A pandemic influenza modeling and visualization tool

Maciejewski, Ross; Livengood, Philip; Rudolph, Stephen; Collins, Timothy F.; Ebert, David S.; Brigantic, Robert T.; Corley, Courtney D.; Muller, George; Sanders, Stephen W.

doi:10.1016/j.jvlc.2011.04.002

Cited by 72 publications

(55 citation statements)

References 31 publications

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“…These models can be used to simulate the behavior of a system changing over time which is initially characterized by the original data and can be controlled by the user by means of parameter refinements. Examples of models in the domain of epidemic research include [11], [12] and [13]. In brief, models drive the transformation of the data to be used for visualizations and "provide a basis for decision makers to understand their world" [14].…”

Section: B Visual Analyticsmentioning

confidence: 99%

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PandemCap: Decision support tool for epidemic management

Yanez

Duggan

Hayes

et al. 2017

2017 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

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Abstract-Pandemics or high impact epidemics are one of the biggest threats facing humanity today. While a complete elimination of the occurrence of such threats is improbable, it is possible to contain their impact by efficient management which in turn depends on effective decision-making. In the event of a pandemic the data flows are enormous and pose severe cognitive overload to the public health decision-makers. In this context, this paper presents PandemCap, an innovative decision support tool that can be used by the public health officials for making better and well informed decisions in the event of pandemics or high impact epidemics. PandemCap provides an interactive, flexible platform to public health decision-makers by making extensive use of techniques from the domains of visual analytics and epidemic modeling. In addition, the tool also allows for the study of the impact of various interventions or control measures such as the use of vaccines, anti-virals, hospital beds, and ventilators.

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Section: B Visual Analyticsmentioning

confidence: 99%

“…Another popular visualization and modelling tool to support decision-making in the domain of public health is PanViz [13]. It is an influenza simulation tool that uses spatio-temporal views to help public health officials understand the impact of decisions over the disease spread.…”

Section: Related Workmentioning

confidence: 99%

PandemCap: Decision support tool for epidemic management

Yanez

Duggan

Hayes

et al. 2017

2017 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

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“…Moreover, in the visual analytics research as a whole, it is usual to address one of the tasks, and only a few works address two of them [1] [46][55] [64]. Our work gives a first example of how all three tasks can be supported as a single workflow.…”

Section: How This Work Extends the State Of The Artmentioning

confidence: 99%

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Andrienko

Rinzivillo

2016

Machine Learning and Knowledge Discovery in Databases

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. Permanent repository linkWe propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.

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“…Regarding economic studies, for example, costeffectiveness analyses of influenza vaccination were first reported in the US in 1997 [27], and later in 2003 in the Netherlands, together with mathematical modelling [31]. Following the 2009 pandemic, the use of these studies, jointly risk and uncertainty assessments, were frequently reported in European countries, the US, and Canada [32][33][34]. Table 1 summarizes the information and factors by segment of the policy chain and gives examples of countries that emphasised them as having a strong impact in the decision-making process of influenza vaccination policy.…”

Section: Information Influencing Decisions and Other Factorsmentioning

confidence: 99%

A literature review to identify factors that determine policies for influenza vaccination

Silva

Perrier

Cohen³

et al. 2015

Health Policy

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The relevance of evidence and factors influencing policy-making varied between countries, according to complex interactions between the stakeholders involved at different levels of decision-making process. These interactions remain unclear, especially at national level, where the most important influenza policy decisions are made. To better define and understand the exact interactions and use of evidence, we recommend undertaking future qualitative studies at national level using small number of countries.

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A pandemic influenza modeling and visualization tool

Cited by 72 publications

References 31 publications

PandemCap: Decision support tool for epidemic management

PandemCap: Decision support tool for epidemic management

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

A literature review to identify factors that determine policies for influenza vaccination

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