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Recent advances in large-scale human mobility datasets have opened new opportunities to improve public health through data-driven strategies, advanced computational methods, and interdisciplinary approaches. A key focus in epidemiological research is the estimation and analysis of social contact patterns, representing the frequency and nature of interactions among different demographic groups. These patterns are vital for modeling disease transmission, evaluating public health interventions, and guiding resource allocation. However, obtaining accurate and representative contact data remains a major challenge. In this paper, we propose a novel, scalable framework for generating and analyzing large-scale social contact datasets derived from foot-traffic data. Our approach integrates statistical modeling to estimate demographic distributions, such as age groups, at millions of points of interest (POIs) across the United States and globally, including restaurants, stores, hospitals, and schools. This framework enables actionable insights to inform public health strategies and improve population health outcomes. Moreover, the resulting datasets have broad cross-sector utility, supporting applications in strategic business planning, resource distribution, and personalized marketing and advertising. 

Social media data has shown potential for identifying infectious disease outbreaks faster than official records of disease incidence. We examine spatial, temporal, and spatiotemporal relationships between COVID-19-related microblog sentiment and COVID-19 cases over space and time to investigate whether microblog-derived sentiment can be used for local infectious disease outbreak early warning. Therefore, we measure the sentiment of 56,755,894 COVID-19 related microblogs (tweets) from the microblogging platform X. We group these tweets by county and by calendar week to investigate spatial and temporal correlation between sentiment and observed cases (in the corresponding county and week). Our temporal analysis shows a significant negative correlation between sentiment and cases between June and September 2020. During this time, tweet sentiment could have served as an early warning for new COVID-19 outbreaks. Our spatial analysis shows that the East of the United States exhibits a significant negative correlation between Sentiment and Cases while the West exhibits a significant positive correlation. In these regions, Tweet Sentiment could have been used as an early warning signal for new outbreaks. Our spatiotemporal analysis discovers even stronger correlations in certain regions during certain time periods. If we could understand when, where, and why this correlation is strong, then we may be able to leverage social media as a successful early warning system.