RAPID: Geospatial Modeling of COVID-19 Spread and Risk Communication by Integrating Human Mobility and Social Media Big Data

RAPID：通过整合人员流动性和社交媒体大数据对 COVID-19 传播和风险沟通进行地理空间建​​模

• 批准号: 2027375
• 负责人: Song Gao
• 金额: $ 19.99万
• 依托单位: University of Wisconsin-Madison
• 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: 2020-04-15 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027375&HistoricalAwards=false
• 关键词: RAPID; Geospatial; Modeling; COVID; 19

This project will investigate the gap between the science of epidemic modeling and risk communication to the general public in response to the COVID-19 pandemic. With the rapid development of information, communication, and technologies, new data acquisition and assessment methods are needed to evaluate the risk of epidemic transmission and geographic spreading from the community perspective, to help effectively monitor social distancing policies, and to understand social disparities and environmental contexts in risk communication. This project will make theoretical, methodological, and practical contributions that advance the understanding of the COVID-19 spread across both time and space. The communication aspects of this research will serve to educate communities about the science, timing, and geography of virus transmission in order to enhance actions for addressing such global health challenges. This project explores the capabilities and potential of integrating social media big data and geospatial artificial intelligence (GeoAI) technologies to enable and transform spatial epidemiology research and risk communication. Results will be disseminated broadly to multiple stakeholder groups. Further, this project will support both researchers and students from underrepresented groups, broadening participation in STEM fields. Lastly, the Web platform developed in this project will serve as an education tool for students in geography, communication, mathematics, and public health, as well as for effectively engaging with communities about the science of COVID-19. Past health research mainly focuses on quantitative modeling of human transmission using various epidemic models. How to effectively communicate the science of an epidemic outbreak to the general public remains a challenge. When an epidemic outbreak occurs without specific controls in place, it can be particularly challenging to improve community risk awareness and action. The research team, composed of experts from geography, mathematics, public health and life sciences communication will (1) develop innovative mathematical predictive models that integrate spatio-temporal-social network information and community-centered approaches; (2) integrate census statistics, human mobility and social media big data, as well as policy controls to conduct data-synthesis-driven and epidemiology-guided risk analysis; And (3) utilize panel surveys and text mining techniques on social media data for better understanding public awareness of COVID-19 and for investigating various instant message and visual image strategies to effectively communicate about risks to the public. The results of this project will lead to a better understanding of the geography and spread of COVID-19. Additionally, it is expected that the methods developed in this project can be applied to mitigate the outbreak risks of future epidemics.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.

该项目将调查在应对COVID-19大流行时，流行病建模科学与向公众传达风险之间的差距。随着信息、通信和技术的快速发展，需要新的数据采集和评估方法，从社区角度评估流行病传播和地理传播的风险，帮助有效监测社会距离政策，并了解风险沟通中的社会差异和环境背景。该项目将在理论、方法和实践方面做出贡献，促进对COVID-19跨越时间和空间的理解。这项研究的传播方面将有助于教育社区关于病毒传播的科学、时间和地理，以便加强应对此类全球卫生挑战的行动。该项目探索整合社交媒体大数据和地理空间人工智能（GeoAI）技术的能力和潜力，以实现和改变空间流行病学研究和风险沟通。结果将广泛传播给多个利益攸关方群体。此外，该项目将支持来自代表性不足群体的研究人员和学生，扩大STEM领域的参与。最后，本项目开发的网络平台将作为学生在地理、通信、数学和公共卫生方面的教育工具，并有效地与社区开展有关COVID-19科学的交流。过去的卫生研究主要集中在利用各种流行病模型对人类传播进行定量建模。如何有效地向公众传播流行病爆发的科学知识仍然是一项挑战。在没有具体控制措施的情况下爆发流行病时，提高社区风险意识和采取行动可能特别具有挑战性。由地理学、数学、公共卫生和生命科学传播学专家组成的研究团队将：(1)开发整合时空社会网络信息和以社区为中心的创新数学预测模型；(2)整合人口普查统计、人口流动和社交媒体大数据以及政策调控，开展数据综合驱动和流行病学指导的风险分析；(3)利用社交媒体数据的小组调查和文本挖掘技术，更好地了解公众对COVID-19的认识，并调查各种即时消息和视觉图像策略，以有效地向公众传达风险。该项目的成果将有助于更好地了解COVID-19的地理和传播情况。此外，预计本项目开发的方法可用于减轻未来流行病爆发的风险。该奖项反映了美国国家科学基金会的法定使命，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

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

• DOI: 10.1001/jamanetworkopen.2020.20485
• 发表时间: 2020-09-08
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Gao, Song;Rao, Jinmeng;Patz, Jonathan A.
• 通讯作者: Patz, Jonathan A.

Exploring Store Visit Changes During the COVID-19 Pandemic Using Mobile Phone Location Data

• DOI: 10.1007/978-3-030-72808-3_13
• 发表时间: 2021
• 期刊: Mapping COVID-19 in Space and Time
• 作者: Yunlei Liang;Kyle McNair;Song Gao;Aslıgül Göçmen

Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness

科学阴谋视频的视觉框架：将机器学习与传播理论相结合，研究颜色和亮度的使用

• 发表时间: 2021
• 期刊: Computational communication research
• 作者: Chen, Kaiping;Kim, Sang Jung;Raschka, Sebastina;Gao, Qiantong
• 通讯作者: Gao, Qiantong

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

• DOI: 10.1073/pnas.2020524118
• 发表时间: 2021-06-15
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Hou, Xiao;Gao, Song;Patz, Jonathan A.
• 通讯作者: Patz, Jonathan A.

Platform Effects on Alternative Influencer Content: Understanding How Audiences and Channels Shape Misinformation Online

平台对另类影响者内容的影响：了解受众和渠道如何在网上塑造错误信息

• DOI: 10.3389/fpos.2021.642394
• 发表时间: 2021
• 期刊: Frontiers in Political Science
• 作者: Hiaeshutter-Rice, Dan;Chinn, Sedona;Chen, Kaiping
• 通讯作者: Chen, Kaiping