Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations
Abstract
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.
J. Dykes, A. Abdul-Rahman, D. Archambault, B. Bach, R. Borgo, M. Chen, J. Enright, H. Fang, E. E. Firat, E. Freeman, T. Gönen, C. Harris, R. Jianu, N. W. John, S. Khan, A. Lahiff, R. S. Laramee, L. Matthews, S. Mohr, P. H. Nguyen, A. A. M. Rahat, R. Reeve, P. D. Ritsos, J. C. Roberts, A. Slingsby, B. Swallow, T. Torsney-Weir, C. Turkay, R. Turner, F. P. Vidal, Q. Wang, J. Wood, and K. Xu, “Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations,” Philosophical Transactions of the Royal Society A (Special issue on ’Technical challenges of modelling real-life epidemics and examples of overcoming these’) , vol. 380, no. 2233, p. 20210299, Aug. 2022.
doi:10.1098/rsta.2021.0299
Bibtex
@article{Dykes-et-al-PhilTransA-2022,
author = {Dykes, Jason and Abdul-Rahman, Alfie and Archambault, Daniel and Bach, Benjamin and Borgo, Rita and Chen, Min and Enright, Jessica and Fang, Hui and Firat, Elif E. and Freeman, Euan and G\"{o}nen, Tuna and Harris, Claire and Jianu, Radu and John, Nigel W. and Khan, Saiful and Lahiff, Andrew and Laramee, Robert S. and Matthews, Louise and Mohr, Sibylle and Nguyen, Phong H. and Rahat, Alma A. M. and Reeve, Richard and Ritsos, Panagiotis D. and Roberts, Jonathan C. and Slingsby, Aidan and Swallow, Ben and Torsney-Weir, Thomas and Turkay, Cagatay and Turner, Robert and Vidal, Franck P. and Wang, Qiru and Wood, Jo and Xu, Kai},
title = {Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections \& Recommendations},
journal = {Philosophical Transactions of the Royal Society A (Special issue on 'Technical challenges of modelling real-life epidemics and examples of overcoming these') },
year = {2022},
volume = {380},
number = {2233},
pages = {20210299},
month = aug,
doi = {10.1098/rsta.2021.0299}
}
