Discussion on “Real-time monitoring of events applied to syndromic surveillance”

Abstract

I discuss the article “Real-time monitoring of events applied to syndromic surveillance” by Sparks and collaborators. This discussion focuses on how statistical network modeling and inference can be used to augment the analysis done in their paper. In particular I describe what network models can be used to characterize the dynamics and interactions of Twitter users, and more broadly how network analysis can be used to benefit statistical process monitoring. I hope to not only provide readers a new perspective on how to approach statistical process monitoring in the context of social interactions, but also to motivate future research that address the unique challenges facing quality engineers.

Keywords:

• network analysis
• statistical process control
• statistical process monitoring
• syndromic surveillance
• Twitter

Additional information

Notes on contributors

James D. Wilson

James D. Wilson is an Assistant Professor of Statistics and Data Science at the University of San Francisco. He is also the Co-Director of Data Science and Associate Director of Research of the Data Institute at the University of San Francisco. He received his Ph.D. of Statistics and Operations Research at the University of North Carolina at Chapel Hill in 2015. His research brings together techniques from machine learning, statistical inference, and random graph theory to model, analyze, and explore relational (network) data. He is particularly interested in developing random graph models and feature extraction methodologies for dynamic and multilayer networks; monitoring networked systems; and investigating networks that arise in diverse applications ranging from neuroscience to political science to infectious disease. He is a member of the ASA, ACM, and IMS.