Evidence map›Paper›PMID 30466409›Full record

ArticleBMC bioinformatics2018

EpiViewer: an epidemiological application for exploring time series data.

Swapna Thorve, Mandy L Wilson, Bryan L Lewis, Samarth Swarup, Anil Kumar S Vullikanti, Madhav V Marathe

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

6 authors.

Swapna ThorveDepartment of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.
Mandy L WilsonBiocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA.
Bryan L LewisBiocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA.
Samarth SwarupBiocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA.
Anil Kumar S VullikantiDepartment of Computer Science, University of Virginia, Charlottesville, Virginia, USA.
Madhav V MaratheDepartment of Computer Science, University of Virginia, Charlottesville, Virginia, USA. mvm7hz@virginia.edu.

Funding

Defense Threat Reduction Agency HDTRA1-11-D-0016-0001Defense Threat Reduction Agency HDTRA1-11-D-0016-0005Defense Threat Reduction Agency HDTRA1-17-D-0023Defense Threat Reduction Agency HDTRA117F0118
6 · The paper itself

Abstract

backgroundVisualization 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.

resultsIn 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.

conclusionEpiViewer 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.

Indexed as

HumansInformation DisseminationSoftwareBar chartEpidemiologyLine chartMetricsTemporalTime seriesUser actionsVisualization

Identifiers

PMID30466409
PMCPMC6251172

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.