One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.

Tracker tags, such as Apple’s AirTags and Tile’s trackers, are small and cheap devices for finding lost items. Tags use Bluetooth technology and community tracking to find the location of lost tags whenever a compatible smartphone passes nearby. When we lose a tag, several questions may arise, like what are the chances of finding it? Or, when will we receive a notification of its detection? Unfortunately, companies such as Apple or Tile do not provide technical information about the efficiency of their tags. Thus, this paper is aimed at answering those questions based on a methodological analysis and evaluation of tags’ efficiency. The main aspects that impact the efficiency of finding a lost tag are the detection range, community size, and human mobility. These aspects are evaluated in a campus scenario, showing that the probability of finding lost tags is very high: more than 98% of them were found, and the average detection time was about 1 hour.