Since December 2019, the global pandemic caused by the highly infectious novel coronavirus 2019-nCoV (COVID-19) has been rapidly spreading. As of April 2020, the outbreak has spread to over 210 countries, with over 2,400,000 confirmed cases and over 170,000 deaths. 1 COVID-19 causes a severe pneumonia characterized by fever, cough and shortness of breath. Similar coronavirus outbreaks have occurred in the past causing severe pneumonia like COVID-19, most recently, severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). However, over time, SARS-CoV and MERS-CoV were shown to cause extrapulmonary signs and symptoms including hepatitis, acute renal failure, encephalitis, myositis and gastroenteritis. Similarly, sporadic reports of COVID-19 related extrapulmonary manifestations emerge. Unfortunately, there is no comprehensive summary of the multiorgan manifestations of COVID-19, making it difficult for clinicians to quickly educate themselves about this highly contagious and deadly pathogen. What is more, is that SARS-CoV and MERS-CoV are the closest humanity has come to combating something similar to COVID-19, however, there exists no comparison between the manifestations of any of these novel coronaviruses. In this review, we summarize the current knowledge of the manifestations of the novel coronaviruses SARS-CoV, MERS-CoV and COVID-19, with a particular focus on the latter, and highlight their differences and similarities.

We propose a simple and efficient method to obtain unweighted deterministic samples of the multivariate Gaussian density. It allows to place a large number of homogeneously placed samples even in high-dimensional spaces. There is a demand for large high-quality sample sets in many nonlinear filters. The Smart Sampling Kalman Filter (S2KF), for example, uses many samples and is an extension of the Unscented Kalman Filter (UKF) that is limited due to its small sample set. Generalized Fibonacci grids have the property that if stretched or compressed along certain directions, the grid points keep approximately equal distances to all their neighbors. This can be exploited to easily obtain deterministic samples of arbitrary Gaussians. As the computational effort to generate these anisotropically scalable point sets is low, generalized Fibonacci grid sampling appears to be a great new source of large sample sets in high-quality state estimation.

Whole-chamber mapping using a 64-pole basket catheter (BC) has become a featured approach for the analysis of excitation patterns during atrial fibrillation. A flexible catheter design avoids perforation but may lead to spline bunching and influence coverage. We aim to quantify the catheter deformation and endocardial coverage in clinical situations and study the effect of catheter size and electrode arrangement using an in silico basket model. Atrial coverage and spline separation were evaluated quantitatively in an ensemble of clinical measurements. A computational model of the BC was implemented including an algorithm to adapt its shape to the atrial anatomy. Two clinically relevant mapping positions in each atrium were assessed in both clinical and simulated data. The simulation environment allowed varying both BC size and electrode arrangement. Results showed that interspline distances of more than 20 mm are common, leading to a coverage of less than 50% of the left atrial (LA) surface. In an ideal in silico scenario with variable catheter designs, a maximum coverage of 65% could be reached. As spline bunching and insufficient coverage can hardly be avoided, this has to be taken into account for interpretation of excitation patterns and development of new panoramic mapping techniques.

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