TY - JOUR
AU - Rydow, Erik
AU - Borgo, Rita
AU - Fang, Hui
AU - Torsney-Weir, Thomas
AU - Swallow, Ben
AU - Porphyre, Thibaud
AU - Turkay, Cagatay
AU - Chen, Min
PY - 2022
DA - September
TI - Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling
JO - IEEE Transactions on Visualization and Computer Graphics
DO - https://doi.org/10.1109/TVCG.2022.3209464
AB - 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.
PB - IEEE
UR - http://hdl.handle.net/10023/26249
KW - Coronavirus (COVID-19)
ER