# How Human Mobility Models Can Help to Deal with COVID-19
## 1 Introduction  
The eruption of the COVID-19 pandemic has become a severe threat to our society and our way of living. For the time being, more than a million people have died, more than 10 million are infected, and the collateral effects of this pandemic are affecting each and every part of our society and economy. There is no sign that its effects will diminish in the short term, as vaccines are still in test stages and it seems that no new effective treatments have emerged. Other approaches are based on leveraging the increasing availability of data, communications, and computing power. The so-called Digital Epidemiology takes advantage of this technology to track infectious diseases and to allow new approaches to deal with epidemics [1]. Digital Epidemiology is epidemiology that uses data that was generated outside the public health system [2], as for example, mobile phones tracking information, social network tendencies, and mobility models. For example, contact tracing can be considered a more selective isolation measure targeted to the population most likely to have the infection [1]. The utilisation of current smartphones has led to the creation of what is called smartphone-based contact tracing, which is used to detect the exposure to risky contacts [3, 4, 5, 6]. For example, Apple and Google have recently teamed up to include the necessary support into their smartphone operating systems to implement these applications. Most of the technology of these mobile applications are based on the results of years of research on mobile computing and singularly on Opportunist Networks. Opportunistic Networks use proximity contacts of mobile devices to exchange messages in a way to spread information among these devices. Their behaviour and dynamics can be comparable to the epidemic spread of a disease, so the models developed to evaluate opportunistic networks are usually adaptations of known epidemic models [7]. Thestudy of human mobility and social behaviour is also essential for evaluating information dissemination in these opportunistic networks [8]. A lot of mobility models have been used for evaluating the transmission of messages in Opportunistic Networks. Much of these models are from related mobile networks, such as Mobile Ad hoc Networking (MANET) and DTN (Delay Tolerant Networks). In current mobile networks, mobility and the number of users in a scenario can vary during the evaluation time, and therefore it is necessary to have scenarios or models that try to reproduce human behaviour as truly as possible. The general idea of this paper is to evaluate how Opportunistic Networks (OppNets) mobility models can be applied to the fighting against COVID-19. Particularly, these models can be used to evaluate the exposure risk of contagion in a given place. Whereas in OppNets the idea is to evaluate the opportunity of transmitting a message when two mobile devices contacts (that is, there are within communication range), in this paper the idea is to evaluate the opportunity of transmitting an infection when two individuals are close. COVID-19, the disease, is caused by the SARS-CoV-2, the virus, which is a strain of coronavirus. There is overwhelming evidence that the SARS-CoV-2 virus inhalation represents a major transmission route for COVID-19 [9]. The SARS-CoV-2 virus is transmitted mainly by droplets and aerosols and are highly concentrated near an infected person, so they can infect people more easily in close proximity. The virus transmissibility is also affected by the quality of the medium, i.e. air renewal, temperature, and solar radiation which can reduce their transmission. From the beginning of the COVID-19 pandemic, it was shown that there is a higher risk of transmission indoors compared with outdoor environments [10]. In this paper, we generate several scenarios to evaluate the temporal and spatial risk of being infected. We start with basic synthetic scenarios, such as Random WayPoint Model (RWP), Small World in Motion (SWIM), and Random WayPoint Model with attraction points (RWPa).