Data & Parameters

数据

• 批准号: 8932702
• 负责人: Willem Gijsbert Van Panhuis
• 金额: $ 21.09万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8932702
• 关键词: Address; Childhood; Communicable Diseases; Communities; Computer Simulation; Computing Methodologies; Data; Data Set; Disease; Epidemiology; Genetic; Geography; Individual; Infection; Knowledge; Laws; Methods; Modeling; Natural History; Pathway interactions; Pattern; Policy Maker; Public Health; Readiness; Resolution; Resources; Time; United States; Vaccination; Vital Statistics; disease transmission; flexibility; health data; improved; infectious disease model; innovation; insight; pathogen; spatiotemporal; statistics; success; tool; transmission process

Infectious disease preparedness and control are critically dependent on knowledge of disease transmission patterns across space and time. The study of these patterns is greatly dependent on high-resolution public health data and adaptable tools for statistical inference. This Project aims to improve knowledge on spatiotemporal transmission patterns for infectious diseases in the United States and abroad leading to improved control strategies and better preparedness. This Project will contribute the first components of an integrated decision support pathway created by the MIDAS Center. It will create new insights in disease transmission patterns, develop innovative statistical and parameter estimation tools and will create a comprehensive high-resolution data resource for the MIDAS Network, policy makers, and the community at large. Specific aims are: 1) acquisition, curation, and integration of new large scale epidemiologic, vital statistics, and demographic data to improve the case-law of infectious disease dynamics, resulting in an essential data resource for modeling; 2) creation of innovative tools for the analysis of spatiotemporal disease transmission patterns, using spatiotemporal statistics and flexible non-linear parameter estimation methods; and 3) inferring individual level transmission dynamics from newly integrated disease data, contact patterns, and genetic data using advanced computational methods. This will result in new knowledge and scientific insight in the impact of pathogen natural history, vaccination, and other factors on spatiotemporal dynamics of distinct childhood infections and also in individual level transmission networks. This Project will shift the paradigm for the study of infectious disease transmission dynamics from ad hoc analyses of specific situations constrained by limited data availability to the systematic exploration of transmission dynamics for a wide range of pathogens and geographies. This Project will build on previous successes of the MIDAS Center to integrate and make available large scale public health datasets and to create innovative non-linear parameter estimation methods. This Project will advance the field by accelerating resources and tools for infectious disease modeling, resulting in better preparedness and control of existing and emerging threats

传染病的准备和控制严重取决于疾病传播的知识 跨时时间和时间的模式。这些模式的研究在很大程度上取决于高分辨率的公众 健康数据和适应性的统计推断工具。该项目旨在提高知识 美国和国外传染病的时空传播模式导致 改善控制策略和更好的准备。该项目将贡献 MIDAS中心创建的集成决策支持途径。它将在疾病中创造新的见解 传输模式，开发创新的统计和参数估计工具，并将创建一个 MIDAS网络，政策制定者和社区的全面高分辨率数据资源 大的。具体目的是：1）新大规模流行病学的获取，策划和整合至关重要 统计数据和人口统计数据，以改善传染病动态的判例法，从而导致 建模的基本数据资源； 2）创建用于分析时空分析的创新工具 疾病传播模式，使用时空统计和柔性非线性参数估计 方法; 3）从新综合疾病数据中推断个人水平传输动力学，接触 使用高级计算方法的模式和遗传数据。这将导致新知识和 对病原体自然历史，疫苗接种和其他因素对时空的影响的科学见解 不同儿童感染以及在个体水平传输网络中的动态。这个项目将 从特定的特定分析中移动范式以研究传染病传播动力学 受到有限的数据可用性限制的情况，以系统地探索传输动力学 广泛的病原体和地理位置。这个项目将基于MIDAS的先前成功 集成和提供大规模公共卫生数据集并创建创新的非线性的中心 参数估计方法。该项目将通过加速资源和工具来推进该领域 传染病建模，从而更好地准备和控制现有威胁和新兴威胁