# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
## 2 Related Works
Other models also consider exposed individuals with incubation periods (asymptomatic individuals with no capacity of infection), such as the model developed by [PERSON] et al. [28] for the SARS disease, based on the SEIR model (susceptible-exposed (noninfectious)-infectious-recovered), or the incubation time used in the simulation process, such as the one presented by [PERSON] et al. [29]. [PERSON] et al. [30] utilized population-based modeling using a stochastic model. The authors estimated physical contacts from communication traces obtained using mobile phones, and thus helped to quarantine people selectively. They showed that contact tracing is only effective at the beginning of the outbreak due to the quickly increasing costs as the epidemic grows. [PERSON] et al. [31] introduced a different method to model the spread of infectious diseases by analyzing the dynamics of the nodes using temporal graphs. Other papers have studied the technology behind contact tracing. For example, [PERSON] et al. [32] have evaluated the potential of using Bluetooth. Their results show that Bluetooth received signal strength can vary substantially depending on the relative orientations of handsets, on absorption by the human body and reflection/absorption of radio signals in buildings and trains. A more detailed study on the technological aspects that can affect the precision of smartphone-based contact tracing has been recently introduced by [PERSON] et al. [33]. Finally, for further information about automated contact tracing technologies, please refer to the following recent surveys [34; 35]. Regarding the COVID-19 pandemic, the following authors have evaluated its impact: [PERSON] et al. [36] state that contact tracing and isolation as currently practiced is not helping to prevent the COVID-19 pandemic. This is essentially due to the large number of undetected asymptomatic infected individuals helping to spread the infection. Therefore, the authors propose the use of smartphones to trace the prior contacts, proving that even when not all the population is willing to use the application, it can be feasible to contain epidemics (although the required portion should be significantly high). Another study by [PERSON] et al. [37] showed that isolation and tracing alone were unlikely to be sufficient to keep an outbreak under control, and additional measures would need to be implemented simultaneously. Moreover, we confirm that a high level of application adoption is crucial to make digital contact tracing an effective measure. Finally, [PERSON] et al. [38] drew similar conclusions: reducing the testing delay, that is, shortening the time between symptom onset and test positivity, is the most crucial aspect to enhance the effectiveness of contact tracing. [PERSON] et al. [29] obtained a similar outcome using a simulated model. That is, in most scenarios, very efficient contact tracing and people isolation is enough to control a new COVID-19 outbreak when at least 79% of the contacts are traced. These strong requirements make smartphone-based contact tracing an infeasible solution. Finally, [PERSON] [39] has also proven, using an analytical model, that moderate rates of adoption of a contact tracing application can reduce the reproductive number \(R_{0}\), but are by no means sufficient to reduce it below 1 (that is, a control of the outbreak) unless other measures are taken. Wrapping up, most of the previous papers are focused on the epidemiological aspects of the disease from a medical point of view. Our paper is more oriented toward evaluating the impacts that the design and architecture of the contact tracing application have.