Stochastic modelling and statistical inference for epidemics in structured populations

结构化人群流行病的随机建模和统计推断

• 批准号: EP/F03234X/1
• 负责人: Frank Ball
• 金额: $ 2.02万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF03234X%2F1
• 关键词: Stochastic; modelling; statistical; inference; epidemics

The aim of the proposed research is to develop stochastic epidemic models that incorporate important population heterogeneities, together with techniques for their analysis and statistical inference. Two broad classes of such models will be considered.The first class is concerned with models for infectious diseases in which the degree of severity of infected individuals and their potential for future spread are determined by the size of the infecting dose. More specifically, various models with two severities of infection, mild and severe, will be investigated, first for a homogeneously mixing population and then for a community of households. For each model, a threshold parameter that determines whether or not an outbreak can become established will be determined, together with other properties, such as the probability that an outbreak does become established and the final outcome if it does. Implications for vaccination strategies will be explored, using a variety of models for how vaccination affects a vaccinee's susceptibility to the disease in question and their ability to spread the disease if they become infected.The second class of models is that in which the spread of infection can occur at three different levels within the at-risk population. An example would be a model in which infection is permitted to occur within households, within schools, and also in the population at large, with different risks of infection in each place. Such models are relatively underexplored in the literature, but are of increasing importance in real-life epidemic and pandemic planning. In particular, the efficacy of control strategies such as school closure or travel restrictions relies crucially on the kind of population-level mixing that such models describe. The proposed research aims to explore fundamental issues of statistical inference and data collection for such models, addressing such questions as what can be inferred from different sorts of data, and the extent to which three-level-mixing models are more useful than simpler, but less realistic, models.

这项研究的目的是开发包含重要的种群异质性的随机流行病模型，以及分析和统计推断的技术。我们将考虑两大类这样的模型。第一类是关于传染病的模型，在该模型中，受感染个体的严重程度和他们未来传播的可能性取决于感染剂量的大小。更具体地说，将研究具有两种严重程度的感染的各种模型，首先是针对均匀混合的人口，然后是针对家庭社区。对于每个模型，将确定确定是否可以建立疫情的阈值参数，以及其他属性，例如疫情确实建立的概率以及如果建立的话，最终结果。将探索疫苗接种策略的含义，使用各种模型来说明接种疫苗如何影响受试者对所述疾病的易感性，以及他们在被感染时传播疾病的能力。第二类模型是在高危人群中感染可以在三个不同水平上传播的模型。一个例子是允许在家庭、学校和一般人群中发生感染的模式，每个地方感染的风险不同。此类模型在文献中的探索相对较少，但在现实生活中的流行病和大流行规划中越来越重要。特别是，关闭学校或限制旅行等控制策略的有效性关键取决于这些模型所描述的那种人口层面的混合。拟议的研究旨在探索这类模型的统计推断和数据收集的基本问题，解决从不同类型的数据中可以推断出什么，以及三水平混合模型在多大程度上比更简单但不太现实的模型更有用等问题。

项目成果

Household epidemic models with varying infection response

具有不同感染反应的家庭流行病模型

• DOI: 10.48550/arxiv.1005.4570
• 发表时间: 2010
• 作者: Ball F

Household epidemic models with varying infection response.

具有不同感染反应的家庭流行病模型。

• DOI: 10.1007/s00285-010-0372-6
• 发表时间: 2011
• 期刊: Journal of mathematical biology
• 影响因子: 1.9
• 作者: Ball F

Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak

• DOI: 10.1111/j.1467-9469.2010.00726.x
• 发表时间: 2011-09-01
• 期刊: SCANDINAVIAN JOURNAL OF STATISTICS
• 影响因子: 1
• 作者: Britton, Tom;Kypraios, Theodore;O'Neill, Philip D.
• 通讯作者: O'Neill, Philip D.