Inference for complex epidemiological problems: censoring, mismeasurement, and high-dimensional problems

复杂流行病学问题的推理：审查、误测和高维问题

• 批准号: RGPIN-2022-05164
• 负责人: Brown, Patrick
• 金额: $ 1.53万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765682
• 关键词: Inference; complex; epidemiological; problems; censoring

This project will develop new statistical models and associated inference methods to address the complex epidemiological problems being encountered by the latest wave of research in global health, emerging infectious diseases, and environmental health. Three streams of research comprise this project. First, new for variance matrices of spatio-temporal processes will be developed. Second, inferential methods based on partial likelihoods and posterior approximations will be created for hierarchical models relating daily air quality to population-level health outcomes. Third, methods for studying COVID-19 with serosurveys will be developed. The research will have an immediate impact on three applied research programs which the project team will be embedded in. These projects include: building a new air quality warning system with Health Canada; estimating the prevalence and impact of COVID-19 in Canada with the Ab-C study; and understanding the factors influencing global mortality as part of the Centre for Global Health Research (CGHR). - Space-time: Building on Brown and Stafford (2021), spectral representations of spatio-temporal covariance functions will be leveraged to build inferential algorithms for large datasets at high spatial resolutions. These approximations will be used with models for aggregated spatial point processes, such as publicly reported health outcomes. - Case-crossover models: These models are a convenient and effective way of quantifying short-term effects of air pollution, where each death is grouped with a number of `control days' on previous weeks. Following on from Stringer, Brown and Stafford (2021a) and Zhang et al. (2021), case-crossover models will be extended to incorporate overdispersion and hierarchical non-linear effects. - Bayesian computation: The models developed will all need to deviate from the conventional `conditionally-independent' and `latent-Gaussian' specification in important ways, which makes inference challenging. Approximations from Stringer et al (2021b) will be improved through the use of multivariate skew-Normal densities, in place of the current mixture of Normals. An inner optimization step which is performed multiple times will be improved with the use of an EMS algorithm. - Infectious diseases: Reported cases cover only a fraction of infected individuals, and a longitudinal dried blood spot survey measuring seroprevalence has been undertaken by CGHR to obtain a more accurate picture of the nature of the disease in Canada. Test sensitivity is far from perfect and uncertainty in case ascertainment must be adjusted for. In addition to methodological research papers, the work will result in open-source statistical software distributed as R packages for use by the wider research community.

该项目将开发新的统计模型和相关的推理方法，以解决全球卫生、新出现的传染病和环境卫生研究的最新浪潮所遇到的复杂流行病学问题。这个项目包括三个方面的研究。首先，开发新的时空过程方差矩阵。其次，基于部分似然和后验近似的推理方法将为日常空气质量与人口水平健康结果相关的分层模型创建。三是建立基于血清调查的新冠肺炎研究方法。该研究将对项目团队将参与的三个应用研究项目产生直接影响。这些项目包括：与加拿大卫生部建立一个新的空气质量预警系统；利用Ab-C研究估计COVID-19在加拿大的流行率和影响；以及了解影响全球死亡率的因素，作为全球卫生研究中心的一部分。-时空：在Brown和Stafford（2021）的基础上，将利用时空协方差函数的频谱表示来构建高空间分辨率大型数据集的推理算法。这些近似值将用于汇总空间点过程的模型，例如公开报告的健康结果。-病例交叉模型：这些模型是量化空气污染短期影响的一种方便而有效的方法，其中每个死亡与前几周的一些“对照日”分组。继Stringer， Brown and Stafford （2021a）和Zhang等人（2021）之后，案例交叉模型将得到扩展，以纳入过分散和分层非线性效应。-贝叶斯计算：开发的模型都需要在重要方面偏离传统的“条件独立”和“潜在高斯”规范，这使得推理具有挑战性。Stringer等人（2021b）的近似将通过使用多元偏正态密度来代替当前的正态混合来改进。使用EMS算法可以改进多次执行的内部优化步骤。传染病：报告的病例仅覆盖一小部分感染者，加拿大人权中心进行了一项纵向干血点调查，测量血清患病率，以便更准确地了解加拿大这种疾病的性质。检测灵敏度还远远不够完善，必须对病例确定的不确定性进行调整。除了方法学研究论文之外，这项工作还将产生开源统计软件，以R包的形式分发给更广泛的研究社区。