Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.
computational notebooks
epidemiological modelling
visual analytics
visual design
visualization
Journal
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
ISSN: 1471-2962
Titre abrégé: Philos Trans A Math Phys Eng Sci
Pays: England
ID NLM: 101133385
Informations de publication
Date de publication:
03 Oct 2022
03 Oct 2022
Historique:
entrez:
15
8
2022
pubmed:
16
8
2022
medline:
17
8
2022
Statut:
ppublish
Résumé
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/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Identifiants
pubmed: 35965467
doi: 10.1098/rsta.2021.0299
pmc: PMC9376715
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20210299Commentaires et corrections
Type : ErratumIn
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