SPATIO-TEMPORAL EPIDEMIOLOGY: METHODS AND APPLICATIONS

时空流行病学：方法和应用

• 批准号: 8758573
• 负责人: JONATHAN C WAKEFIELD
• 金额: $ 22.62万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8758573
• 关键词: Area; Behavioral Risk Factor Surveillance System; Characteristics; Clinical; Collection; Communicable Diseases; Complex; Computer software; Data; Data Sources; Demographic and Health Surveys; Disease; Enterovirus; Epidemic; Epidemiologic Methods; Estimation Techniques; Evolution; Funding; Future; Genetic; Grant; HIV; Hand, Foot and Mouth Disease; Health; Individual; Infant Mortality; Infection; Intervention; Measures; Methodology; Methods; Modeling; Pattern; Population; Prevalence; Prevention strategy; Probability; Process; Public Health; Research Personnel; Resources; Risk; Ruta; Sample Size; Sampling; Scheme; Severity of illness; Space Models; Statistical Models; Surface; Surveys; System; Tanzania; Time; Tuberculosis; Vaccination; Vaccines; Variant; Viral; Weight; Writing; base; burden of illness; community intervention; design; flexibility; indexing; infant death; infectious disease model; insight; interest; novel; open source; pathogen; public health relevance; transmission process; user-friendly; vaccine efficacy

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.

描述（由申请人提供）：一个主要的公共卫生目的是提供准确的估计和对该地区疾病负担的预测；这项工作对于预防策略至关重要。该提案描述了用于建模空间和时间索引的健康数据的方法。前两个目的涉及传染病数据和基于复杂的调查数据的第三个目标小区域估计（SAE）。尽管在时空和空间中广泛收集了传染病数据，但在统计方法中仍存在重要差距，用于分析此类数据。具体而言，对于可用的建模选项既有有限的建模选项，也缺乏软件实现。因此，该项目的重点放在实际上适用的方法上，这些方法将基于现代贝叶斯平滑模型在可自由的软件中提供。关于传染病数据，提出了一个基于柔性样条的模型。这些方法将在许多发展中国家的传染病应用中开发，包括手和口腔疾病以及结核病。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结合在一起，以产生偏差和较低方差的估计值。这些模型将应用于行为风险因素监视系统（BRFSS）的数据，以及来自坦桑尼亚人口统计和健康调查（DHS）的婴儿死亡率和艾滋病毒数据，以回答重要的公共健康问题。