A Visual Analytics Based Decision Making Environment for COVID-19 Modeling and Visualization

Shehzad Afzal, Sohaib Ghani, Hank C. Jenkins-Smith, David S. Ebert, Markus Hadwiger, Ibrahim Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Public 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.
Original languageEnglish (US)
Title of host publication2020 IEEE Visualization Conference (VIS)
PublisherIEEE
Pages86-90
Number of pages5
ISBN (Print)978-1-7281-8015-1
DOIs
StatePublished - Feb 1 2021

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