Spatio-temporal data integration methods for infectious disease surveillance

传染病监测时空数据整合方法

• 批准号: 9311927
• 负责人: Howard H Chang
• 金额: $ 77.48万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311927
• 关键词: Address; Agreement; Architecture; Bayesian Method; Bayesian Modeling; Centers for Disease Control and Prevention (U.S.); Characteristics; China; Collaborations; Communicable Diseases; County; Data; Data Aggregation; Data Analyses; Databases; Dependence; Dependency; Detection; Diagnostic; Disease; Epidemiologic Monitoring; Epidemiology; Evaluation; Exhibits; Goals; Heterogeneity; Hookworm Infections; Hookworms; Incidence; Individual; Infection; Infectious Disease Epidemiology; Investments; Joints; Letters; Link; Malaria; Methods; Modeling; National Institute of Allergy and Infectious Disease; Outcome; Prevalence; Property; Province; Public Health; Research; Research Project Grants; Resources; Schistosomiasis; Severity of illness; Source; State Interests; Surveillance Methods; Surveillance Modeling; System; Time; Tuberculosis; Uncertainty; analytical tool; base; burden of illness; co-infection; data integration; data resource; design; diagnostic accuracy; disorder control; epidemiology study; experience; experimental study; flexibility; improved; innovation; interest; method development; national surveillance; novel; prevent; response; simulation; spatial temporal variation; spatiotemporal; statistics; surveillance data; surveillance strategy; theories; tool; trend

Project Summary/Abstract Effective surveillance systems are essential to developing targeted, efficient public health responses to infectious disease. In many domestic and global settings, multiple systems are being employed with varying degrees of overlap and with each system designed to achieve different surveillance objectives. The objective of this research project is to develop and distribute a spatio-temporal data integration toolset (“OPTI-SURVEIL”) for analyzing data from multiple surveillance systems, reducing spatial uncertainty by linking data across systems, space and time. With the support and guidance of the US CDC and China CDC (see letters of support), we will develop practical, multi-system analytical tools that will: (1) provide improved estimates of disease burden across space and time; (2) assess individual-level and system-level sensitivity and bias in case ascertainment; and (3) identify redundancies and gaps in current surveillance strategies. Approach: In Aim 1, we will develop spatial- temporal capture-recapture methods for performing data integration at the individual-level. The methods will account for individual-level heterogeneity in case ascertainment and key system-level parameters (sensitivity, bias, dependency). The methods will be extended to consider multiple diseases simultaneously, in order to address the increasing interest in the burden of co-infections. In Aim 2, we will develop spatial-temporal Bayesian hierarchical models for performing data integration using aggregated surveillance data that commonly arise from public surveillance databases. The models will exploit spatial-temporal dependence in county-level case numbers, account for spatial-temporal missing data due to system availability, and probabilistically incorporate information on ascertainment sensitivity and bias at the individual-level and at the system-level. In Aim 3, we will develop simulation-based methods to perform joint evaluation of multiple surveillance system designs. These methods will be applied to optimize a system’s design to maximize case detection while considering resource constraints, system sensitivity/bias, and the presence of other systems. We will apply OPTIM-SURVEIL to existing data in China, and will focus on four infectious diseases of global importance: tuberculosis (TB), malaria, schistosomiasis and hookworm. These diseases exhibit a diverse set of surveillance challenges, including diagnostic accuracy, increasingly rare case counts as elimination is approached, variability in disease severity, and challenges identifying key co-infections (e.g., TB and malaria). Expected Outcomes: The methods that we will develop and distribute will provide timely and practical tools for analyzing data from multiple disease surveillance systems. For the four target infections, we will answer specific questions about how information on specific surveillance architectures and properties—such as alternative spatial configurations of systems that have variable sensitivity and case ascertainment bias—can be used to improve the design of systems for achieving specific surveillance objectives. We will evaluate key tradeoffs in surveillance designs, and assess optimal surveillance approaches for solving particular public health challenges.

项目摘要/摘要 有效的监视系统对于发展有针对性的，有效的公共卫生反应至关重要 传染病。在许多国内和全球环境中，正在雇用多个系统 重叠程度，并且每个系统都旨在实现不同的监视目标。目的 该研究项目是为了开发和分发空间数据集成工具集（“ Opti-Surveil”） 分析了来自多个监视系统的数据，通过链接跨系统的数据来降低空间不确定性， 空间和时间。在美国疾病预防控制中心和中国CDC的支持和指导下（请参阅支持信），我们将 开发实用的多系统分析工具，该工具将：（1）改进了疾病燃烧的估计值 空间和时间； （2）在确定性的情况下，评估个人级别和系统级的灵敏度和偏见； （3） 确定当前监视策略中的冗余和差距。方法：在AIM 1中，我们将发展空间 - 用于在个体级别执行数据集成的临时捕获选项方法。方法将 如果确定和关键系统级参数（灵敏度， 偏见，依赖性）。这些方法将扩展到简单考虑多种疾病，以便 解决了对共同感染的伯宁的兴趣日益增长的兴趣。在AIM 2中，我们将发展时空贝叶斯 使用汇总的监视数据进行数据集成的分层模型，这些模型通常由 公共监视数据库。这些模型将利用县级案件中的空间依赖性 数字，考虑到系统可用性引起的时空丢失数据，并且可能合并 有关确定性敏感性和偏见在个人级别和系统级别的信息。在AIM 3中，我们将 基于开发模拟的方法，以执行多个监视系统设计的联合评估。这些 将应用方法来优化系统的设计，以最大程度地提高案例检测 约束，系统灵敏度/偏见以及其他系统的存在。我们将在 中国的现有数据将重点关注四种全球重要性的感染疾病：结核病（TB），疟疾， 血吸虫病和钩虫。这些疾病表现出各种各样的监视挑战，包括 诊断准确性，随着进化的进化，越来越罕见的病例计数，疾病严重程度的变异性， 以及确定关键共同感染的挑战（例如，结核病和疟疾）。预期结果：我们的方法 将开发和分发将提供及时，实用的工具，用于分析来自多种疾病的数据 监视系统。对于四种目标感染，我们将回答有关信息的特定问题 特定的监视体系结构和属性，例如系统的替代空间配置 具有可变的灵敏度和病例确定偏见 - 可用于改善系统的设计 实现特定的监视目标。我们将评估监视设计和评估中的关键权衡 解决特定公共卫生挑战的最佳监视方法。