ABSTRACT
The COVID-19 pandemic generated large amounts of diverse data, including testing, treatments, vaccine trials, data from modeling, etc. To support epidemiologists and modeling scientists in their efforts to understand and respond to the pandemic, there arose a need for web visualization and visual analytics (VIS) applications to provide insights and support decision-making. In this paper, we present RAMPVIS, an infrastructure designed to support a range of observational, analytical, model-developmental, and dissemination tasks. One of the main features of the system is the ability to "propagate" a visualization designed for one data source to similar ones, this allows a user to quickly visualize large amounts of data. In addition to the COVID pandemic, the RAMPVIS software may be adapted and used with different data to provide rapid visualization support for other emergency responses.
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.
