RAPID: COVID-19 Coronavirus Testbed and Knowledge Base Construction and Personalized Risk Evaluation

RAPID：COVID-19冠状病毒测试平台和知识库建设以及个性化风险评估

• 批准号: 2027339
• 负责人: Xingquan Zhu
• 金额: $ 9万
• 依托单位: Florida Atlantic University
• 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: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027339&HistoricalAwards=false
• 关键词: RAPID; COVID; 19; Coronavirus; Testbed

The 2019 novel coronavirus disease (COVID-19) is an evolving epidemic. There is little knowledge about COVID-19’s outbreak and spread patterns, and the impact of viral evolution, demography, social behavior, cultural differences, and quarantine policies on the outbreaks. As the battle against COVID-19 continues, a deluge of information is being produced. Academia, news agencies, and governments continuously publish advances in the understanding of the virus clinical pathologies, its genome sequences, and relevant administrative policies and actions taken. Nevertheless, the dramatic outbreak differences with respect to diverse geographies, regional policies, and cultural groups also raise confusion, contradictions, and inconsistencies in disease outbreak modeling. It is therefore crucial to build a knowledge base of COVID-19 to understand the correlations and roles that different factors play in predicting the spread of the virus, thus enabling both individuals and health care officials to implement appropriate policies to mitigate the effects of the epidemic on public health and society at large. This project will create a COVID-19 coronavirus testbed and knowledge base, as well as a personalized risk evaluation tool for individuals to assess their infection risk in a dynamic environment. The technical aims of the project include two thrusts. The first creates a testbed and knowledgebase that includes information for modeling outbreak and mutation of COVID-19. This testbed will serve as a benchmark for the public to model and understand the spread of COVID-19, and eventually mitigate the negative effects of COVID-19 on public health, society, and the economy. The second thrust develops a multi-source deep neural network-based predictive tool to combine demographics, policies, regional infections, and individual information for personalized risk evaluation. As a result, the public can employ personalized information to estimate their infection risk level, using social and behavioral information (e.g., family size, shopping patterns, and dining patterns), local authority policies (e.g., school, restaurant, and movie theater closures as well as night time curfew), demographics (e.g., population age, density, and income), health condition (e.g., heart disease incidence, cancer prevalence, and substance abuse), and regional virus condition (e.g., number of infection cases in the region studied and infection rate).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.

2019 年新型冠状病毒病 (COVID-19) 是一种不断演变的流行病，人们对 COVID-19 的爆发和传播模式以及病毒进化、人口统计、社会行为、文化差异和检疫政策对疫情的影响知之甚少。随着抗击 COVID-19 的斗争持续进行，学术界、新闻机构和政府不断发布对病毒临床病理学、基因组序列以及相关管理政策和行动的了解进展。这不同地理位置、区域政策和文化群体之间的巨大疫情差异也会引起疾病爆发模型的混乱、矛盾和不一致，因此建立 COVID-19 知识库以了解不同因素之间的相关性和作用至关重要。在预测病毒的传播方面发挥作用，从而使个人和卫生保健官员能够实施适当的政策，以减轻该流行病对公共卫生和整个社会的影响。该项目将创建一个 COVID-19 冠状病毒测试平台和知识库，以及个性化的风险评估该项目的技术目标包括两个重点：创建一个测试平台和知识库，其中包含用于对 COVID-19 爆发和突变进行建模的信息。第二个重点是开发一种基于多源深度神经网络的预测工具，以帮助公众建模和了解 COVID-19 的传播，并最终减轻 COVID-19 对公共卫生、社会和经济的负面影响。结合人口统计、政策、地区感染情况和因此，公众可以利用个性化信息，利用社会和行为信息（例如家庭规模、购物模式和就餐模式）、地方当局政策（例如学校）来估计自己的感染风险水平。 、餐厅和电影院关闭以及夜间宵禁）、人口统计数据（例如人口年龄、密度和收入）、健康状况（例如心脏病发病率、癌症患病率和药物滥用）以及区域病毒状况（例如，所研究地区的感染病例数和感染率）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

BalLeRMix +: mixture model approaches for robust joint identification of both positive selection and long-term balancing selection

BalLeRMix：用于正选择和长期平衡选择的稳健联合识别的混合模型方法

• DOI: 10.1093/bioinformatics/btab720
• 发表时间: 2021-10
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Cheng, Xiaoheng;DeGiorgio, Michael;Schwartz, ed., Russell
• 通讯作者: Schwartz, ed., Russell

Community and topic modeling for infectious disease clinical trial recommendation

传染病临床试验推荐的社区和主题建模

• DOI: 10.1007/s13721-021-00321-7
• 发表时间: 2021-12
• 期刊: Network Modeling Analysis in Health Informatics and Bioinformatics
• 作者: Elkin, Magdalyn E.;Zhu, Xingquan
• 通讯作者: Zhu, Xingquan

OpenWGL: open-world graph learning for unseen class node classification

OpenWGL：用于看不见的类节点分类的开放世界图学习

• DOI: 10.1007/s10115-021-01594-0
• 发表时间: 2021-09
• 期刊: Knowledge and Information Systems
• 影响因子: 2.7
• 作者: Wu, Man;Pan, Shirui;Zhu, Xingquan
• 通讯作者: Zhu, Xingquan

Multi-Label Graph Convolutional Network Representation Learning

多标签图卷积网络表示学习

• DOI: 10.1109/tbdata.2020.3019478
• 发表时间: 2019-12-26
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Min Shi;Yufei Tang;Xingquan Zhu;Jianxun Liu
• 通讯作者: Jianxun Liu

A Machine Learning Study of COVID-19 Serology and Molecular Tests and Predictions

COVID-19 血清学、分子测试和预测的机器学习研究

• 发表时间: 2022-01
• 期刊: Smart health
• 作者: Elkin, Magdalyn E.;Zhu, Xingquan
• 通讯作者: Zhu, Xingquan