Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Scatter plots are the most commonly employed technique for the visualization of bivariate data. Despite their versatility and expressiveness in showing data aspects, such as clusters, correlations, and outliers, scatter plots face a main problem. For large and dense data, the representation suffers from clutter due to overplotting. This is often partially solved with the use of density plots. Yet, data overlap may occur in certain regions of a scatter or density plot, while other regions may be partially, or even completely empty. Adequate pixel-based techniques can be employed for effectively filling the plotting space, giving an additional notion of the numerosity of data motifs or clusters. We propose the Pixel-Relaxed Scatter Plots, a new and simple variant, to improve the display of dense scatter plots, using pixel-based, space-filling mappings. Our Pixel-Relaxed Scatter Plots make better use of the plotting canvas, while avoiding data overplotting, and optimizing space coverage and insight in the presence and size of data motifs. We have employed different methods to map scatter plot points to pixels and to visually present this mapping. We demonstrate our approach on several synthetic and realistic datasets, and we discuss the suitability of our technique for different tasks. Our conducted user evaluation shows that our Pixel-Relaxed Scatter Plots can be a useful enhancement to traditional scatter plots.

The COVID-19 pandemic is characterized by both health and economic risks. A ‘safety loop’ model postulates risk-related decisions are not based on objective and measurable risks but on the subjective perception of those risks. We here illustrate a quantification of the difference between objective and subjective risks. The objective risks (or chances) can be obtained from traditional 2 × 2 tables by calculating the positive (+LR) and negative (−LR) likelihood ratios. The subjective perception of objective risks is calculated from the same 2 × 2 tables by exchanging the X- and Y-axes. The traditional 2 × 2 table starts with the hypothesis, uses a test and a gold standard to confirm or exclude the investigated condition. The 2 × 2 table with inverted axes starts with the communication of a test result and presumes that the communication of bad news (whether right or false) will induce ‘Perceived Anxiety’ while good news will induce ‘Perceived Safety’. Two different functions (confirmation and exclusion) of both perceptions (Perceived Anxiety and Safety) can be quantified with those calculations. The analysis of six published tests and of one incompletely reported test on COVID-19 polymerase chain reactions (completed by four assumptions on high and low sensitivities and specificities) demonstrated that none of these tests induces ‘Perceived Safety’. Eight of the ten tests confirmed the induction of ‘Perceived Anxiety’ with + LRs (range 3.1–5900). In two of these eight tests, a −LR (0.25 and 0.004) excluded the induction of ‘Perceived Safety’. Communication of test results caused perceived anxiety but not perceived safety in 80% of the investigated tests. Medical tests – whether true or false – generate strong psychological messages. In the case of COVID-19 tests may induce more perceived anxiety than safety. Risk communication has to balance objective and subjective risks.