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        <datestamp>2023-09-04</datestamp>
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          <dc:contributor>Hart, John C</dc:contributor>
          <dc:date>2023-05</dc:date>
          <dc:format>application/pdf</dc:format>
          <dc:language>en</dc:language>
          <dc:type>text</dc:type>
          <dc:description>Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms</dc:description>
          <dc:description>The student, Ho Shan Lam, accepted the attached license on 2023-04-18 at 13:22.</dc:description>
          <dc:description>The student, Ho Shan Lam, submitted this Thesis for approval on 2023-04-18 at 13:38.</dc:description>
          <dc:description>This Thesis was approved for publication on 2023-04-19 at 09:07.</dc:description>
          <dc:description>DSpace SAF Submission Ingestion Package generated from Vireo submission #17542 on 2023-09-01 at 17:06:45</dc:description>
          <dc:title>MWEpi-Viz: an interactive visualization dashboard for ensemble simulations in computational epidemiology</dc:title>
          <dc:creator>Lam, Ho Shan</dc:creator>
          <dc:date>2023-04-19</dc:date>
          <dc:subject>Agent-based Modeling</dc:subject>
          <dc:subject>Computational Epidemiology</dc:subject>
          <dc:subject>Ensemble Visualization</dc:subject>
          <dc:description>The COVID-19 pandemic has prompted the development of numerous visualization tools to facilitate understanding and communicating on epidemiological research findings. However, existing visualization work for agent-based epidemiology simulations often focuses on macro-scale phenomena, while overlooking the connection between macro-scale trends and micro-scale agent behaviors. Portraying this connection accurately for ensemble simulations can bring substantial insights into both the epidemiology of infectious diseases and public health intervention design and evaluation. To address this gap, we present MWEpi-Viz, an interactive dashboard that facilitates exploration of ensemble datasets generated via epidemiological agent-based models. In this thesis, we describe the development process, system design, and efficacy of MWEpi-Viz, which draws upon months of participatory research with a computational epidemiologist. We illustrate the utility of MWEpi-Viz using an ensemble dataset from a counterfactual COVID-19 scenario in the Champaign-Urbana community, August 2020.</dc:description>
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          <dc:language>eng</dc:language>
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          <dc:rights>Copyright 2023 Ho Shan Lam</dc:rights>
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            <discipline>Computer Science</discipline>
            <grantor>University of Illinois at Urbana-Champaign</grantor>
            <department>Computer Science</department>
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