Inference on Spatio-Temporal Log-Gaussian Cox Processes for Spatially Aggregated Disease Incidence Data

空间聚合疾病发病率数据的时空对数高斯 Cox 过程的推断

• 批准号: 342306-2012
• 负责人: Brown, Patrick
• 金额: $ 0.87万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572482
• 关键词: Inference; Spatio; Temporal; Log; Gaussian

Motivated by the problem of understanding spatial variations in disease risk using routinely collected survey data, this project aims to develop models and statistical inference methods for spatially aggregated data spanning long time periods. When studying small-area variations in disease risk, where prediction of disease risk at a high resolution is required, the fact that exact case locations are unavailable becomes important. Further, the census regions and postal regions on which data are typically made available vary greatly in size being highly informative in urban areas and heavily censored in the less populated ares which dominate maps. Studies on small regions or rare outcomes require long study periods to accumulate sufficient cases. This introduces the problem of risk changing over time as well as changes to census and postal regions. This project will build on the applicant's previous work by first extending the models to allow for changes in risk over time, and second by improving the method of statistical inference to allow for the higher dimensionality resulting from the shift to a spatio-temporal model. The existing Markov random field approximation to continuous spatial surfaces will be extended to approximate Markovian spatio-temporal processes with sparse precision matrices. Markov Chain Monte Carlo inference will be improved by using a number of recently developed methods such as Remiann manifold Langevin MCMC. The methods will be developed with the aim of addressing substantive problems in the applied literature, with existing collaborations concerning Syphilis in North Carolina, Lupus in Toronto, and Mesothelioma lung cancer in western Ontario maintained and built upon. As a result of this work, a greater variety of problems in disease mapping will be able to be addressed, notably when case counts are low, aggregation is irregular, and spatio-temporal interactions are present.

该项目的动机是利用常规收集的调查数据了解疾病风险的空间变化，目的是为跨越长时间的空间汇总数据开发模型和统计推断方法。 在研究疾病风险的小区域变化时，需要以高分辨率预测疾病风险，无法获得确切病例位置的事实变得很重要。 此外，通常提供数据的人口普查地区和邮政地区的规模差异很大，在城市地区信息量很大，而在地图上人口较少的战神则受到严格审查。 对小区域或罕见结果的研究需要长时间的研究来积累足够的病例。 这就带来了风险随时间变化以及人口普查和邮政区域变化的问题。 该项目将建立在申请人以前的工作的基础上，首先扩展模型，以允许随着时间的推移风险的变化，其次通过改进统计推断的方法，以允许从转移到时空模型产生的更高维度。 现有的马尔可夫随机场近似连续空间表面将扩展到近似马尔可夫时空过程稀疏精度矩阵。 马尔可夫链蒙特卡罗推理将通过使用一些最近开发的方法，如Remiann流形Langevin MCMC得到改进。 这些方法将开发的目的是解决应用文献中的实质性问题，与现有的合作，关于Syphilis在北卡罗来纳州，狼疮在多伦多，间皮瘤肺癌在西部安大略维持和建立。 这项工作的结果是，将能够解决疾病绘图中的更多问题，特别是当病例数低、聚集不规则以及存在时空相互作用时。