GeoDEN: a visual exploration tool for analyzing the geographic spread of Dengue serotypes
Static maps and animations remain popular in spatial epidemiology of dengue, limiting the analytical depth and scope of visualizations. Over half of the global population live in dengue endemic regions. Understanding the spatiotemporal dynamics of the four closely related dengue serotypes, and their...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 March 2025
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| In: |
Computer graphics forum
Year: 2025, Pages: 1-16 |
| ISSN: | 1467-8659 |
| DOI: | 10.1111/cgf.70087 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/cgf.70087 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.70087 
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| Verfasserangaben: | A. Marler, Y. Roell, S. Knoblauch, J.P. Messina, T. Jaenisch and M. Karimzadeh |
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| 520 | |a Static maps and animations remain popular in spatial epidemiology of dengue, limiting the analytical depth and scope of visualizations. Over half of the global population live in dengue endemic regions. Understanding the spatiotemporal dynamics of the four closely related dengue serotypes, and their immunological interactions, remains a challenge at a global scale. To facilitate this understanding, we worked with dengue epidemiologists in a user-centred design framework to create GeoDEN, an exploratory visualization tool that empowers experts to investigate spatiotemporal patterns in dengue serotype reports. The tool has several linked visualizations and filtering mechanisms, enabling analysis at a range of spatial and temporal scales. To identify successes and failures, we present both insight-based and value-driven evaluations. Our domain experts found GeoDEN valuable, verifying existing hypotheses and uncovering novel insights that warrant further investigation by the epidemiology community. The developed visual exploration approach can be adapted for exploring other epidemiology and disease incident datasets. | ||
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