EAGER: Collaborative Research: Combining Community and Clinical Data for Augmenting Influenza Modeling

EAGER：合作研究：结合社区和临床数据增强流感模型

• 批准号: 1643623
• 负责人: Jeffrey Shaman
• 金额: $ 11.9万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1643623&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Combining; Community

This EAGER represents timely and essential exploratory work assessing the value of community-sourced data in infectious disease modeling efforts. Community-generated data can suffer from lack of information about the reference population, which hinders prevalence estimates. In theory, real-time and near real-time community-sourced data has been recognized to offer important opportunity to improve timeliness and scope of infectious disease modeling efforts, but there are still fundamental questions regarding the value of community infection data for understanding, monitoring and forecasting. Towards this, work here will study how community and clinically generated data compare regarding measures of disease incidence, contributing population demographics, and spatio-temporal coverage in influenza dynamics. Public dissemination of our research and findings will help expose and educate the community in data generation and forecasting efforts.This project involves a rigorous and systematic comparison between contemporaneous community and clinical data on acute respiratory infections. The goal of this work will be to first generate a diverse community-sourced data set with a defined reference population. We will then assess significance of outcomes between groups in community and clinical data, accounting for demographic and epidemiological factors. Dynamical modeling and Bayesian inference methods will be used to develop and augment disease forecasts. Normalized and municipal scale estimates from the community samples will be integrated and the data generation and modeling efforts will together be used to assess the impact of community data on real-time and near-real time simulations and forecasts. The high-risk work can potentially be paradigm shifting regarding how we collect and use data in forecasting methods for disease as well as a broader range of societal issues.

EAGER代表了及时和必要的探索性工作，评估社区来源的数据在传染病建模工作中的价值。社区产生的数据可能因缺乏关于参考人口的信息而受到影响，这妨碍了流行率估计。从理论上讲，实时和近实时社区来源的数据已被公认为提高传染病建模工作的及时性和范围的重要机会，但关于社区感染数据在理解，监测和预测方面的价值仍然存在根本性问题。为此，这里的工作将研究社区和临床产生的数据如何比较疾病发病率的措施，人口统计学，时空覆盖流感动态。公开传播我们的研究和发现将有助于在数据生成和预测工作方面向社区进行宣传和教育。该项目涉及对急性呼吸道感染的同期社区和临床数据进行严格和系统的比较。这项工作的目标将是首先生成一个具有确定的参考人群的来自不同社区的数据集。然后，我们将评估社区和临床数据中各组之间结果的重要性，考虑人口统计学和流行病学因素。动态建模和贝叶斯推理方法将用于发展和增强疾病预测。来自社区样本的标准化和市政规模估计将被整合，数据生成和建模工作将共同用于评估社区数据对实时和近实时模拟和预测的影响。高风险工作可能会改变我们如何收集和使用数据预测疾病以及更广泛的社会问题的模式。