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EpiViewer: an epidemiological application for exploring time series data.

Swapna Thorve1,2, Mandy L Wilson3, Bryan L Lewis3

  • 1Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.

BMC Bioinformatics
|November 24, 2018
PubMed
Summary
This summary is machine-generated.

EpiViewer is a new dashboard for visualizing and analyzing epidemic time series data. It helps researchers compare diverse datasets, identify trends, and improve forecasting during public health emergencies.

Keywords:
Bar chartEpidemiologyLine chartMetricsTemporalTime seriesUser actionsVisualization

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Area of Science:

  • Epidemiology
  • Data Visualization
  • Public Health Informatics

Background:

  • Time series visualization is crucial for epidemic analysis and forecasting.
  • Comparing diverse epidemiological datasets (e.g., incidence, hospitalizations, deaths) across different scales is challenging.
  • Existing tools lack comprehensive features for collaborative visualization and analysis of multi-source epidemic data.

Purpose of the Study:

  • To introduce EpiViewer, a novel web-based dashboard for epidemiological time series data exploration.
  • To provide a user-friendly platform for visualizing, comparing, and organizing data from various sources.
  • To enhance epidemic data analysis and forecasting capabilities through integrated visualization and statistical features.

Main Methods:

  • Development of a single-page web application, EpiViewer.
  • Implementation of an intuitive interface for uploading and visualizing temporal datasets (line/bar charts).
  • Integration of features for hierarchical categorization, zooming, filtering, and meta-attribute tagging for detailed analysis.

Main Results:

  • EpiViewer enables users to upload and visualize epidemiological time series data from multiple sources.
  • The dashboard facilitates comparison, organization, and tracking of data evolution during epidemics.
  • Enhanced visual analysis features allow for detailed inspection and comparison of multiple time series on a single canvas.

Conclusions:

  • EpiViewer offers a robust framework for exploring, comparing, and organizing temporal epidemiological datasets.
  • The application simplifies filtering and analysis of epicurves using meta-attribute tagging.
  • EpiViewer enhances collaborative data sharing and analysis, proving easy to use and valuable for epidemiological research.