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）。沃里克大学已经通过国家卫生研究院资助的基因组学和赋能数据健康保护研究股与PHE建立了合作联系，该项目将建立在这些现有联系的基础上。请注意，PHE目前正在重组，并将很快更名为国家健康保护研究所（NIHP），但这不会对我们的合作关系或这里提出的项目产生不利影响。研究的背景-数学流行病学主要是基于确定性隔室心理模型。这些模型从数学的角度来看是方便的，但并不总是适合代表传染病，特别是在爆发的早期阶段。研究的目的和目标-我们的目标是开发一个模型，这是现实的传染病在爆发的情况下传播的方式。我们的目标是在此模型的基础上开发推理方法学，以便流行病学数据可以用来推断关键的流行病学参数和测试hypothesis.The研究方法的新奇-我们的研究方法是基于现实的传染病传播的随机模型。我们还利用最新的计算机密集型统计推断方法。新奇将通过我们的新方法和以前的方法之间的彻底比较来证明。潜在的影响，应用和好处-应用所提出的方法将允许更好地估计关键的流行病学参数，特别是在新的爆发的早期阶段。这将反过来告诉我们应该在疾病控制方面投入多少努力，并为设计合适的控制措施提供证据基础。研究如何与职权范围相关-拟议的研究思路福尔斯分为几个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]，制造未来，数学科学，研究机构外部合作伙伴-英国公共卫生

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