RAPID: Urban Resilience to Health Emergencies: Revealing Latent Epidemic Spread Risks from Population Activity Fluctuations and Collective Sense-making

COVID-19疫情在地球仪各地造成可怕的社会及经济影响，其传播已成为美国的主要社会威胁。然而，大多数流行病传播模型没有充分考虑与人类反应行为（人口和个人行为者）和城市系统供应链在流行病爆发期间的扰动相关的巨大不确定性。在这个项目中，研究小组收集和分析有时限的数据，以更好地了解和预测，并更有效地应对城市地区爆发传染病的风险。这些数据可用于帮助确定影响城市规模人口反应行为的基本过程，在线社交媒体中的集体意义，城市系统供应链的中断以及不同城市部门行为者之间的集体信息处理和协调。这些发现可以促进对流行病爆发威胁复杂性的基本理解，这将超出标准爆发模型和纯粹的临床研究。这些结果为更好地预测提供了新的方法，并为如何进行城市规模的流行病传播风险监测提供了新的见解。这些发现为预防、帮助控制和减轻未来流行病和大流行病的影响的战略和可能的数据驱动工具和方法提供了信息。首先，该项目将确定和收集数据，这些数据可以提供有关人口应对流行病威胁的反应行为的微弱信号。例如，交通模式的异常可能表明由于远程办公而导致需求减少。流动数据提供了关于人口流动模式的信息，有助于监测社会距离措施的有效性。其次，该项目收集社交媒体帖子，如Twitter，以研究流行病风险如何在在线社交网络中处理和编码。第三，通过组织访谈和调查，该项目揭示了应对疫情传播风险和城市系统扰动的城市各部门不同行为体之间的集体信息处理和协调行动。通过空间建模、网络分析和数据分析技术对数据进行分析。在分析这些数据集时，特别关注弱势群体社区的人口活动模式（例如，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。

COVID-19 outbreaks have had dire societal and economic impacts across the globe, and its spread has become a major societal threat in the United States. The majority of epidemic spread models, however, do not fully consider the tremendous uncertainty associated with human response behaviors (both populations and individual actors) and perturbations in urban system supply chains during an epidemic outbreak. For this project, the research team collects and analyzes time-bound data to better understand and predict and to more effectively respond to the risk of infection disease outbreaks in urban areas. These data can be used to help identify the underlying processes that influence urban-scale population response behaviors, collective sense-making in online social media, disruptions in urban system supply chains, and collective information processing and coordination among actors across different urban sectors. These finding can advance the fundamental understanding of the complexities of epidemic outbreak threats, which would extend beyond standard outbreak models and purely clinical research. The outcomes suggest new ways for better prediction and offer novel insights regarding ways to conduct urban-scale surveillance of epidemic spread risks. The findings inform strategies and possible data-driven tools and methods to prevent, help contain, and mitigate the effects of future epidemics and pandemics.The specific project tasks are threefold. First, the project will identify and collect data that could provide weak signals about population response behaviors in response to epidemic threats. For example, anomalies in traffic patterns can suggest reduction in demand due to telecommuting. Mobility data, which informs about patterns of population fluxes, facilitates monitoring of the effectiveness of social distancing measures. Second, the project collects social media posts, such those in as Twitter, to examine how epidemic risk is processed and encoded in online social networks. Third, through organizational interviews and surveys, the project uncovers collective information processing and coordination actions among different actors across various urban sectors responding to epidemic spread risks and urban system perturbations. The data are analyzed through spatial modeling, network analysis, and data analytics techniques. In analysis of these datasets, a particular attention are given to population activity patterns in neighborhoods with vulnerable populations (e.g., elderly, low income, and racial minorities).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.