EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control

Yang, Chuang; Zhang, Z; Zhang, Fan; Jiang, Renhe; Chen, Q; Song, Xia; Shibasaki, Ryosuke

doi:10.1109/tvcg.2022.3165385

Cited by 17 publications

(9 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The studies that used a map-style representation (79/114) adopted different strategies: static geographic 2D or 3D maps [14,15,19,20,22,31,33,36,38,[42][43][44]50,75,81,83,88,93,102,104,107,109,117,118,123], dynamic 2D maps [20][21][22]25,61,65,80,85,89,90,94,97], or choropleth maps [17,23,24,27,30,32,34,35,37,40,46,48,49,52,…”

Section: Q2: Which Techniques Are Most Frequently Used To Visualize E...mentioning

confidence: 99%

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Kim,

Cánovas-Segura,

Campos

et al. 2024

Technologies

View full text Add to dashboard Cite

In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics.

show abstract

Section: Q2: Which Techniques Are Most Frequently Used To Visualize E...mentioning

confidence: 99%

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Kim,

Cánovas-Segura,

Campos

et al. 2024

Technologies

View full text Add to dashboard Cite

show abstract

“…Zhao et al, for example, introduced six main data visualizations that are frequently collaborated as public platforms in China during the COVID-19 pandemic [69], while Dong et al [18] presented UcVE as a progressive spatiotemporal comparison of the COVID-19 situation in multiple locations. Additionally, Phyloview [33], a visual analytics system, examines the spatiotemporal evolution of COVID-19 over time, while EpiMob [26], a visual interface for city-wide human mobility restrictions to control epidemics, simulates changes in human mobility and the infection states that may result from implementing a particular restriction policy (or combination of policies). Olaizola et al [31] also proposed a visual analytics platform designed to centralize digital contact tracing for COVID-19.…”

Section: Visual Analytics In Covid-19mentioning

confidence: 99%

“…This is a need that has been listed in a systematic survey as a valuable future direction of research to prevent the spread of disease [6]. In these situations, despite the introduction of adequate visual analytics methods [18], [26], [34] and AI4VIS [25], [27], [32] with the goal of providing interactions and artificial intelligence advantages to deal with data correlations and other complexities, dynamic and multi-aspect visual analyses of COVID-19 remain insufficient.…”

Section: Introductionmentioning

confidence: 99%

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

Yu¹,

Liu²,

Cao³

et al. 2023

Preprint

View full text Add to dashboard Cite

Clinic testing plays a critical role in containing infectious diseases such as COVID-19. However, one of the key research questions in fighting such pandemics is how to optimize testing capacities across clinics. In particular, domain experts expect to know exactly how to adjust the features that may affect testing capacities, given that dynamics and uncertainty make this a highly challenging problem. Hence, as a tool to support both policymakers and clinicians, we collaborated with domain experts to build ClinicLens, an interactive visual analytics system for exploring and optimizing the testing capacities of clinics. ClinicLens houses a range of features based on an aggregated set of COVID-19 data. It comprises Back-end Engine and Front-end Visualization that take users through an iterative exploration chain of extracting, training, and predicting testing-sensitive features and visual representations. It also combines AI4VIS and visual analytics to demonstrate how a clinic might optimize its testing capacity given the impacts of a range of features. Three qualitative case studies along with feedback from subject-matter experts validate that ClinicLens is both a useful and effective tool for exploring the trends in COVID-19 and optimizing clinic testing capacities across regions. The entire approach has been open-sourced online: https://github.com/YuDong5018/clinic-lens.

show abstract

“…With the pandemic as a backdrop, there have already remained two main kinds of COVID-19 data research in visualization: micro-perspective in scientific visualization with this virus that is analyzed from a biomedical perspective and combined with clinical medicine (Nguyen et al 2021;Liu et al 2021). And macro-perspective in multiple related COVID-19 datasets, which contain such infection cases, recovery, and mortality rates with COVID and connected with social factors like geographical (Goetschel et al 2021), social media and journalism (Yu et al 2021;Leite et al 2020), trajectory of human mobility (Yang et al 2022), and other factors (Antweiler et al 2021;Gharizadeh et al 2020;Hua et al 2020).…”

Section: Visual Analytics Of Covid-19 Datamentioning

confidence: 99%

“…Jiang et al (2020) presented a knowledge graph system to interactively explore epidemic situations. Yang et al noticed that based on crowd movement and control measures may have an impact on the epidemic, so they proposed EpiMob (Yang et al 2022), a VA system which simulates the changes in human mobility and infection status. However, most of these visualization methods focus on displaying data to specific populations (Jadhav et al 2021; or using infectious disease models (Afzal et al 2020) to analyze and predict data (Srabanti et al 2021), whereas GIS-based geographic information systems can only pre-mark sampling points on the map and cannot interact, limiting the human-computer interaction of extended analysis capabilities and making joint analysis difficult for decision makers.…”

Section: Visual Analytics Of Covid-19 Datamentioning

confidence: 99%

User-centered visual explorer of in-process comparison in spatiotemporal space

Dong

Ian

Liu

et al. 2022

J Vis

View full text Add to dashboard Cite

We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback. Graphical abstract

show abstract

EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control

Cited by 17 publications

References 71 publications

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

User-centered visual explorer of in-process comparison in spatiotemporal space

Contact Info

Product

Resources

About