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Experimental observations of the collapse of initially static columns of sand in axisymmetric and two-dimensional geometries are presented. The experiments were carried out using cylinders and rectangular tanks 30 to 60 cm tall, and cover aspect ratios between 0.5 and 20, where the aspect ratio is defined as the ratio of the initial height to horizontal extent of the column. The final positions of sand grains from different points initially on the outer surface of the columns are mapped. For all axisymmetric columns the point of maximum runout is found to originate from a point at fractional height 0.74 ± 0.03 of the initial vertical height of the column, independent of the aspect ratio. For two-dimensional columns the corresponding point is 0.65 ± 0.07. Collapses of columns of water-saturated sand into water display a different form of flow, which leads to there being no such well-defined point. In this case, grains from all but the innermost, basal areas of the initial column can end up in the outermost region of the final deposit. For collapses in air and aspect ratios greater than 1, the detail of the initial geometry is relatively insignificant in determining the shape of the final deposit. The results of this and previous studies thus have general applicability, even to situations with less initial symmetry. Movies are available with the online version of the paper.

Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.

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