Towards the Detection and Visual Analysis of COVID-19 Infection Clusters

Antweiler, Dario; Sessler, David; Ginzel, Sebastian; Kohlhammer, Jörn

doi:10.2312/eurova.20211097

Cited by 3 publications

(2 citation statements)

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“…For COVID-19 specifically, a number of visual analytics systems for situational awareness and policy decisions have been proposed [MHW * 21, DHA * 20, LSC * 20], including from a geospatial perspective for simulations [AGJS * 20]. In terms of visual analytics systems to support contact tracing visualizations, there has been some work that has integrated link prediction with visualizations to visualize potential clusters of COVID-19 contacts [ASGK21]. Although there has been significant work on the dynamics of a pandemic from a variety of perspectives at a high level, this work focuses on visual analytics to support the dynamic relationships in an individual based simulation supporting contact tracing.…”

Section: Visualization For Public Health and Pandemicsmentioning

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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Section: Visualization For Public Health and Pandemicsmentioning

confidence: 99%

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

Sondag

Turkay

et al. 2022

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…As an extension to our original publication [3] , our added contributions are as follows: We complemented the temporal and graph-based dashboard with a geospatial visualization component, displaying user-selected chains of infections on an interactive map.…”

Section: Introductionmentioning

confidence: 99%