Infectious Disease Surveillance and Modeling through Spatial Big Data
During one of epidemiology’s formative moments, John Snow mapped London households with cholera and succeeded in highlighting the risk of disease associated with the Broad Street pump. Since then, spatial investigations have played a critical role in improving our understanding of the associations between risks and disease outcomes. Modern electronic resources allow us to carry out spatial epidemiology studies through large-scale digital health, which provide opportunities by increasing accessibility to populations over space and time with data on personal beliefs, behaviors, and health outcomes at an unprecedented breadth and depth. In this talk, I will discuss case studies where spatial big data has improved spatial modeling and describe ongoing challenges as spatial big data become more pervasive in informing disease surveillance, disease control, and public health policy.
Shweta Bansal is a Provost's Distinguished Associate Professor of Biology at Georgetown University. She is trained as a network scientist and disease ecologist from the University of Texas at Austin and was a fellow of the prestigious RAPIDD Postdoctoral Program (of the US National Institutes of Health and the Department of Homeland Security). At Georgetown University, she leads an interdisciplinary research group that develops big data-driven mathematical models to address how social behavior and spatial dynamics shape infectious disease transmission and how knowledge of such processes can improve disease surveillance and control in human and animal disease systems.