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：
Wei Wang
金额：
$ 9万
依托单位：
University of California-Los Angeles
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

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031187&HistoricalAwards=false
关键词：
RAPID Dynamic Graph Neural Networks

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.

新颖的冠状病毒Covid-19已成为人类历史上最大的大流行者之一，并对公共卫生，社会和经济产生了持久的影响。美国的案件数量已超过100万，死亡人数超过5万。迫切需要进行研究和发展，可以预测对病毒的传播有预测的理解，从而实现缓解方法来减轻Covid-19的负面影响。传统的流行病学模型通常仅考虑建立预测模型的少数功能，这可能无法捕获这种新大流行的各种干预机制的潜在风险因素和影响。在该项目中，研究人员开发了新型的机器学习方法，可以同时建模并预测COVID-19传播，检测和监测风险因素，并评估干预措施随时间和空间的有效性。新模型摄入并整合了跨不同来源的异质和迅速积累的数据，例如出版物，新闻，人口普查，社交媒体和爆发观察跟踪器。它采用一种新的上下文化语言模型来准确地识别庞大的文本数据的命名实体和关系，并构建知识图以提取潜在的风险因素。构建动态图。每个位置节点可能具有一组静态和时间相关的属性。事件，个人行为，社交活动，干预措施被映射到活动节点，并在当时连接到相应的位置节点。随着时间的推移，训练了一个新型的动态图神经网络，以对所有位置进行联合预测。重大注意力重量的活动节点代表主要的危险因素或有效的干预机制。该项目将导致对预测模型和所有源代码的公开传播，这立即受益于与Covid-12的战斗。

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

DOI：
10.1145/3580305.3599362
发表时间：
2023-07
期刊：
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Zijie Huang;Yizhou Sun;Wei Wang
通讯作者：
Zijie Huang;Yizhou Sun;Wei Wang

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

DOI：
发表时间：
2020-11
期刊：
ArXiv
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：
American journal of botany
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

Coupled Graph ODE for Learning Interacting System Dynamics

DOI：
10.1145/3447548.3467385
发表时间：
2021-01-01
期刊：
KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
影响因子：
0
作者：
Huang, Zijie;Sun, Yizhou;Wang, Wei
通讯作者：
Wang, Wei

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

DOI：
10.1098/rsta.2021.0125
发表时间：
2022-01-10
期刊：
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
影响因子：
0
作者：
Jiang JY;Zhou Y;Chen X;Jhou YR;Zhao L;Liu S;Yang PC;Ahmar J;Wang W
通讯作者：
Wang W

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Wei Wang其他文献

Synergistic antitumor efficacy of combined DNA vaccines targeting tumor cells and angiogenesis.

针对肿瘤细胞和血管生成的联合 DNA 疫苗的协同抗肿瘤功效。

DOI：
发表时间：
2015
期刊：
Biochemical and Biophysical Research Communications - BBRC
影响因子：
0
作者：
Xiaotao Yin;Wei Wang;Xiaoming Zhu;Yu Wang;Shuai Wu;Zicheng Wang;Lin Wang;Z. Du;Jiangping Gao;Ji
通讯作者：
Ji