RAPID: COVID-19-Net: Integrating Health, Pathogen and Environmental Data into a Knowledge Graph for Case Tracking, Analysis, and Forecasting

RAPID：COVID-19-Net：将健康、病原体和环境数据集成到知识图谱中以进行病例跟踪、分析和预测

• 批准号: 2028411
• 负责人: Peter Rose
• 金额: $ 20万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028411&HistoricalAwards=false
• 关键词: RAPID; COVID; 19; Net; Integrating

This COVID-19 RAPID research program could help answer several key questions about the pandemic, such as “What characteristics are in common among outbreaks in the US and other locations?”, “How different are viral strains, and how these differences affect transmission in different regions?”, or “How similar or different the patterns of the coronavirus infection spread and its impacts are compared to previous pandemics?” The multitude of diverse COVID-19-related data streams, which are rapidly made available online with little coordination or reliance on common standards, creates enormous challenges for researchers trying to answer such questions today, and analyze and predict patterns of the pandemic in its cross-disciplinary complexity. This project will do so by linking diverse information about pathogens, health data, and environmental indicators into a common knowledge graph, to let researchers trace the virus in different geographic conditions and provide input into effective intervention policies. The core technical component of the project is a COVID-19 knowledge graph, which represents linked facts about different aspects of the pandemic, from scientific literature and large data collections and models. The project will develop a novel methodology for creating a knowledge graph from disparate sources describing pathogens, population health, environmental conditions, and critical infrastructure components. The graph will be created in coordination with several partner projects working on the NSF Open Knowledge Network. This will ensure that additional knowledge domains can be integrated to support cross-disciplinary analysis. The results of the collaborative knowledge network development will be published as a continuously updated online dashboard for the general public and a collection of Jupyter Notebooks, open source software to enable interactive computing, to help researchers explore various aspects of the knowledge graph and improve our understanding of how the virus is spread and what intervention strategies are potentially most effective.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个COVID-19快速研究计划可以帮助回答有关大流行的几个关键问题，例如“美国和其他地方的疫情有哪些共同特征？"，病毒株有何不同，这些差异如何影响不同地区的传播？或者“冠状病毒感染传播的模式及其影响与以前的大流行病相比有何相似或不同？”大量不同的COVID-19相关数据流在网上迅速发布，几乎没有协调或依赖共同标准，这给今天试图回答这些问题的研究人员带来了巨大的挑战，并在跨学科的复杂性中分析和预测大流行的模式。该项目将通过将有关病原体，健康数据和环境指标的各种信息链接到一个共同的知识图中，让研究人员在不同的地理条件下追踪病毒，并为有效的干预政策提供投入。该项目的核心技术组成部分是COVID-19知识图谱，该图谱从科学文献和大型数据收集和模型中反映了有关大流行不同方面的相关事实。该项目将开发一种新的方法，用于从描述病原体、人群健康、环境条件和关键基础设施组件的不同来源创建知识图。该图表将与NSF开放知识网络上的几个合作伙伴项目协调创建。这将确保能够整合更多的知识领域，以支持跨学科分析。合作开发知识网络的成果将作为一个不断更新的在线仪表板向公众公布，并作为一个能够进行互动计算的开放源码软件-一个“计算机笔记本”汇编，帮助研究人员探索知识图谱的各个方面，提高我们对病毒如何传播以及哪些干预策略可能最有效的理解。该奖项反映了NSF的法定使命并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。