Pandemics have profound global health impacts, significantly reshaping social interactions and behaviors, including heightened awareness and anxiety, reduced mobility, and implementing policies like lockdowns and social distancing during outbreaks. Meanwhile, effective social and behavioral responses can be rapidly deployed to mitigate pandemics. Understanding the spatiotemporal disparities of social-behavioral dynamics and their short- and long-term interactions with pandemic progression is crucial for designing effective responses. However, deciphering these dynamics and their complex interactions remains challenging. This research leverages social media data, mobility metrics captured by smartphones, and web-harvested policy evaluations to develop a comprehensive framework for quantifying social-behavioral dynamics during pandemics across geographic and temporal dimensions. Using COVID-19 as a case study, the research focuses on the ten most populous U.S. cities during 2020–2021. The objectives are: (1) to analyze diverse geospatial big data to construct indexes that capture social-behavioral dynamics, (2) to uncover geographic and temporal disparities in these dynamics among the ten cities, and (3) to examine the relationships and lag effects between social-behavioral changes and health outcomes throughout the two years. This work underscores the potential of multi-sourced big data and advanced computing methods to inform public health decision-making and enhance preparedness for future pandemics.
Zou et al. (Sat,) studied this question.