# How Human Mobility Models Can Help to Deal with COVID-19
## 2 Related Work  
Surveillance and control of emerging infectious diseases are vital for public health. The utilisation of new technologies, such as internet-based surveillance, infectious diseases modelling, remote sensing, telecommunications, and mobile phones, can predict, prevent, and control these infectious diseases [13]. It is a new approach to deal with epidemics, usually categorised with a newly coined term: Digital Epidemiology [2]. A recent technology which can be used to cope with COVID-19 is Opportunistic Networks (OppNets). OppNets are based on the opportunity of contacts between mobile devices (that is when they are in communication range) as a way to propagate messages. The effectiveness of OppNets depends mainly on users' mobility. The dependence between mobility and the performance of OppNets have been studied extensively, see for example [7; 14; 15; 16; 17]. For example, [PERSON] et. al. [16] studied the impact of node density on the data dissemination time using a synthetic mobility model. Other proposals, such as [18; 19], study the message dissemination behaviour of the Epidemic protocol by focusing on the mobility patterns of the nodes, evaluating the relationship between factors such as mobility model, speed and node density, and locations of the nodes. Other papers are more focused on evaluating the impact of human behaviour in the opportunistic forwarding of messages [20; 21]. In general, the previous results show that information diffusion increases with the node density and the number of contacts, mobility being the main enabler of opportunistic data dissemination [22]. Since mobility is a key factor for evaluating OppNets, several synthetic models have been devised in order to capture this human mobility. From the basic models, such as _Random Walk_ and _Random Waypoint_[23], to more realistic models that consider some social aspects of human movements like working days and meal hours. Nevertheless, these synthetic models can only capture some specific characteristics of human mobility. Therefore, given the limitation of these synthetic models, some recent papers use pedestrian simulators for evaluating the mobility of pedestrians. In [11], the authors introduce a complex model for streets based on queues, along with contact and duration probabilities. This model is compared with simulation results using the commercial pedestrian simulator LEGION. Using this pedestrian simulator, the authors in [8] study the impact of mobility and the scenario used on opportunistic communication (inter-contact time and contact duration). The results show that, as expected, the type of scenario is the most important aspect to consider, and so a general model cannot be derived. Mobility models and infection dynamics are also related to Network Science, since the transmission of COVID-19 requires a contact or at least proximity, and this is a network of contacts [24]. So, the dynamics of the COVID-19 can be explained using techniques of network science. Regarding the risk of transmission, it depends on the following factors: contact pattern, host-related infectivity/susceptibility pattern, environment, and socioeconomic status [25]. Early studies of COVID-19 provided evidence that sustained close contact drives the majority of infections. For instance, family/friend gatherings or travelling on public transport were found to have a higher risk for transmission than brief (<10 min) encounters [26; 27]. Furthermore, findings from contact tracing studies suggest a higher risk of transmission indoors compared with outdoor environments [10]. That is, prolonged contact in an enclosed setting can lead to increased risk of transmission. Finally, recent studies have focused on evaluating the risk of airborne transmission of SARS-CoV-2 as a function of time using the known Wells-Riley equation [28; 29; 30; 31] or the Drake equation [32].