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

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NIHR资助的健康保护研究单位在基因组和使能数据方面与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，PHE目前正在进行重组，不久将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这里提议的项目产生不利影响。研究的背景-数学流行病学主要基于确定性部分-心理模型。这些模型从数学角度来看很方便，但并不总是适合表示传染病，特别是在疫情爆发的早期阶段。研究的目的和目标-我们的目标是开发一个现实的模型，说明传染病在疫情中的传播方式。我们还致力于在此模型的基础上发展推理方法学，以便可以使用流行病学数据来推断关键的流行病学参数并检验假设。研究方法的新颖性-我们的研究方法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。我们将通过对我们的新方法和以前的方法进行彻底的比较来展示新颖性。潜在的影响、应用和好处--应用拟议的方法将使我们能够更好地估计关键的流行病学参数，特别是在新爆发的早期阶段。这将反过来告知应该在疾病控制方面付出多少努力，并为设计适当的控制措施提供证据基础。研究如何与职权范围相关-建议的研究思路落入几个EPSRC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)和医疗技术。研究领域；全球不确定性、医疗技术、ICT[信息和通信技术]、制造未来、数学科学、研究基础设施外部合作伙伴-公共卫生英格兰

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