[Automated surveillance and risk prediction with the aim of risk-stratified infection control and prevention (RISK PRINCIPE)]. / Automatisierte Surveillance und Risikovorhersage mit dem Ziel einer risikostratifizierten Infektionskontrolle und -prävention (RISK Prediction for Risk-stratified Infection Control and Prevention).

Healthcare-associated infections (HCAIs) represent an enormous burden for patients, healthcare workers, relatives and society worldwide, including Germany. The central tasks of infection prevention are recording and evaluating infections with the aim of identifying prevention potential and risk factors, taking appropriate measures and finally evaluating them. From an infection prevention perspective, it would be of great value if (i) the recording of infection cases was automated and (ii) if it were possible to identify particularly vulnerable patients and patient groups in advance, who would benefit from specific and/or additional interventions.To achieve this risk-adapted, individualized infection prevention, the RISK PRINCIPE research project develops algorithms and computer-based applications based on standardised, large datasets and incorporates expertise in the field of infection prevention.The project has two objectives: a) to develop and validate a semi-automated surveillance system for hospital-acquired bloodstream infections, prototypically for HCAI, and b) to use comprehensive patient data from different sources to create an individual or group-specific infection risk profile.RISK PRINCIPE is based on bringing together the expertise of medical informatics and infection medicine with a focus on hygiene and draws on information and experience from two consortia (HiGHmed and SMITH) of the German Medical Informatics Initiative (MII), which have been working on use cases in infection medicine for more than five years.

Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges.

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v=m·10e. We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyze error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.

A Problem Space for Designing Visualizations.

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related "frameworks" that provide abstractions of the problems a visualization is meant to address. In this Visualization Viewpoints article, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations.

Tracing COVID-19 Infection Chains Within Healthcare Institutions - Another Brick in the Wall Against SARS-CoV-2.

Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.

Insights by Visual Comparison: The State and Challenges.

Data comparison is one of the core tasks in exploratory analysis, which combines algorithmic analysis and interactive visualization in a visual data comparison process. Comparison of large and complex datasets requires several steps-i.e., a workflow. This article discusses the comparison process, its research challenges, and examples of solutions.

Typology of Uncertainty in Static Geolocated Graphs for Visualization.

Static geolocated graphs have nodes connected by edges, where both can have geographic location and associated attributes. For example, it can be uncertain exactly where a node is located or whether an edge between two nodes exists. Because source data is often incomplete or inexact, it is necessary to visualize this uncertainty to help users make appropriate decisions. The proposed typology of uncertainty extends related typologies with specific features needed for characterizing uncertainty in static geolocated graphs.

Visualization System Requirements for Data Processing Pipeline Design and Optimization.

The rising quantity and complexity of data creates a need to design and optimize data processing pipelines-the set of data processing steps, parameters and algorithms that perform operations on the data. Visualization can support this process but, although there are many examples of systems for visual parameter analysis, there remains a need to systematically assess users' requirements and match those requirements to exemplar visualization methods. This article presents a new characterization of the requirements for pipeline design and optimization. This characterization is based on both a review of the literature and first-hand assessment of eight application case studies. We also match these requirements with exemplar functionality provided by existing visualization tools. Thus, we provide end-users and visualization developers with a way of identifying functionality that addresses data processing problems in an application. We also identify seven future challenges for visualization research that are not met by the capabilities of today's systems.

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering.

Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods.

Visual exploration of parameter influence on phylogenetic trees.

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.