Visualization and Visual Analytics in Support of Rapid Assistance in Modelling the Pandemic (RAMP)
RAMPVIS is a group of volunteers specialised in Data Visualization and Visual Analytics, who answered a call to support the modelling scientists and epidemiologists in the Scottish COVID-19 Response Consortium (SCRC).
Most complex models in the literature took decades and sometimes centuries to develop. Many were built on a substantial amount of post-hoc evidence and analysis, while many are still being improved today. The success can usually be attributed to a collective effort by generations of modelling scientists in data collection, observation, and analysis; hypothesis formulation; and model development, validation, deployment, monitoring, and improvement. In combating COVID-19, such a collective effort in decades has to be compressed into a period of weeks and months. Without adequate visualization (VIS) and visual analytics (VA) support, a modelling workflow typically compels modelling scientists and epidemiologists to spend more time acquiring information from data, to observe less data or make less frequent observations, to rely solely on data mining algorithms blindly without keeping track of potential limitations and errors, or to rely solely on intuition without using algorithms to explore different options. Hence, there is an urgent need to increase the VIS/VA capacity in such rapid model development workflows, and to maintain the provision of this capacity throughout the period of combating the COVID-19 pandemic. There are many different visualization tasks in different model-developmental workflows. They can be broadly categorised into four levels according to the size and complexity of the search space for an optimal decision.
Level 1. Disseminative visualization, which is a presentational aid for disseminating information or insight to others. It is commonly mistaken as the only or main purpose of visualization. Because a few governmental websites and public services have been providing inforgraphics to depict some COVID-19 data, it will be unwise to allocate much VIS/VA resource to repeat the existing provision in the UK. Our requirements analysis indicates that there is a scope for developing more engaging visualization using animated or interactive storytelling techniques.
Level 2. Observational visualization, which is an operational aid that enables intuitive and/or fast observation of captured data and simulation results. Our requirements analysis shows that even just counting time series alone, there are more time series in the COVID-19 context than any major stock market. Furthermore, there are many other forms of data featuring spatial, temporal, multivariate, network, and movement information. Meanwhile, unlike any stock market, there is no infrastructure in the UK that enables hundreds of modelling scientists and epidemiologists to visualize captured data and simulation results routinely at their fingertips.
Level 3. Analytical visualization, which is an investigative aid for examining and understanding complex relationships (e.g., correlation, association, causality, contradiction, etc.). Despite the advancement of data mining technology, our requirements analysis shows that most modelling scientists and epidemiologists do not have adequate access to data mining algorithms, such as time series analysis, classification and clustering, association analysis, and dimensionality reduction, nor the visualization techniques that accompany such data mining algorithms.
Level 4. Model-developmental visualization, which is a developmental aid for improving existing models, methods, algorithms, and systems, as well as for creating new ones. These visualization techniques are typically model-specific and developed through close collaboration between VIS/VA specialists and modelling scientists. Our requirements analysis shows that while almost all modelling scientists use visualization to depict simulation results and model predictions, most do not have access to VA specialists for developing techniques such as ensemble visualization and parameter optimization.