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

新冠肺炎疫情在全球范围内造成了严重的社会和经济影响，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（群体和个体行为者）以及流行病爆发期间城市系统供应链中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，以便更好地了解和预测并更有效地应对城市地区传染病爆发的风险。这些数据可用于帮助确定影响城市规模人口反应行为、在线社交媒体中的集体意义构建、城市系统供应链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的潜在过程。这些发现可以促进对流行病爆发威胁复杂性的基本理解，这将超越标准的爆发模型和纯粹的临床研究。这些结果为更好的预测提供了新的方法，并为如何开展城市规模的流行病传播风险监测提供了新的见解。调查结果为预防、帮助控制和减轻未来流行病和大流行的影响的战略和可能的数据驱动工具和方法提供了信息。具体的项目任务有三个方面。首先，该项目将确定和收集可以提供关于人口应对流行病威胁行为的微弱信号的数据。例如，交通模式的异常可能表明由于远程办公导致需求减少。流动数据为人口流动模式提供信息，有助于监测社会保持距离措施的有效性。其次，该项目收集社交媒体上的帖子，如推特上的帖子，以研究在线社交网络如何处理和编码流行病风险。第三，通过组织访谈和调查，该项目揭示了城市各部门不同行为者应对疫情传播风险和城市系统扰动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。在对这些数据集的分析中，特别关注弱势群体（如老年人、低收入者和少数族裔）社区的人口活动模式。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。

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.