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提供准确的估计和预测的疾病负担在地区一级;这一奋进是至关重要的预防战略。该提案描述了用于对空间和时间索引的健康数据进行建模的方法。前两个目标涉及传染病数据，第三个目标是根据复杂的调查数据进行小面积估计。尽管在时间和空间上广泛收集了传染病数据，但在分析这些数据的统计方法上存在重大差距。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，在这个项目中的重点是实际适用的方法，将在免费提供的软件，现代贝叶斯平滑模型的基础上提供。针对传染病数据，提出了一种基于柔性样条的传染病模型.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，所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，以产生具有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的数据和坦桑尼亚人口和健康调查的婴儿死亡率和艾滋病毒数据，以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.