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Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling
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Rydow, Erik, Borgo, Rita, Fang, Hui, Torsney-Weir, Thomas, Swallow, Ben, Porphyre, Thibaud, Turkay, Cagatay and Chen, Min (2022) Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling. IEEE Transactions on Visualization and Computer Graphics . doi:10.1109/tvcg.2022.3209464 ISSN 1077-2626. (In Press)
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Official URL: https://doi.org/10.1109/tvcg.2022.3209464
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.
| Item Type: | Journal Article | |||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine T Technology > T Technology (General) |
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| Divisions: | Faculty of Social Sciences > Centre for Interdisciplinary Methodologies | |||||||||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | COVID-19 Pandemic, 2020- , COVID-19 (Disease) -- Epidemiology -- Mathematical models, Visual analytics , Computer graphics , Visualization | |||||||||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Visualization and Computer Graphics | |||||||||||||||||||||
| Publisher: | Institute of Electrical and Electronics Engineers | |||||||||||||||||||||
| ISSN: | 1077-2626 | |||||||||||||||||||||
| Official Date: | 29 September 2022 | |||||||||||||||||||||
| Dates: |
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| DOI: | 10.1109/tvcg.2022.3209464 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | In Press | |||||||||||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| Date of first compliant deposit: | 12 October 2022 | |||||||||||||||||||||
| Date of first compliant Open Access: | 13 October 2022 | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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