Evidence map›Paper›PMID 39167120›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2024

Visualizing multilayer spatiotemporal epidemiological data with animated geocircles.

Brian Ondov, Harsh B Patel, Ai-Te Kuo, John Kastner, Yunheng Han, Hong Wei, Niklas Elmqvist, Hanan Samet

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Reflections on interactive visualization of electronic health records: past, present, future.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Brian OndovDepartment of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT 06510, United States.ORCID 0000-0002-0740-1793
Harsh B PatelT. Rowe Price, Baltimore, MD 21202, United States.
Ai-Te KuoDepartment of Computer Science, Auburn University, Auburn, AL 36849, United States.
John KastnerAmazon Web Services, Amazon, Inc, Seattle, WA 98109, United States.
Yunheng HanDepartment of Computer Science, University of Maryland, College Park, MD 20742, United States.
Hong WeiMeta Research, Meta Platforms, Inc., Menlo Park, CA 94025, United States.
Niklas ElmqvistDepartment of Computer Science, Aarhus Universitet, 8200 Aarhus, Denmark.
Hanan SametDepartment of Computer Science, University of Maryland, College Park, MD 20742, United States.

Funding

National Science Foundation IIS-18-16889NIH HHS
6 · The paper itself

Abstract

objectiveThe 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. MATERIALS AND

methodsWe 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.

resultsSessions 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. DISCUSSION: 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.

conclusionCoronaViz'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.

Indexed as

COVID-19Spatio-Temporal AnalysisComputer GraphicsData VisualizationHumansSoftwareUser-Computer InterfaceCOVID-19data visualizationepidemiologygeographic information systems

Identifiers

PMID39167120
PMCPMC11491657

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.