Monitoring the Spatial Spread of COVID-19 and Effectiveness of the Control Measures through Human Movement using Big Social Media Data: A Study Protocol (Preprint)

Zhenlong Li, Xiaoming Li, Dwayne Porter, Jiajia Zhang, Yuqin Jiang, Bankole Olatosi, Sharon Weissman
2020 JMIR Research Protocols  
Human movement is among the essential forces that drive spatial spread of infectious diseases. To date, reducing and tracking human movement during the pandemic have proven effective in limiting the spread of COVID-19. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and dollar bills tracking. However, intrinsic limitations of these data sources
more » ... us from systematic monitoring and analyses of human movement from different spatial scales (from local to global). Big social media data such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (e.g., from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big Twitter data coupled with other human mobility data sources and AI to monitor and analyze human movement at different spatial scales (from global to regional to local) for enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. This research will first develop a database with optimized spatiotemporal indexing to store and manage the multi-source datasets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. This research will then develop innovative data models, predictive models, and computing algorithms, to effectively extract and analyze human movement patterns from big geotagged Twitter data and other human mobility data sources for enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Research findings can help government officials, public health managers and emergency responders, and researchers to answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of the social/physical distancing practice in curtaining the spread of the virus. DERR1-10.2196/24432.
doi:10.2196/24432 pmid:33301418 fatcat:zwoulwsp5vebre7jsjdeimbomu