## 2 Materials and Methods  
The first source of data used in this work is that collected by Grandata (this dataset is used in the context of the UNDP LAC call \"Exploring the impact and response to the COVID-19 pandemic in Latin America and the Caribbean using mobility data\" sponsored (no funding, data only) by the Research Team/Policy Response Office of the Chief Economist, Regional Office for Latin America and the Caribbean.) [42]. Since 1 March 2020, geolocation events of smartphone owners were recorded by the Grandata group, using a MAID (Mobile Advertising ID) \"hash\". These events track the mobility patterns of smartphone users for a considerable sample of the population of twelve Latin American countries (In the case of the Santiago Metropolitan Region, a sample of approximately 10% of the population is considered). To distinguish only individuals for which there are enough data, unique users with less than 10 daily geolocation events (displacements or changes in the user's geolocation) or those whose daily events only took place within 8 h were excluded from the dataset. The average number of geolocation events of the individuals in the remaining data sample is 130, recording a piece of valuable information about each user's mobility history. For unique users, the mode of their geolocation events (or the location most frequented) was assumed to be their residency, and all other events are considered outings or \"out-of-home\" events. All home events were then deleted, and the remaining ones are aggregated for a specific area or region to create mobility indicators. These geo-referenced data were aggregated for hexagonal areas constructed using the H3 geospatial indexing system [48]. For each area, all the outing events that took place within it, irrespective of where the user's home is located, were added up in that area's mobility indicators. In essence, H3 works by dividing the surface of the earth into a number of homogeneous hexagon tiles, each one of which is indexed using a hierarchical linear index. This system has an advantage over the traditional longitude-latitude coordinate system, since it better fits the world's curvature, offering more precise spatial representation. Mobilitydata are considered at different resolution levels. The H3 system includes up to 15 levels of precision, the 15 th level dividing the map in the smallest hexagons possible (the most precise), and the 0 levels using the largest ones (the least precise). We used level 6 hexagons in the H3 geospatial system, which spans 0.74 km2. Each hexagon's index serves both as a unique identifier, as well as a geospatial coordinate. The H3 website [48] includes documentation on how to obtain longitude and latitude coordinates from an H3 index and vice versa. Mobility indicators for the hexagons are not indexes, but rather the corresponding hexagon's percentile placement in the mobility distribution, counting all hexagons within the MR. From now on we will call it \"score\". For the analysis, the MR is covered with hexagons, in each of them, the _score_ is determined from the geolocation events that occur inside. The values of these scores indicate daily percentiles of the movements/mobility that take place within the area marked by the hexagon. The percentiles are calculated with respect to the score distribution accounting for all the geolocation events given in all the hexagons that cover the MR. In panel C of Figure 1, these scores are plotted for a given working day. The highest scores/percentiles are in blue, while the lowest is yellow, passing through a range of greens. The second source of mobility data comes from the payment smart-card use in the MR public transport system and the buses/Metro geo-coded information [44]. Every week, over 35 million smart-card transactions are recorded in the MR public transport. Throughout the week, similar numbers of transactions per working day of approximately six million are observed.