EpiViewer: an epidemiological application for exploring time series data.

Autor: Thorve S; Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.; Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, Virginia, USA., Wilson ML; Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA., Lewis BL; Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA., Swarup S; Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA., Vullikanti AKS; Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA.; Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA., Marathe MV; Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA. mvm7hz@virginia.edu.; Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA. mvm7hz@virginia.edu.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2018 Nov 22; Vol. 19 (1), pp. 449. Date of Electronic Publication: 2018 Nov 22.
DOI: 10.1186/s12859-018-2439-0
Abstrakt: Background: Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources - data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data.
Results: In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves.
Conclusion: EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.
Databáze: MEDLINE
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