Abstract—The COVID-19 pandemic brought to the forefront
an unprecedented need for experts, as well as citizens, to visualize
spatio-temporal disease surveillance data. Web application
dashboards were quickly developed to fill t his g ap, b ut a ll of
these dashboards supported a particular niche view of the
pandemic (ie, current status or specific r egions). I n t his paper,
we describe our work developing our COVID-19 Surveillance
Dashboard, which offers a unique view of the pandemic while
also allowing users to focus on the details that interest them. From
the beginning, our goal was to provide a simple visual tool for
comparing, organizing, and tracking near-real-time surveillance
data as the pandemic progresses. In developing this dashboard,
we also identified 6 key metrics which we propose as a standard
for the design and evaluation of real-time epidemic science
dashboards. Our dashboard was one of the first r eleased t o the
public, and continues to be actively visited. Our own group uses
it to support federal, state and local public health authorities,
and it is used by individuals worldwide to track the evolution
of the COVID-19 pandemic, build their own dashboards, and
support their organizations as they plan their responses to the
pandemic.
more »
« less
Visualizing multilayer spatiotemporal epidemiological data with animated geocircles
The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts.
We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings.
Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards.
We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation.
CoronaViz’s unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.
- PAR ID:
- 10539561
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the American Medical Informatics Association
- Volume:
- 31
- Issue:
- 11
- ISSN:
- 1067-5027
- Format(s):
- Medium: X Size: p. 2507-2518
- Size(s):
- p. 2507-2518
- Sponsoring Org:
- National Science Foundation
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