Bayesian spatial statistics for small area estimation of HIV

用于 HIV 小区域估计的贝叶斯空间统计

• 批准号: 2605899
• 金额: --
• 依托单位: Imperial College London
• 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-2605899
• 关键词: Bayesian; spatial; statistics; small; area

This research aims to develop Bayesian methods for sub-national modelling and inference of HIV in sub-Saharan Africa.Accurate sub-national estimates are needed to inform the targeted policy interventions required to reduce HIV disease burden and end the epidemic.There are three key objectives of this research.The first is to create models for spatial random effects which take a more realistic view of space.The second objective is to develop new inference methods which extend the integrated nested Laplace approximation to models outside the latent Gaussian class.Lastly, this research aims to advance a Bayesian approach to survey design which will make use of the small area estimation model to achieve greater sample efficiency.This project falls within the healthcare technologies and mathematical sciences EPSRC research areas.

本研究旨在开发贝叶斯方法，用于撒哈拉以南非洲地区的次国家艾滋病毒建模和推断。需要准确的次国家估计，以便为减少艾滋病毒疾病负担和结束这一流行病所需的有针对性的政策干预提供信息。本研究有三个主要目标。第一个是创建空间随机效应的模型，以更现实的视角看待空间。第二个目标是开发新的推理方法，将集成嵌套拉普拉斯近似扩展到潜在高斯类之外的模型。最后，本研究旨在提出一种贝叶斯方法来进行调查设计，该方法将利用小面积估计模型来实现更高的样本效率。该项目属于EPSRC的医疗保健技术和数学科学研究领域。