  We report on an ongoing collaboration between epidemiological modellers and
visualization researchers by documenting and reflecting upon knowledge
constructs -- a series of ideas, approaches and methods taken from existing
visualization research and practice -- deployed and developed to support
modelling of the COVID-19 pandemic. Structured independent commentary on these
efforts is synthesized through iterative reflection to develop: evidence of the
effectiveness and value of visualization in this context; open problems upon
which the research communities may focus; guidance for future activity of this
type; and recommendations to safeguard the achievements and promote, advance,
secure and prepare for future collaborations of this kind. In describing and
comparing a series of related projects that were undertaken in unprecedented
conditions, our hope is that this unique report, and its rich interactive
supplementary materials, will guide the scientific community in embracing
visualization in its observation, analysis and modelling of data as well as in
disseminating findings. Equally we hope to encourage the visualization
community to engage with impactful science in addressing its emerging data
challenges. If we are successful, this showcase of activity may stimulate
mutually beneficial engagement between communities with complementary expertise
to address problems of significance in epidemiology and beyond.
https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/
