Visual Analytics for Decision-Making during Pandemics

Abstract

We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.