RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses

Chen, Min; Abdul‐Rahman, Alfie; Archambault, Daniel; Dykes, Jason; Ritsos, Panagiotis D.; Slingsby, Aidan; Torsney-Weir, Thomas; Turkay, Çağatay; Bach, Benjamin; Borgo, Rita; Brett, Alys; Fang, Hui; Jianu, Radu; Khan, Saiful; Laramee, Robert S.; Matthews, Louise; Nguyêñ, Phong Q.; Reeve, Richard; Roberts, Jonathan C.; Vidal, Franck; Wang, Qiru; Wood, Jo; Xu, Kai

doi:10.1016/j.epidem.2022.100569

Cited by 18 publications

(16 citation statements)

References 82 publications

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“…Visualization theory [ 74 ] was used to structure this activity, specifically to establish the broad applicability of visualization in modelling workflows and communicate this persuasively to modelling scientists [ 75 ]. Activities involving various modelling workflows were supported concurrently ( figure 2 ) by creating seven groups to work in parallel.…”

Section: Introduction Context and Intentmentioning

confidence: 99%

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

Dykes

Abdul‐Rahman

Archambault

et al. 2022

Phil. Trans. R. Soc. A.

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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’.

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Section: Introduction Context and Intentmentioning

confidence: 99%

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

Dykes

Abdul‐Rahman

Archambault

et al. 2022

Phil. Trans. R. Soc. A.

Self Cite

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“…At the beginning of our collaboration in May 2020, we did not have the opportunity to select our collaborators or the problem: our contact tracing team was formed through a national‐level organization, RAMPVIS through the Royal Society, for the COVID‐19 response; all we had was our enthusiasm to help and our respective expertise. Also, the expectation was to respond quickly to the modelling needs by developing visualization prototypes in an environment where the requirements and priorities were constantly changing (our definition of emergency response and that of RAMPVIS [CARA∗21]). A carefully designed visualization would have taken too long and would be less useful in an evolving pandemic situation.…”

Section: Emergency Response: Early Days To Prototypesmentioning

confidence: 99%

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

Sondag

Turkay

et al. 2022

Computer Graphics Forum

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Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex 'infection maps' of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns. CCS Concepts• Applied computing → Health informatics; • Human-centered computing → Visualization design and evaluation methods; Visual analytics;

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“…Visualization theory [26] was used to structure this activity, specifically to establish the broad applicability of visualization in modelling workflows and communicate this persuasively to modelling scientists [25]. Activities involving various modelling workflows were supported concurrently (Fig.…”

Section: Rapid Assistance In Modelling the Pandemicmentioning

confidence: 99%

“…This initial effort has been consolidated by a follow-on project funded by the UKRI/EPSRC through their COVID 19 Rapid Response programme -RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19. Running until January 2022, this subsequent 12-month project has helped maintain and develop the activity generated through the volunteering, while providing additional resources to enable further capability, research and communication [25,56].…”

Section: Rapid Assistance In Modelling the Pandemicmentioning

confidence: 99%

“…A series of academic articles, both published and in preparation, in the visualization and epidemiological modelling domains, report on specific results that combine technology, design and data successfully in new and revealing ways, e.g. [25,56]. This paper does not focus in detail on the specific developments that took place.…”

Section: Challenges Solutions Reflection and Recommendationsmentioning

confidence: 99%

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Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations

Dykes,

Abdul-Rahman,

Archambault

et al. 2022

Preprint

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RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses

Cited by 18 publications

References 82 publications

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations

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