-023-10311-6
发表时间：
2023-12-01
期刊：
STATISTICS AND COMPUTING
影响因子：
2.2
作者：
Golightly，Andrew;Wadkin，Laura E.;Kypraios，Theodore
通讯作者：
Kypraios，Theodore

Inference for epidemic models with time-varying infection rates: Tracking the dynamics of oak processionary moth in the UK.

DOI：
10.1002/ece3.8871
发表时间：
2022-05
期刊：
ECOLOGY AND EVOLUTION
影响因子：
2.6
作者：
Wadkin, Laura E.;Branson, Julia;Hoppit, Andrew;Parker, Nicholas G.;Golightly, Andrew;Baggaley, Andrew W.
通讯作者：
Baggaley, Andrew W.

Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model

通过二节点流行病网络模型的推理量化入侵害虫动态