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dc.contributor.authorAfzal, Shehzad
dc.contributor.authorGhani, Sohaib
dc.contributor.authorJenkins-Smith, Hank C.
dc.contributor.authorEbert, David S.
dc.contributor.authorHadwiger, Markus
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2021-02-24T07:04:27Z
dc.date.available2020-11-04T08:40:12Z
dc.date.available2021-02-24T07:04:27Z
dc.date.issued2021-02-01
dc.date.submitted2020-11-04
dc.identifier.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
dc.identifier.isbn978-1-7281-8015-1
dc.identifier.doi10.1109/VIS47514.2020.00024
dc.identifier.urihttp://hdl.handle.net/10754/665803
dc.description.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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9331307/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9331307
dc.rightsArchived with thanks to IEEE. © IEEE 2021. This article is free to access and download, along with rights for full text and data mining, re-use and analysis.
dc.subjectHuman-centered computing
dc.subjectVisualization
dc.subjectVisualization application domains
dc.subjectVisual analytics
dc.titleA Visual Analytics Based Decision Making Environment for COVID-19 Modeling and Visualization
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentVisualization
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentEarth Science and Engineering Program
dc.conference.date25-30 Oct. 2020
dc.conference.name2020 IEEE Visualization Conference (VIS)
dc.conference.locationSalt Lake City, UT, USA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of Oklahoma
dc.contributor.institutionPurdue University
dc.identifier.pages86-90
dc.identifier.arxivid2010.11897
kaust.personAfzal, Shehzad
kaust.personGhani, Sohaib
kaust.personHadwiger, Markus
kaust.personHoteit, Ibrahim
dc.identifier.eid2-s2.0-85100767408
refterms.dateFOA2020-11-04T08:41:04Z


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