PanViz 2.0: Intregating AI into Visual analytics to adapt to the novel challenges of COVID-19

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dc.contributor.authorSnyder, Luke
dc.contributor.authorReinert, Audrey
dc.contributor.authorEbert, David
dc.date.accessioned2020-12-24T19:16:41Z
dc.date.available2020-12-24T19:16:41Z
dc.date.issued2021-01-05
dc.description.abstractThe ongoing and evolving COVID-19 pandemic has resulted in tremendous negative effects on people’s daily lives. It is critical for decision makers such as health care officials and governors to foresee potential impacts and make timely decisions. We present PanViz 2.0, a visual analytics application that combines an epidemic model and AI-driven analytics to infer the best-fit parameters to enable the adaptation to ongoing pandemics at multiple spatial aggregations (nation wide, state level, and county level). Our experiments for predicting the fatality cases in each county of the state of Oklahoma demonstrate the flexibility of our application in adapting to various scenarios and regions.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2021.176
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70788
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectInteractive Visual Analytics and Visualization for Decision Making
dc.subjectpandemic influenza
dc.subjectregression models
dc.subjecttime series data
dc.subjectvisual analytics
dc.titlePanViz 2.0: Intregating AI into Visual analytics to adapt to the novel challenges of COVID-19
prism.startingpage1457

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Interactive Visual Analytics and Visualization for Decision Making