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.

这一渴望代表了评估社区源数据在传染病建模工作中的价值的及时且必不可少的探索工作。社区生成的数据可能缺乏有关参考人群的信息，这阻碍了患病率的估计。从理论上讲，已经认识到实时和接近实时的社区数据，可以提供重要的机会来提高传染病建模工作的及时性和范围，但是关于社区感染数据的价值仍然存在基本问题，以理解，监测和预测。为此，这里的工作将研究社区和临床生成的数据如何相比，有关疾病发生率的测量，促进人群人群以及流感动力学中的时空覆盖率。公众对我们的研究和发现的传播将有助于揭露和教育社区在数据生成和预测工作中。该项目涉及同时社区与急性呼吸道感染的临床数据之间进行严格而系统的比较。这项工作的目的是首先生成具有定义参考人群的社区源数据集。然后，我们将评估社区和临床数据中群体之间结果的重要性，这考虑了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。将整合来自社区样本的归一化和市政量表的估计，数据生成和建模工作将共同评估社区数据对实时和近乎现实的时间模拟和预测的影响。关于我们如何在疾病预测方法中收集和使用数据以及更广泛的社会问题，高危工作可能是范式转移。