Collaborative Research: RAPID: Building a Spatiotemporal Platform for Rapid Response to COVID-19

合作研究：RAPID：构建快速响应 COVID-19 的时空平台

• 批准号: 2027521
• 负责人: Chaowei Yang
• 金额: $ 10万
• 依托单位: George Mason 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: 2020-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027521&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Building; Spatiotemporal

The Spatiotemporal Innovation IUCRC develops novel spatiotemporal analytical tools to enable applications of national and global significance. In response to the COVID-19 crisis, Harvard University and George Mason University, university sites within this IUCRC, propose this collaborative project to collect and share COVID related data in near real time, conduct spatiotemporal analytics, and mine socioeconomic and environmental knowledge to facilitate decision support systems in response to the pandemic. This project will build a unique cloud-based platform composed of a data collection subsystem for collecting global, high quality COVID-19-related data; spatiotemporal analytics tools for analyzing the disease evolution and socioeconomic patterns; and, modeling tools for assessing medical supplies and logistics. Through web access services, the platform will provide capabilities for easy access to the data collected as well as access to the developed spatiotemporal analytical and modeling tools. Such capabilities will facilitate quick production of data-driven decision support systems for community preparedness. This project has secured participation of 50+ international researchers in developing the proposed platform. These researchers will help collect and validate data, analyze how policies influence the outbreaks, how the Earth environment is impacted, and how to balance reopening of the economy and controlling the spreading of the disease in the U.S. based on experiences from Asia and Europe. Over 200 undergraduate volunteers, including many from underrepresented groups, are already involved in this project through Harvard’s Coronavirus Visualization Team efforts. Data, information, and knowledge accumulated in this project have been, and will continue to be, archived long term in a comprehensive gateway (covid-19.stcenter.net). Such data include spatiotemporal distribution of confirmed cases, relevant social, economic and natural information from different resources, such as authoritative reports, news releases, Earth observation, and social media. Software and tools developed are posted on GitHub for open access. Sustained online collaboration is being conducted to produce replicable research using spatiotemporal analyses to mine patterns and relations between COVID-19 and social and natural factors for community response and preparedness.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.

时空创新 IUCCRC 开发新颖的时空分析工具，以实现具有国家和全球意义的应用 为了应对 COVID-19 危机，IUCRC 内的哈佛大学和乔治梅森大学提出了这个合作项目，以收集和共享与新冠病毒相关的信息。该项目将建立一个独特的基于云的平台，该平台由用于收集全球高水平数据的数据收集子系统组成。与 COVID-19 相关的高质量数据；用于分析疾病演变和社会经济模式的时空分析工具；以及用于评估医疗用品和物流的建模工具，该平台还将提供轻松访问所收集数据的功能。作为对已开发的时空分析和建模工具的访问，此类功能将有助于快速生成用于社区准备的数据驱动决策支持系统。这些研究人员将帮助收集和开发拟议的平台。验证数据，分析政策如何根据亚洲和欧洲的经验，超过 200 名本科生志愿者（其中许多来自代表性不足的群体）已经参与其中通过哈佛大学冠状病毒可视化团队的努力参与该项目，该项目中积累的数据、信息和知识已经并将继续长期存档在综合网关（covid-19.stcenter.net）中。确诊病例的时空分布、来自不同资源的相关社会、经济和自然信息，如权威报告、新闻发布、地球观测和社交媒体，均发布在 GitHub 上以供持续在线协作。使用时空分析来挖掘 COVID-19 与社会和自然因素之间的模式和关系，以进行可复制的研究，以促进社区应对和准备。该奖项授予 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持优点和更广泛的影响审查标准。

项目成果

The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

政策措施对美国人口流动、COVID-19 病例和死亡率的影响：时空视角

• DOI: 10.3390/ijerph18030996
• 发表时间: 2021-01-23
• 期刊: International Journal of Environmental Research and Public Health
• 作者: Li Y;Li M;Rice M;Zhang H;Sha D;Li M;Su Y;Yang C
• 通讯作者: Yang C

Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective

COVID-19 遏制和关闭政策对对流层二氧化氮的影响：全球视角

• DOI: 10.1016/j.envint.2021.106887
• 发表时间: 2022-01
• 期刊: Environment International
• 影响因子: 11.8
• 作者: Li, Yun;Li, Moming;Rice, Megan;Yang, Chaowei
• 通讯作者: Yang, Chaowei

A State-Level Socioeconomic Data Collection of the United States for COVID-19 Research

美国用于 COVID-19 研究的州级社会经济数据收集

• DOI: 10.3390/data5040118
• 发表时间: 2020-12
• 期刊: Data
• 影响因子: 2.6
• 作者: Sha, Dexuan;Malarvizhi, Anusha Srirenganathan;Liu, Qian;Tian, Yifei;Zhou, You;Ruan, Shiyang;Dong, Rui;Carte, Kyla;Lan, Hai;Wang, Zifu;et al
• 通讯作者: et al

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

使用 AI 方法对 COVID-19 患者进行个体层面的死亡率预测

• DOI: 10.3389/fpubh.2020.587937
• 发表时间: 2020-09-30
• 期刊: Frontiers in Public Health
• 影响因子: 5.2
• 作者: Yun Li;Melanie A. Horowitz;Jiakang Liu;A. Chew;H. Lan;Qian Liu;D. Sha;C. Yang
• 通讯作者: C. Yang

A spatiotemporal data collection of viral cases for COVID-19 rapid response

用于 COVID-19 快速反应的病毒病例时空数据收集

• DOI: 10.1080/20964471.2020.1844934
• 发表时间: 2020-01
• 期刊: Big Earth Data
• 影响因子: 4
• 作者: Sha, Dexuan;Liu, Yi;Liu, Qian;Li, Yun;Tian, Yifei;Beaini, Fayez;Zhong, Cheng;Hu, Tao;Wang, Zifu;Lan, Hai;et al
• 通讯作者: et al