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

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

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.