Modelling and inference of tree pandemics in Great Britain

英国树木流行病的建模和推断

• 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

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木，最近英国白蜡树和栗树的流行已被新闻广泛报道。整个树木种群的大量砍伐导致了巨大的生态变化，也可能带来深刻的社会经济挑战。美国环境、食品和农村事务部发现，缺乏现实的模型来描述和预测树木病害的传播，这是他们有效控制病虫害能力的一个关键差距。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。这项拟议的研究将结合应用数学建模和统计推理的尖端技术，开发一种全面的建模方法来预测英国的树木病害。我们提出的框架将允许该模型在被用于预测新出现的病原体之前，根据过去暴发的数据进行训练，例如板栗疫病。

项目成果

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

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

• DOI: 10.3390/d15040496
• 发表时间: 2023
• 期刊: Diversity
• 作者: Wadkin L

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.