Integrating predictive analytics into a spatiotemporal epidemic simulation

Bryan, Chris; Xue, Wenrui; Mniszewski, Susan M.; Ma, Kwan‐Liu

doi:10.1109/vast.2015.7347626

Cited by 20 publications

(29 citation statements)

References 28 publications

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“…Explore enables the user to examine and identify interesting data subsets, and this function can be critical in modeling. In predictive visual analytics, explore interactions are especially common in weather forecasting [DPD*15, HMC*13], environmental management [MBH*12], and epidemic simulation [BWMM15] where large, high‐dimensional data is being modeled. Höllt et al [HMC*13] present a system for interactive visual analysis in ocean forecasting where panning is used to explore the ocean surface (Figure b).…”

Section: Interactions In Pvamentioning

confidence: 99%

“…Connect interactions highlight links and relationships between entities or bring related items into view. Additionally, connect can be used to highlight features of the same entity distributed throughout a visualization grouped by features, such as a treemap [CGSQ11], or to highlight the same item across multiple coordinated views [BWMM15]. As shown in Figure , the treemap displays the multidimensional clusters of people based on their risk of having different disease.…”

Section: Interactions In Pvamentioning

confidence: 99%

“…Broadly, it is knowledge about what is relevant. Germane knowledge forms a basis for setting good model hyper‐parameters, choosing appropriate thresholds or cut‐offs, selecting relevant ranges or data subsets [BJA*15, MBD*11], and excluding outliers from the analysis process [BWMM15].…”

Section: Future Directions In Pvamentioning

confidence: 99%

See 2 more Smart Citations

The State‐of‐the‐Art in Predictive Visual Analytics

García

Hansen

et al. 2017

Computer Graphics Forum

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Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.

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Section: Interactions In Pvamentioning

confidence: 99%

Section: Interactions In Pvamentioning

confidence: 99%

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The State‐of‐the‐Art in Predictive Visual Analytics

García

Hansen

et al. 2017

Computer Graphics Forum

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“…Since these parameters are estimated, they carry some uncertainty. Instead of simulating the outbreak only once with precise input values, multiple simulations runs with slightly changed input parameters reveal the space of possible developments [BWMM15]. The resulting ensemble data reflect the temporal and spatial development.…”

Section: Analysis and Control Of Epidemicsmentioning

confidence: 99%

“…The incubation and infection times are modelled as a probability distribution. For mosquito-borne diseases, for example, the local differences in mosquito count are essential but can only be guessed based on land coverage data (parks have a higher count than office buildings) [BWMM15]. For infectious diseases, differences in the local population density affect the development.…”

Section: Simulation Of Spreadingmentioning

confidence: 99%

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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EpiDesktop—A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios

Derdouri

Osaragi

2021

Progress in IS

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Integrating predictive analytics into a spatiotemporal epidemic simulation

Cited by 20 publications

References 28 publications

The State‐of‐the‐Art in Predictive Visual Analytics

The State‐of‐the‐Art in Predictive Visual Analytics

A Survey of Visual Analytics for Public Health

EpiDesktop—A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios

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