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/
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This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.
Since the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. As a vibrant sub‐field of information visualization and visual analytics, many interactive EHR and PopHR visualization (EHR Vis) systems have been proposed, developed, and evaluated by clinicians to support effective clinical analysis and decision making. We present the state‐of‐the‐art (STAR) of EHR Vis literature and open access healthcare data sources and provide an up‐to‐date overview on this important topic. We identify trends and challenges in the field, introduce novel literature and data classifications, and incorporate a popular medical terminology standard called the Unified Medical Language System (UMLS). We provide a curated list of electronic and population healthcare data sources and open access datasets as a resource for potential researchers, in order to address one of the main challenges in this field. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions.
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