TY - JOUR
AU - Dykes, Jason
AU - Abdul-Rahman, Alfie
AU - Archambault, Daniel
AU - Bach, Benjamin
AU - Borgo, Rita
AU - Chen, Min
AU - Enright, Jessica
AU - Fang, Hui
AU - Firat, Elif E.
AU - Freeman, Euan
AU - Gonen, Tuna
AU - Harris, Claire
AU - Jianu, Radu
AU - John, Nigel W.
AU - Khan, Saiful
AU - Lahiff, Andrew
AU - Laramee, Robert S.
AU - Matthews, Louise
AU - Mohr, Sibylle
AU - Nguyen, Phong H.
AU - Rahat, Alma A. M.
AU - Reeve, Richard
AU - Ritsos, Panagiotis D.
AU - Roberts, Jonathan C.
AU - Slingsby, Aidan
AU - Swallow, Ben
AU - Torsney-Weir, Thomas
AU - Turkay, Cagatay
AU - Turner, Robert
AU - Vidal, Franck P.
AU - Wang, Qiru
AU - Wood, Jo
AU - Xu, Kai
PY - 2022
DA - October
TI - Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
VL - 380
IS - 2233
DO - https://doi.org/10.1098/rsta.2021.0299
AB - 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. See: https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/
PB - The Royal Society
UR - https://www.research.ed.ac.uk/en/publications/f25add9c-d2d9-4803-93f1-0a7429e13cbd
KW - Coronavirus (COVID-19)
ER