Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling
Date
29/09/2022Author
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Abstract
Computational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted , and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.
Citation
Rydow , E , Borgo , R , Fang , H , Torsney-weir , T , Swallow , B , Porphyre , T , Turkay , C & Chen , M 2022 , ' Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling ' , IEEE Transactions on Visualization and Computer Graphics , vol. Early Access , 9906007 . https://doi.org/10.1109/TVCG.2022.3209464
Publication
IEEE Transactions on Visualization and Computer Graphics
Status
Peer reviewed
ISSN
1077-2626Type
Journal article
Rights
Copyright © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/TVCG.2022.3209464.
Description
Funding information: Authors would like to thank UKRI/EPSRC “RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19” (EP/V054236/1), Scottish Government Rural and Environment Science and Analytical Services Division, Centre of Expertise on Animal Disease Outbreaks (EPIC), French National Research Agency and Boehringer Ingelheim Animal Health France for support through the IDEXLYON project (ANR-16-IDEX-0005), the Industrial Chair in Veterinary Public Health, as part of Lyon VPH Hub.Collections
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