Data & Parameters

传染病的预防和控制在很大程度上取决于疾病传播的知识。 跨越空间和时间的模式。对这些模式的研究在很大程度上依赖于高分辨率的公众 健康数据和适用的统计推断工具。该项目旨在提高对以下方面的认识 美国和国外传染病的时空传播模式导致 改进控制战略和更好的准备。该项目将贡献一个 MIDAS中心创建的综合决策支持路径。它将对疾病产生新的见解 传播模式，开发创新的统计和参数估计工具，并将创建 为MIDAS网络、决策者和社区提供全面的高分辨率数据资源，网址为 大号的。具体目标是：1)获取、管理和整合新的大规模流行病学、至关重要的 统计数据和人口数据，以改进传染病动态的判例法，导致 用于建模的基本数据资源；2)创建用于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；以及3)从新整合的疾病数据中推断个体水平的传播动力学 模式，以及使用先进计算方法的遗传数据。这将带来新的知识和 对病原体自然病史、疫苗接种和其他因素对时空的影响的科学见解 不同的儿童感染的动态以及个人一级的传播网络。该项目将 改变传染病传播动力学研究的范式，从对特定疾病的特殊分析转向 受数据可获得性有限限制的情况下，需要系统地探索 广泛的病原体和地理位置。该项目将建立在以前的MIDAS成功的基础上 中心整合和提供大规模公共卫生数据集，并创建创新的非线性 参数估计方法。该项目将通过加速以下方面的资源和工具来推动该领域的发展 传染病模型，从而更好地防备和控制现有和新出现的威胁

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