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

传染病的防范和控制在很大程度上取决于对疾病传播的了解 跨越空间和时间的模式。这些模式的研究很大程度上依赖于高分辨率的公共 健康数据和用于统计推断的适应性工具。该项目旨在提高相关知识 美国和国外传染病的时空传播模式导致 改进控制策略并做好准备。该项目将贡献一个 由 MIDAS 中心创建的综合决策支持路径。它将创造对疾病的新见解 传输模式，开发创新的统计和参数估计工具，并将创建一个 为 MIDAS 网络、政策制定者和社区提供全面的高分辨率数据资源 大的。具体目标是：1）获取、管理和整合新的大规模流行病学、重要的 统计数据和人口数据，以改善传染病动态的判例法，从而产生 建模的重要数据资源； 2）创建时空分析的创新工具 疾病传播模式，使用时空统计和灵活的非线性参数估计 方法; 3）从新整合的疾病数据、接触者推断个体水平的传播动态 使用先进的计算方法的模式和遗传数据。这将带来新的知识和 关于病原体自然史、疫苗接种和其他因素对时空影响的科学见解 不同儿童感染的动态以及个人层面的传播网络。该项目将 改变传染病传播动力学研究的范式，从特定的临时分析 受数据可用性有限的情况限制，无法对传输动态进行系统探索 病原体的范围和地域范围广泛。该项目将建立在 MIDAS 先前成功的基础上 整合和提供大规模公共卫生数据集并创建创新的非线性数据中心 参数估计方法。该项目将通过加速资源和工具来推动该领域的发展 传染病建模，从而更好地准备和控制现有和新出现的威胁