Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces

Antweiler, Dario; Sessler, David; Rossknecht, Maxim; Abb, Benjamin; Ginzel, Sebastian; Kohlhammer, Jörn

doi:10.1016/j.cag.2022.05.013

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…Baumgartl et al [17] propose a tool based on synchronized visualization to detect and analyze outbreaks in German Hospitals. Antweiler et al, [18] describe a dashboard based solution that combines graph, temporal and geographical views while Sondag et al [19] propose a dynamic tree based visualization where domain-specific knowledge is used for clustering purposes. Moreover, these authors analyze existing work in graph visualization concluding that the different proposed methods to show global information (also based on other visual paradigms such as Self Organizing Maps, choropleth maps, etc.)…”

Section: Visualizationmentioning

confidence: 99%

Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing

Olaizola

Bruse

Odriozola

et al. 2023

IEEE Comput. Grap. Appl.

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The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified -ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

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Section: Visualizationmentioning

confidence: 99%

Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing

Olaizola

Bruse

Odriozola

et al. 2023

IEEE Comput. Grap. Appl.

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“…From the third aspect, intelligent techniques are introduced to assist in the exploration and investigation of the COVID-19 pandemic. It takes into account more relevant yet not directly-linked complex data which includes modeling [48,50,55,56], predicting [31,[51][52][53][54], and other complex exploration strategies [19,49] with AI algorithms [10]. Reinert et al developed a framework that enables effective and efficient visual exploration through interactive, human-guided analytical environments during the pandemic [76].…”

Section: Visualization In Covid-19 Datasetsmentioning

confidence: 99%

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

Yu¹,

Liang²,

Chen³

et al. 2023

Preprint

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The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we have developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and the multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, which combined COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from LGA-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding of analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors against the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

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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: Background and Related Workmentioning

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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Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces

Cited by 13 publications

References 28 publications

Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing

Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

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