Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

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/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Fifteen-minute consultation: The EATERS method for the diagnosis of food allergies.

The EATERS mnemonic is a novel method for taking an allergy focused clinical history. It provides a degree of certainty for diagnosing food allergy and can be used in both IgE and non IgE mediated reactions. EATERS will allow health care professionals to use their existing clinical skills to interpret the history of an allergic reaction, and by doing so will help to make sense of allergy test results.

Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling.

Computational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted, and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.

Oficanthon Paulian, 1985, a junior synonym of Lepanus Balthasar, 1966 (Coleoptera: Scarabaeidae: Scarabaeinae), with redescription of Lepanus mirabilis (Paulian, 1985).

The monotypic genus Oficanthon Paulian, 1985 (Coleoptera: Scarabaeidae: Scarabaeinae) is treated as a junior synonym of the genus Lepanus Balthasar, 1966, and L. mirabilis (Paulian, 1985), new combination is proposed for Oficanthon mirabilis Paulian, 1985. Both morphological and molecular evidence support this synonymy. Morphological evidence further supports its placement within the Lepanus ustulatus species group. Lepanus mirabilis is redescribed.

Complex model calibration through emulation, a worked example for a stochastic epidemic model.

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.

Revision of Australian species of the dung beetle genus Lepanus (Coleoptera: Scarabaeidae: Scarabaeinae): review of the L. ustulatus, L. storeyi, and L. nitidus species groups and description of eight new species.

This publication is the third part of an ongoing revision of Australian species of the genus Lepanus (Coleoptera: Scarabaeidae: Scarabaeinae) and revises three species groups. Keys to species within the L. ustulatus, L. storeyi, and L. nitidus species groups are provided and eight new species are described. Within the L. ustulatus species group, L. ustulatus (Lansberge, 1874) and L. globulus (Macleay, 1887) are redescribed and three new species are described: Lepanus cameroni new species from Cape York Peninsula, far north Queensland; Lepanus cardwellensis new species from the Australian Wet Tropics, northern Queensland; and Lepanus lemannae new species from the Australian Wet Tropics to the Central Mackay Coast, Queensland. Within the L. storeyi species group, L. storeyi Weir Monteith, 2010 is redescribed and two new species are described: Lepanus meierae new species from southeastern Queensland to Wollongong, New South Wales and Lepanus williamsi new species from eastern New South Wales. Within the L. nitidus species group, L. nitidus Matthews 1974 and L. dichrous Gillet, 1925 are redescribed and three new species are described: Lepanus vangerweni new species, Lepanus carbinensis new species, and Lepanus kulki new species from the Australian Wet Tropics, northern Queensland. Following these descriptions, a total of 50 Lepanus species are now described from Australia.

Revision of Australian species of the dung beetle genus Lepanus (Coleoptera: Scarabaeidae: Scarabaeinae): description of 10 new species from the L. australis, L. monteithi, and L. villosus species groups.

This publication is part in an ongoing revision of the Australian species of the genus Lepanus Balthasar, 1966 (Coleoptera: Scarabaeidae: Scarabaeinae) and revises the three species groups with simple pygidia. Keys to species within the L. australis Matthews, 1974, L. monteithi Matthews, 1974, and L. villosus Matthews, 1974 species groups are provided and a total of 10 new species are described. Within the L. australis species group, L. australis Matthews, 1974 and L. latheticus Matthews, 1974 are redescribed and four new species are described: Lepanus burnetti new species and Lepanus oxleyi new species from southeastern Queensland; Lepanus eungella new species from coastal, central Queensland; and Lepanus dukungarri new species from Australian Wet Tropics, northern Queensland. Within the L. monteithi species group, L. monteithi Matthews, 1974 is redescribed and four new species are described: Lepanus geoffi new species from Cape York Peninsula, far north Queensland; Lepanus yorkensis new species from Torres Strait and Cape York Peninsula, far north Queensland; Lepanus reidi new species from the Australian Wet Tropics and Lepanus topend new species from the northern Northern Territory. Within the L. villosus species group, L. villosus Matthews, 1974 and L. vestitus Matthews, 1974 are redescribed and two new species are described: Lepanus pseudovillosus new species and Lepanus reticulatus new species from the Australian Wet Tropics, northern Queensland. Including these descriptions a total of 43 Lepanus species are now described from Australia.

Revision of Australian species of the dung beetle genus Lepanus (Coleoptera: Scarabaeidae: Scarabaeinae): key to species groups and description of 14 new species from the L. pygmaeus species group.

Lepanus Balthasar, 1966 (Coleoptera: Scarabaeidae: Scarabaeinae) is one of the most diverse genera of dung beetles in Australasia and includes large numbers of undescribed species. This publication is the first part in an ongoing revision of the Australian members of this genus and outlines ten informal species groups. Fourteen new species that are similar to Lepanus pygmaeus (Macleay, 1888) are described here: Lepanus mckenziei new species from the Pilbara region of Western Australia; Lepanus sauroni new species from coastal regions of the Northern Territory and the Kimberley region of Western Australia; Lepanus gubara new species, Lepanus pecki new species, Lepanus podocarp new species and Lepanus pungalina new species from the Top End, Northern Territory; Lepanus lentil new species from the Top End, Northern Territory and central Queensland; Lepanus crenidens new species, Lepanus lingziae new species, and Lepanus menendezae new species from northern Queensland; Lepanus andersonorum new species, and Lepanus tozerensis new species from Cape York Peninsula, far north Queensland; Lepanus feehani new species from the Australian Wet Tropics; and Lepanus guthrieae new species from the east to west coast of northern Australia. Lepanus pygmaeus is redescribed and a lectotype designated for it. A lectotype is designated for Panelus arthuri Blackburn, 1900. The synonymy of L. pygmaeus with P. arthuri is confirmed. A key to species groups within Lepanus is provided, as well as a key to species in the L. pygmaeus species group.

Predicting the Interactive Rendering Time Threshold of Gaussian Process Models With HyperSlice.

In this paper we present a method for predicting the rendering time to display multi-dimensional data for the analysis of computer simulations using the HyperSlice [36] method with Gaussian process model reconstruction. Our method relies on a theoretical understanding of how the data points are drawn on slices and then fits the formula to a user's machine using practical experiments. We also describe the typical characteristics of data when analyzing deterministic computer simulations as described by the statistics community. We then show the advantage of carefully considering how many data points can be drawn in real time by proposing two approaches of how this predictive formula can be used in a real-world system.

WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making.

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.

Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices.

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available.

Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration.

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.