Abstract. We propose a topology-based segmentation of 2D symmetric tensor fields, which results in cells bounded by tensorlines. We are particularly interested in the influence of the interpolation scheme on the topology, considering eigenvector-based and component-wise linear interpolation. When using eigenvector-based interpolation the most significant modification to the standard topology extraction algorithm is the insertion of additional vertices at degenerate points. A subsequent Delaunay re-triangulation leads to connections between close degenerate points. These new connections create degenerate edges and triangles. When comparing the resulting topology per triangle with the one obtained by component-wise linear interpolation the results are qualitatively similar, but our approach leads to a less "cluttered" segmentation.
This data consists of 245 clinically recovered patients. The first part of the paper studies the descriptive statistics and the influence of demographic parameters, namely age and gender, in the clinical recovery-period of COVID-19 patients. The second part of the paper is on identifying the distribution of the length of the recovery-period for the patients. We identify a piecewise analysis of three different periods, identified based on trends of both positive confirmation and clinical recovery of COVID-19. As expected, the overall recovery rate has reduced drastically during the exponential increase of incidences. However, our in-depth analysis shows that there is a shift in the age-group of incidences to the younger population, and the recovery-period of the younger population is considerably lower. Here, we have estimated the recovery rate to be 0.125. Overall, the prognosis of COVID-19 indicates an improvement in recovery rate owing to the government-mandated practices of restricted mobility of the older population and aggressive contact tracing.
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