Mathematically, finding an optimal model to forecast the contagion patterns of COVID-19 in different conditions (e.g. geographical, social, seasonal variations; different human interventions; etc.) is an intractable problem. The most effective way to tackle a very complex problem is to have workflows that fully utilise both human and machine intelligence, while data visualization and visual analytics (VIS) is indispensable in such a workflow. Some twenty VIS scientists, researchers, and developers in the UK answered a call in May 2020 for VIS volunteers to support modelling scientists and epidemiologists in the Scottish COVID-19 Response Consortium (SCRC), which is one of the three consortia in the Royal Society’s RAMP Programme (Rapid Assistance in Modelling the Pandemic).

The VIS volunteers quickly organised them into seven teams, developing technical solutions to address different visualization needs in several modelling workflows in SCRC. One team designed and developed a VIS infrastructure to enable rapid visualization of dynamic data, including thousands of time series. Another team implemented and tested a variety of visual analytics techniques for extracting meaningful information from the large volume of data. Four teams worked closely with modelling scientists to design VIS tools for analysing and visualizing model-specific data, such as detailed modelling outputs at the 1-km² geographical resolution, contact tracing simulation results, and probabilistic measures resulting from uncertainty analysis. Another team focused on creating storytelling visualization for disseminating information to the public.

According to the VIS volunteers’ web site RAMP VIS, there are currently some 30 VIS volunteers. In the field of data visualization and visual analytics, such a large scale of volunteering VIS operation for emergency response was unprecedented. The work by the VIS volunteers between June-November 2020 is reported in detail in an arXiv report. The UKRI/EPSRC recently offered financial funding to enable this group of VIS scientists, researchers, and developers to continue their development effort in 2021.

The RAMP VIS effort started on 14 May 2020, a lead scientist of the SCRC first e-met Professor Min Chen to discuss the visualization support for the modelling effort. On 15 May, Professor Chen sent a call for VIS volunteers to all VIS academics in the UK and several VIS researchers and developers in the industry. Oxford e-Research centre alumni Dr. Alfie Abdul-Rahman, Dr. Saiful Khan, and Dr. Hui Fang were among the first 22 volunteers who answered the call in May, and they played a significant role in the RAMP VIS operation. Dr. Alfie Abdul-Rahman, who was a post-doctoral researcher at the Centre and is now a lecturer at King’s College London, has been coordinating the generic support team, and will be one of the deputy coordinators of the funded RAMP VIS operation in 2021. Dr. Saiful Khan, who received his DPhil from Oxford and worked as an industrial researcher/developer at Oxford e-Research Centre and two industrial companies, has been the main software architect for the VIS infrastructure. Dr. Khan recently re-joined the Centre as a research software engineer. Dr. Hui Fang, who was a post-doctoral researcher working on an industrial project and is now a lecturer at Loughborough University, contributed data mining solutions to the work of two RAMP VIS teams. In addition, the UK Science and Technology Facilities Council (STFC), which has been providing the SCRC and the RAMP VIS operation with hardware platforms, will now also provide software solution to the SCRC data infrastructure. Another alumnus, Dr. Alejandra N Gonzalez-Beltran, who was a post-doctoral researcher at Oxford e-Research Centre, and now leads the Software Engineering Group in the Scientific Computing Department at STFC; will coordinate the STFC’s effort for developing the SCRC data infrastructure in 2021.

Find out more

RAMP VIS website