Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling

Erik Rydow, Rita Borgo, Hui Fang, Thomas Torsney-weir, Ben Swallow, Thibaud Porphyre, Cagatay Turkay, Min Chen

Research output: Contribution to journalArticlepeer-review

2 Downloads (Pure)

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.
Original languageEnglish
Article number9906007
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
VolumeEarly Access
DOIs
Publication statusPublished - 29 Sept 2022

Keywords

  • Sensitivity analysis
  • Ensemble visualization
  • COVID-19
  • Epidemiological modeling
  • Epidemiology

Fingerprint

Dive into the research topics of 'Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling'. Together they form a unique fingerprint.

Cite this