Abstract
BackgroundHuman movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. 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 banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global).ObjectiveBig data from social media 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 (eg, 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 data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local).MethodsWe will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems.ResultsThis project was funded as of May 2020. We have started the data collection, processing, and analysis for the project.ConclusionsResearch findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus.International Registered Report Identifier (IRRID)DERR1-10.2196/24432
Highlights
COVID-19, which is caused by severe acute respiratory syndrome (SARS)-CoV-2, was originally detected in Wuhan, China, in December 2019
By leveraging the interdisciplinary team’s collective expertise in spatiotemporal modeling, big data analytics, infectious disease, spatial epidemiology, and health promotion and behavior modification, we propose to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence (AI) to monitor and analyze human movement at different spatial scales
We propose to model the current infection risk of a given place by integrating the following information: (1) population flows derived from the Origin-Destination-Time OD (ODT) cube during the recent time period among all places, (2) the number of total COVID-19 cases for each place, and (3) socioeconomic and demographic variables that relate to the infection risk of that location
Summary
COVID-19, which is caused by SARS-CoV-2, was originally detected in Wuhan, China, in December 2019. Due to the rapid human-to-human transmission of COVID-19, models or measurements that contribute to increased knowledge about potential infectious risk at different geographic levels can play an essential role for residents, medical workers, and governments. Such models can help local authorities and communities better allocate resources and efforts at a community level. It is important for policy makers and emergency responders to understand how people practice social/physical distancing and how effective these control measures are at curbing the spatial propagation of the virus. Intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
