Bayesian Analysis And Prediction Of Infectious Disease Outbreaks Based On Temporal Data

基于时态数据的传染病爆发的贝叶斯分析和预测

• 批准号: 2271332
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271332
• 关键词: Bayesian; Analysis; Prediction; Infectious; Disease

Our external partner organisation is Public Health England (PHE). The University of Warwick already has collaborative links in place with PHE via the NIHR-funded Health Protection Research Unit in Genomics and Enabling Data, and this project will build upon these existing links. Note that PHE is currently being restructured and will soon be renamed as the National Institute for Health Protection (NIHP), but this will not adversely affect our collaborative links or the project proposed here.The context of the research - Mathematical epidemiology are mostly based on deterministic compart-mental models. These models are convenient from mathematical point of view, but not always appropriate to represent infectious diseases especially in the early stages of an outbreak.The aims and objectives of the research - We aim to develop a model that is realistic of the way infectious diseases spread in outbreak situations. We also aim to develop inferential method-ology based on this model, so that epidemiological data can be used to infer key epidemiological parameters and test hypotheses.The novelty of the research methodology - Our research methodology is based on realistic stochastic models for the spread of infectious diseases. We also make use of the latest methods for computer-intensive statistical inference. Novelty will be demonstrated through a thorough comparison between our new methodology and previous ones.The potential impact, applications, and benefits - Application of the proposed methodology will allow a better estimate of key epidemiological parameters especially in the early stages of a new outbreak. This will in turn inform how much effort should go into disease control, and provide an evidence basis for the design of suitable control measures.How the research relates to the remit - The proposed research idea falls into several EPSRC research areas including Mathematical Biology, Non-linear systems, Clinical technologies (excluding imaging) and Healthcare Technologies.Research Areas; Global uncertainties, Healthcare technologies, ICT [Information and Communication Technologies], Manufacturing the Future,Mathematical Sciences, Research InfrastructureExternal Partner - Public Health England

我们的外部合作伙伴组织是英国公共卫生（PHE）。华威大学已经通过国家卫生研究院资助的基因组学和数据健康保护研究单位与PHE建立了合作关系，该项目将建立在这些现有联系的基础上。请注意，公共卫生部门目前正在重组，并将很快更名为国家卫生防护研究所（NIHP），但这不会对我们的合作联系或此处提出的项目产生不利影响。研究的背景-数学流行病学大多基于确定性隔间模型。从数学角度来看，这些模型很方便，但并不总是适合于表示传染病，特别是在爆发的早期阶段。研究的目的和目标——我们的目标是建立一个模型，能够真实地反映传染病在爆发情况下的传播方式。我们的目标是在此模型的基础上发展推理方法，以便流行病学数据可以用来推断关键的流行病学参数和检验假设。研究方法的新颖性-我们的研究方法是基于传染病传播的现实随机模型。我们还利用最新的方法进行计算机密集的统计推断。新颖性将通过将我们的新方法与以前的方法进行彻底的比较来证明。潜在的影响、应用和益处——应用拟议的方法将有助于更好地估计关键的流行病学参数，特别是在新暴发的早期阶段。这将反过来告知应该在疾病控制方面投入多少努力，并为设计适当的控制措施提供证据基础。研究与职权范围的关系——拟议的研究理念属于EPSRC的几个研究领域，包括数学生物学、非线性系统、临床技术（不包括成像）和医疗保健技术。研究领域;全球不确定性，医疗保健技术，ICT[信息和通信技术]，未来制造业，数学科学，研究基础设施