RAPID: Dynamic Graph Neural Networks for Modeling and Monitoring COVID-19 Pandemic

RAPID:用于建模和监测 COVID-19 大流行的动态图神经网络

基本信息

  • 批准号:
    2031187
  • 负责人:
  • 金额:
    $ 9万
  • 依托单位:
  • 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:
    2020-06-01 至 2023-05-31
  • 项目状态:
    已结题

项目摘要

The novel coronavirus, COVID-19, has become one of the biggest pandemics in human history and has generated lasting impacts on public health, society, and economy. The number of cases in the United States has passed 1 million with a total number of deaths over 50 thousand. There is an urgent need for research and development that can bring a predictive understanding of the spread of the virus, thereby enabling mitigation methods to alleviate the negative effects of COVID-19. Traditional epidemiological models usually take into consideration only a small number of features in building a prediction model, which may not be able to capture potential risk factors and effects of various intervention mechanisms of this new pandemic. In this project the investigators develop novel machine learning methods that can simultaneously model and predict the COVID-19 spread, detect and monitor risk factors, and evaluate effectiveness of interventions over time and space. The new model ingests and integrates heterogeneous and rapidly accumulating data across diverse sources, such as publications, news, census, social media, and outbreak observation trackers. It employs a new contextualized language model to accurately recognize named entities and relations from vast text data and build knowledge graphs to extract potential risk factors. A dynamic graph is constructed. Each location node may have a set of static and time-dependent attributes. Events, individual behaviors, social activities, interventions are mapped to activity nodes with edges connecting to the corresponding location nodes at the time. A novel dynamic graph neural network is trained to perform joint predictions of all locations over time. Activity nodes of significant attention weights represent major risk factors or effective intervention mechanisms. The project will result in public dissemination of the prediction model and all source codes, immediately benefiting the combat against COVID-19.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.
新型冠状病毒COVID-19已成为人类历史上最大的流行病之一,并对公共卫生、社会和经济产生了持久影响。美国的病例数已超过100万例,总死亡人数超过5万人。迫切需要进行研究和开发,以便对病毒的传播进行预测性了解,从而使缓解方法能够减轻COVID-19的负面影响。传统的流行病学模型在构建预测模型时通常只考虑少数特征,可能无法捕捉这种新流行病的潜在危险因素和各种干预机制的效果。在这个项目中,研究人员开发了新的机器学习方法,可以同时建模和预测COVID-19的传播,检测和监测风险因素,并评估干预措施在时间和空间上的有效性。新模型吸收并整合了不同来源的异构和快速积累的数据,如出版物,新闻,人口普查,社交媒体和疫情观察跟踪器。它采用了一种新的上下文语言模型,从大量的文本数据中准确识别命名实体和关系,并构建知识图来提取潜在的风险因素。构造了一个动态图。每个位置节点可以具有一组静态和时间相关属性。事件、个人行为、社会活动、干预被映射到活动节点,其中边连接到当时对应的位置节点。一种新的动态图神经网络被训练来随着时间的推移对所有位置进行联合预测。具有显著关注权重的活动节点代表主要的风险因素或有效的干预机制。该项目将导致预测模型和所有源代码的公开传播,立即有利于对抗COVID-19。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。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.
COVID-19-19S监视者:朝着基于社交媒体挖掘的基于强大而有效的大流行监视系统。
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
  • DOI:
  • 发表时间:
    2020-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zijie Huang;Yizhou Sun;Wei Wang-
  • 通讯作者:
    Zijie Huang;Yizhou Sun;Wei Wang-
DICE: Data-Efficient Clinical Event Extraction with Generative Models
  • DOI:
    10.48550/arxiv.2208.07989
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    3
  • 作者:
    Mingyu Derek Ma;Alex S. Taylor;Wei Wang;Nanyun Peng
  • 通讯作者:
    Mingyu Derek Ma;Alex S. Taylor;Wei Wang;Nanyun Peng
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Wei Wang其他文献

A High-Performance Isolated High-Frequency Converter With Optimal Switch Impedance
具有最佳开关阻抗的高性能隔离式高频转换器
Cambrian magmatic flare-up, central Tibet: Magma mixing in proto-Tethyan arc along north Gondwanan margin
西藏中部寒武纪岩浆爆发:沿冈瓦南边缘北缘的原特提斯弧中岩浆混合
  • DOI:
    10.1130/b35859.1
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Peiyuan Hu;Qingguo Zhai;Peter A. Cawood;Guochun Zhao;Jun Wang;Yue Tang;Zhicai Zhu;Wei Wang;Hao Wu
  • 通讯作者:
    Hao Wu
Spatial resolution comparison of AC-SECM with SECM and their characterization of self-healing performance of hexamethylene diisocyanate trimer microcapsule coatings
AC-SECM与SECM的空间分辨率比较及其对六亚甲基二异氰酸酯三聚体微胶囊涂层自修复性能的表征
  • DOI:
    10.1039/c5ta00529a
  • 发表时间:
    2015-02
  • 期刊:
  • 影响因子:
    11.9
  • 作者:
    Wei Wang;Likun Xu;Huyuan Sun;Xiangbo Li;Shouhuan Zhao;Weining Zhang
  • 通讯作者:
    Weining Zhang
Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp
机器学习算法在智能车辆驶出匝道换道模型中的应用
  • DOI:
    10.1080/23249935.2020.1746861
  • 发表时间:
    2020-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Changyin Dong;Hao Wang;Ye Li;Xiaomeng Shi;Daiheng Ni;Wei Wang
  • 通讯作者:
    Wei Wang
Financial development and wage income: Evidence from the global football market
金融发展与工资收入:来自全球足球市场的证据
  • DOI:
    10.1016/j.jbankfin.2023.106813
  • 发表时间:
    2023-02
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Wei Wang;Haoxi Yang;Xi Wang
  • 通讯作者:
    Xi Wang

Wei Wang的其他文献

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{{ truncateString('Wei Wang', 18)}}的其他基金

CAREER: Harnessing the Interplay of Morphology, Viscoelasticity, and Surface-Active Agents to Modulate Soft Wetting
职业:利用形态、粘弹性和表面活性剂的相互作用来调节软润湿
  • 批准号:
    2336504
  • 财政年份:
    2024
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
An Educational Tool for Teaching and Learning Concurrent Computer Programming Techniques
用于教授和学习并行计算机编程技术的教育工具
  • 批准号:
    2215359
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Small: Exploiting Performance Correlations for Accurate and Low-cost Performance Testing for Serverless Computing
协作研究:SHF:小型:利用性能相关性对无服务器计算进行准确且低成本的性能测试
  • 批准号:
    2155096
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: EAGER: Enhancing Security and Privacy of Augmented Reality Mobile Applications through Software Behavior Analysis
合作研究:EAGER:通过软件行为分析增强增强现实移动应用程序的安全性和隐私性
  • 批准号:
    2221843
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence
PIPP 第一阶段:利用开源情报的端到端流行病预警系统
  • 批准号:
    2200274
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Enhancing Programming and Machine Learning Education for Students with Visual Impairments through the Use of Compilers, AI and Cloud Technologies
通过使用编译器、人工智能和云技术加强对视力障碍学生的编程和机器学习教育
  • 批准号:
    2202632
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: A Bioinspired Approach towards Sustainable Membranes for Resilient Brine Treatment
合作研究:用于弹性盐水处理的可持续膜的仿生方法
  • 批准号:
    2226501
  • 财政年份:
    2022
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
III: Medium: Collaborative Research: Collaborative Machine-Learning-Centric Data Analytics at Scale
III:媒介:协作研究:以机器学习为中心的大规模协作数据分析
  • 批准号:
    2106859
  • 财政年份:
    2021
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative Research; RUI: Non-Orthogonal Multiple Access Pricing for Wireless Multimedia Communications
合作研究;
  • 批准号:
    2010284
  • 财政年份:
    2020
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
SusChEM: Direct functionalization of aldehydes enabled by aminocatalysis
SusChEM:通过氨基催化实现醛的直接官能化
  • 批准号:
    1903983
  • 财政年份:
    2019
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant

相似国自然基金

Dynamic Credit Rating with Feedback Effects
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
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相似海外基金

CAREER: Theory for Dynamic Graph Algorithms
职业:动态图算法理论
  • 批准号:
    2238138
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative Research: NCS-FO: Dynamic Brain Graph Mining
合作研究:NCS-FO:动态脑图挖掘
  • 批准号:
    2319450
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Collaborative Research: NCS-FO: Dynamic Brain Graph Mining
合作研究:NCS-FO:动态脑图挖掘
  • 批准号:
    2319451
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
Collaborative Research: NCS-FO: Dynamic Brain Graph Mining
合作研究:NCS-FO:动态脑图挖掘
  • 批准号:
    2319449
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
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NSF-CSIRO: Towards Interpretable and Responsible Graph Modeling for Dynamic Systems
NSF-CSIRO:迈向动态系统的可解释和负责任的图形建模
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  • 财政年份:
    2023
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    $ 9万
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    2213658
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    2022
  • 资助金额:
    $ 9万
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动态图上的图信号处理
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    2768264
  • 财政年份:
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网络中的图中心性度量和动态过程
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COLOURING, DOMINATION AND DISCRETE DYNAMIC GRAPH PROCESSES
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  • 财政年份:
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