Epinome: A Visual-Analytics Workbench for Epidemiology Data

Livnat, Yarden; Rhyne, Theresa-Marie; Samore, Matthew

doi:10.1109/mcg.2012.31

Cited by 23 publications

(23 citation statements)

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“…The injury prevention dashboard was evaluated among others with distributed cognition —an evaluation technique that is intended to solve cooperative problem solving. The most extensive evaluation was found in the discussion of the Epinome system (recall Section 5.5 and [LRS12]). Ten outbreak scenarios from which the 27 participants got a random selection of four were employed.…”

Section: Discussionmentioning

confidence: 99%

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A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

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We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.

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Section: Discussionmentioning

confidence: 99%

“…Web‐based solutions are frequently used to support easy access of various stakeholders [ANI*17, JAK*17, LRS12, KLG*16]. Also, Carroll et al .…”

Section: Commonly Used Visual Analytics Techniquesmentioning

confidence: 99%

A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

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“…Some examples of research conducted in developing web-based systems and visualization platforms for epidemic simulations include Epinome [Livnat et al 2012], Gryphon [Yu et al 2010], FRED (Framework for Reconstructing Epidemiological Dynamics) [Grefenstette et al ], GLEaMviz [Broeck et al 2011] and ISIS (Interface to Synthetic Information Systems) [Beckman et al ]. Epinome is a user centric system with visual analytics support for epidemiology that helps users evaluate intervention strategies based on available information.…”

Section: Background and Related Workmentioning

confidence: 99%

An Interactive, Web-Based High Performance Modeling Environment for Computational Epidemiology

Deodhar

Bisset

Chen

et al. 2014

ACM Trans. Manage. Inf. Syst.

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We present an integrated interactive modeling environment to support public health epidemiology. The environment combines a high resolution individual-based model with a user-friendly web-based interface that allows analysts to access the models and the analytics back-end remotely from a desktop or a mobile device. The environment is based on a loosely-coupled service-oriented-architecture that allows analysts to explore various counter factual scenarios. As the modeling tools for public health epidemiology are getting more sophisticated, it is becoming increasingly hard for non-computational scientists to effectively use the systems that incorporate such models. Thus an important design consideration for an integrated modeling environment is to improve ease of use such that experimental simulations can be driven by the users. This is achieved by designing intuitive and user-friendly interfaces that allow users to design and analyze a computational experiment and steer the experiment based on the state of the system. A key feature of a system that supports this design goal is the ability to start, stop, pause and roll-back the disease propagation and intervention application process interactively. An analyst can access the state of the system at any point in time and formulate dynamic interventions based on additional information obtained through state assessment. In addition, the environment provides automated services for experiment set-up and management, thus reducing the overall time for conducting end-to-end experimental studies. We illustrate the applicability of the system by describing computational experiments based on realistic pandemic planning scenarios. The experiments are designed to demonstrate the system's capability and enhanced user productivity.

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“…The data flow in Boxfish follows the hierarchical grouping and coordination of filters and views as implemented in Epinome [2], an application for exploring epidemiological scenarios. This system allows both the data manipulation and the view interactions to be applied simultaneously to several views at once.…”

Section: Filter Hierarchy In Boxfishmentioning

confidence: 99%

Abstract: Exploring Performance Data with Boxfish

Isaacs

Landge

Gamblin

et al. 2012

2012 SC Companion: High Performance Computing, Networking Storage and Analysis

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The growth in size and complexity of scaling applications and the systems on which they run pose challenges in analyzing and improving their overall performance. With metrics coming from thousands or millions of processes, visualization techniques are necessary to make sense of the increasing amount of data. To aid the process of exploration and understanding, we announce the initial release of Boxfish, an extensible tool for manipulating and visualizing data pertaining to application behavior. Combining and visually presenting data and knowledge from multiple domains, such as the application's communication patterns and the hardware's network configuration and routing policies, can yield the insight necessary to discover the underlying causes of observed behavior. Boxfish allows users to query, filter and project data across these domains to create interactive, linked visualizations. I. PROJECTING DATA ACROSS DOMAINSWe describe the association of elements that exist in one domain with the elements of another as a projection. A map file which associates integer MPI ranks with coordinate-denoted hardware nodes and threads is an example of a commonly used projection. Schulz et al.[1] advocated the use of projections in interpreting performance data and defined three domains of interest -hardware, application and communication. The hardware domain includes performance counters. The application domain includes information relating to the application, such as physics measurements in a simulation or matrix properties in a linear algebra library. The communication domain includes messages sent among subsets of processors. Boxfish recognizes these domains by default, but contributed modules may add others.Boxfish is designed to support the projection of data across domains. When filters or queries are written requiring attributes from multiple domains, or when a view requires attribute information in a native domain, Boxfish searches its available projections to make the necessary transformations. This allows users to view data such as the load on nodes which had a certain range of values in a previous run or the average wait time for communicators in a particular phase of the application. Data tables may have default preferred projections. Projections can be added from files, created based on data Fig. 1. A 3D torus network represented in 2D (left) and 3D (right). Both views represent elements of the hardware domain. However, nodes are colored by their sub-communicators, which belong to the communication domain.Links are colored by the number of packets sent over them. These views are rendered side by side in Boxfish, indicating they are siblings in the filter hierarchy and show the same data. In the 2D view, selected nodes are displayed at a slightly larger size. In the 3D view, the same nodes are selected and highlighted by their relative opacity. attributes, or composed from existing ones. More projections may be added through future or contributed modules. Figure 1 shows a projection from the communication domain on...

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Epinome: A Visual-Analytics Workbench for Epidemiology Data

Cited by 23 publications

References 1 publication

A Survey of Visual Analytics for Public Health

A Survey of Visual Analytics for Public Health

An Interactive, Web-Based High Performance Modeling Environment for Computational Epidemiology

Abstract: Exploring Performance Data with Boxfish

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