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.
Highlights
Visualization plays an important role in epidemic time series analysis and forecasting
In the face of an emerging epidemic, like the Ebola out- use to assess the situation, including reviewing historical break in West Africa in 2014 or the Zika outbreak in outbreaks and strategies that have been tried in the past; Brazil in 2017, authorities often turn to epidemiologists visualization of different kinds of spatiotemporal to help determine the likely severity of the outbreak and datasets are key in interpreting the scope of the outbreak [1]
Full list of author information is available at the end of the article ple, epidemiologists from many organizations were tasked with identifying measures likely to be effective in stopping the spread [2, 3]; this required a good understanding of the spread and prevalence of the infection, as well as the likely progression if left unchecked
Summary
Visualization plays an important role in epidemic time series analysis and forecasting. 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. As the researchers attempted to evaluate these datasets, they found that discrepancies in the data, aggregation type, data formats, category, and scope made it difficult to tell a cohesive story from the various datasets Some of these problems were rooted in how the data was collected, including incomplete or overestimated reporting of the surveillance data, as well as different modeling methods for the forecasts [5, 10]. As the ad-hoc team responding to this crisis was international, a persistent, standardized, and open way of visualizing and sharing these data was needed
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