Some problems with the data of the covid pandemic

To understand and model complex phenomena, such as the Covid-19 pandemic, it is crucial to have sufficient and quality data. The phrases “measure what is measurable and make measurable what is not,” often attributed to Galileo Galilei, or “we only really know what we are talking about when we are able to measure it”, from Lord Kelvin, capture this principle of modern science and make more sense, if possible, after what we have experienced during these months. However, throughout this crisis we have witnessed numerous episodes of lack of data, changes in its definition – over time or according to its origin –, or lack of completeness of the data. Knowing what type of problem is occurring at any given time is essential to correct, in the statistical analysis, the biases caused and obtain good predictions.

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