Dwayne E Porter
University of South Carolina at Columbia
Human population movement is among the critical dimension that drives the spatial spread of COVID-19. During such a global pandemic, monitoring and analyzing human movement patterns or population flows are critical for us to gain a better understanding into current and future infectious risk at the population level. This Rapid Response Research (RAPID) grant will utilize big social media data, artificial intelligence (AI), and spatiotemporal analysis to monitor and model the spatial spread of COVID-19 at different spatial scales (from local to regional to global) through the lens of human mobility patterns. Results of this project, disseminated through an interactive online dashboard, will provide enhanced situation awareness for government official and the public, offering insights and facilitating a collective public awareness of the role people play in the evolution of the COVID-19 crisis. The information provided by the dashboard can help government officials, public health managers and emergency responders to answer critical questions during the pandemic, such as: “What is the current and future infectious risk of a state, county, or community?”; “How effective are the social/physical distancing practice in containing the virus?”; and “What are the consequences for different strategies for reopening our economy and communities?”. The successful implementation of this project will advance the national health, prosperity, and welfare during COVID-19 pandemic and future public health crisis. This project will support education and diversity through engaging graduate and undergraduate students from different backgrounds in data collection, analysis, modeling, tool development, and community outreach and training.Big social media data have been widely used in human mobility studies, yet little research has been conducted to validate the capabilities and limitations of using these data for studying human movement at different geographic scales (e.g., from local to global) in the context of global infectious disease transmission. By leveraging the team’s expertise in spatial computing, big data analytics, infectious disease modeling, public health education and behavior modification, and community engagement, this project aims to develop a novel data-driven approach, including innovative models, efficient computing algorithms, and an interactive dashboard, to monitor, model and map the spatial spread of COVID-19. Specifically, we will (1) develop a novel origin-destination-time data model to efficiently extract historical and near real-time population flows at varying spatiotemporal scales from billions of geotagged social media (Twitter) posts; (2) develop a predictive model using a spatial-temporal fused neural network to estimate future infectious potential by incorporating population flows with other factors; and (3) perform spatiotemporal and geostatistical analysis for aggregated population flows and daily confirmed cases at varying spatial scales to examine the spatiotemporal dynamics and associations between human movement and spatial spread of the virus. Methodological findings of this project are expected to make significant contributions to the development, application, and extension of models and methodology in a variety of human mobility studies. This research will promote the progress of geospatial science and have broad impacts in diverse fields that can benefit from a better understanding of human movement, such as public health, natural hazard, transportation, and tourism.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.