# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
## 3 Smartphone-Based Contact Tracing
### Contact Tracing Applications  
Traditionally, contact tracing is carried out manually by trained people who investigate a recent positive patient in order to obtain their previous contacts or locations visited. This method is widely used for some kinds of diseases (especially sexually transmitted ones) where the contacts are easy to identify. Nevertheless, contact tracing is a costly and lengthy procedure, involving personal interviewsof the patient, family and acquaintances. Nevertheless, in most of the infectious diseases, personal interviews only provide very poor tracing of previous contacts and locations. The goal of contact tracing applications is to predict exposure to a given infection based on detecting contacts using some of the current localization or communication technologies, such as GPS, Wi-Fi, mobile phones and Bluetooth. In this paper we focus on Bluetooth-based applications since most of the current solutions for the COVID-19 disease are based on this technology. The idea is that, by using Bluetooth, one can identify the surrounding devices and also infer the distance to them. To this end, mobile nodes have to scan and periodically store the information of all these surrounding devices, using the received signal strength indicator (RSSI), for calculating the distance between the devices. For example, if Bluetooth is used, a contact trace of the nearby devices can be obtained with a resolution of 1-2 m, so it can be used to detect close personal contacts that are more likely to produce infections. Exposure to COVID-19 is calculated as a function of time and distance to an infected person. Experiments have shown that the exposure risk decreases for distances beyond 2 m, and for periods shorter than 15 min [40]. Additionally, it is considered that it is required to trace back for a period of only two weeks. In order to preserve privacy, mobile phones exchange anonymous key codes when a potential nearby contact is identified. No information is sent about the people using the smartphone or their location. Therefore, this process generates data with the possible users' contacts that complies with the countries' regulations on data protection and privacy. This has lead to two different models for handling and storing these data: centralized and decentralized. Please, see Figure 1 for a visual illustration of the differences between the two different approaches. In the centralized model, the anonymized data are uploaded from people's mobile phones to centralized servers. Then, health authorities can check, notify and manage previous contacts. On the contrary, the decentralized model locally stores these key codes and allows the users, firstly, to notify (or not) a positive, so the mobile application will upload the last 14 days of locally stored keys to the server; and secondly, to voluntary check (or not) their risk exposure, that is, whether they have been in contact with someone who may have been infected by downloading the uploaded keys from the server, and matching locally against the stored keys for evaluating the risk of exposure. For example, the decentralized option is the approach followed by Apple and Google. The API provided by these companies allows health authorities to develop their contact tracing solution. Regarding privacy considerations, both approaches (centralized and decentralized) can comply with privacy and data protection regulations, although the decentralized one may offer a higher degree of privacy. Both approaches use the same method of exchanging anonymized key codes for identifying nearby contacts. The differences are where and when the checking for possible positive contacts is performed. Since, in the centralized model, the matching of possible positive contact is performed in centralized servers, it provides health authorities more control and information about the disease spreading and the social relationships between infected individuals.