A Visual Analytics Based Decision Making Environment for COVID-19 Modeling and Visualization
Jenkins-Smith, Hank C.
Ebert, David S.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Physical Science and Engineering (PSE) Division
Computer Science Program
Visual Computing Center (VCC)
Earth Science and Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/665803
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AbstractPublic health officials dealing with pandemics like COVID-19 have to evaluate and prepare response plans. This planning phase requires not only looking into the spatiotemporal dynamics and impact of the pandemic using simulation models, but they also need to plan and ensure the availability of resources under different spread scenarios. To this end, we have developed a visual analytics environment that enables public health officials to model, simulate, and explore the spread of COVID-19 by supplying county-level information such as population, demographics, and hospital beds. This environment facilitates users to explore spatiotemporal model simulation data relevant to COVID-19 through a geospatial map with linked statistical views, apply different decision measures at different points in time, and understand their potential impact. Users can drill-down to county-level details such as the number of sicknesses, deaths, needs for hospitalization, and variations in these statistics over time. We demonstrate the usefulness of this environment through a use case study and also provide feedback from domain experts. We also provide details about future extensions and potential applications of this work.
CitationAfzal, S., Ghani, S., Jenkins-Smith, H. C., Ebert, D. S., Hadwiger, M., & Hoteit, I. (2020). A Visual Analytics Based Decision Making Environment for COVID-19 Modeling and Visualization. 2020 IEEE Visualization Conference (VIS). doi:10.1109/vis47514.2020.00024
Conference/Event name2020 IEEE Visualization Conference (VIS)
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