Bayesian Non-Parametric Methodology for Stochastic Epidemic Models

随机流行病模型的贝叶斯非参数方法

• 批准号: 1800036
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1800036
• 关键词: Bayesian; Non; Parametric; Methodology; Stochastic

Simulating from and making inference for stochastic epidemic models are key strategies for understanding and controlling the spread of infectious diseases. Despite the enormous attention given to methods for parameter estimation, there has been relatively little activity in the area of nonparametric inference. That is, drawing inference for the infection rate without making specific modelling assumptions about its functional form. In this project we are concerned with heterogeneously mixing models in which the infection rate between two individuals is a function of their characteristics, for example location or type. We develop a novel method for inferring the function nonparametrically, removing the need to make questionable parametric assumptions. We adopt a Bayesian approach by assigning a Gaussian Process prior distribution to the infection rate function and then develop an efficient data augmentation Markov Chain Monte Carlo methodology to estimate the infection rate function, the GP hyperparameters and the unobserved infection times. We will illustrate our methodology using simulated data and as well as data on an Avian Influenza outbreak from the Netherlands.

对随机流行病模型进行模拟和推理是理解和控制传染病传播的关键策略。尽管参数估计方法受到了极大的关注，但在非参数推断领域的活动相对较少。也就是说，对感染率进行推断，而不对其函数形式进行具体的建模假设。在这个项目中，我们关注的是异质混合模型，其中两个个体之间的感染率是其特征的函数，例如位置或类型。我们开发了一种新的方法推断的功能nonparametrically，消除需要作出可疑的参数假设。我们采用贝叶斯方法分配高斯过程先验分布的感染率函数，然后开发一个有效的数据增强马尔可夫链蒙特卡罗方法来估计感染率函数，GP超参数和未观察到的感染时间。我们将使用模拟数据以及荷兰禽流感爆发的数据来说明我们的方法。

项目成果

A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands

• DOI: 10.1111/rssc.12515
• 发表时间: 2021-08-10
• 期刊: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
• 影响因子: 1.6
• 作者: Seymour, Rowland G.;Kypraios, Theodore;Hagenaars, Thomas J.
• 通讯作者: Hagenaars, Thomas J.