SPATIO-TEMPORAL EPIDEMIOLOGY: METHODS AND APPLICATIONS

描述（由申请人提供）：一个主要的公共卫生目标是对地区一级的疾病负担提供准确的估计和预测；这一努力对预防战略至关重要。本建议描述了空间和时间索引健康数据建模的方法。前两个目标涉及传染病数据，第三个目标是基于复杂调查数据的小区域估计（SAE）。尽管在时间和空间上广泛收集传染病数据，但在分析这些数据的统计方法方面存在重大差距。具体来说，建模选项的数量有限，并且缺乏可用的软件实现。因此，本项目的重点是基于现代贝叶斯平滑模型的实用方法，这些方法将在免费软件中提供。针对传染病数据，提出了一种基于柔性样条的模型。这些方法将在一些发展中国家传染病应用的背景下开发，包括手足口病和结核病。对于SAE，提出的方法将基于设计的估计技术与空间平滑先验相结合，以产生低偏差和低方差的估计。这些模型将应用于行为风险因素监测系统（BRFSS）的数据以及坦桑尼亚人口与健康调查（DHS）的婴儿死亡率和艾滋病毒数据，以便回答重要的公共卫生问题。

DESCRIPTION (provided by applicant): A major public health aim is to provide accurate estimates and predictions of disease burden at the area level; this endeavor is vital for prevention strategies. This proposal describes methods for modeling spatially and temporally indexed health data. The first two aims concern infectious disease data and the third aim small-area estimation (SAE) based on complex survey data. Despite the widespread collection of infectious disease data in time and space there are important gaps in statistical methodology for the analysis of such data. Specifically, there are both a limited number of modeling options and a lack of software implementations for those that are available. Hence, the emphasis in this project is on practically applicable methods that will be made available within freely-available software, based on modern Bayesian smoothing models. With respect to infectious disease data, a flexible spline- based model is proposed. The methods will be developed in the context of a number of developing world infectious disease applications including hand, foot and mouth disease and tuberculosis. For SAE the proposed approach combines design-based estimation techniques with spatial smoothing priors, to produce estimates with both low bias and low variance. These models will be applied to data from the Behavioral Risk Factor Surveillance System (BRFSS) and to infant mortality and HIV data from the Tanzania demographic and health survey (DHS), in order to answer important public health questions.