Table 4.
Summary of the papers on contact tracing reviewed in this survey.
| Setting | References | Platform/application | Approaches |
|---|---|---|---|
| Wojtusiak et al. (2021) | Wifi | Five complex algorithms | |
| Dmitrienko et al. (2020) | Deterministic classifiers | ||
| Mcheick et al. (2021) | Wifi-Direct | Statistical analysis | |
| Jeong et al. (2019) | Magnetometer | Pearson correlation coefficient | |
| Indoor | Gouissem et al. (2021) | Wifi and Bluetooth | Mathematical models |
| Abueg et al. (2021) | Individual-based model | Statistical analysis and ENS | |
| Thangamani et al. (2020) | Wifi and IoT | Machine learning | |
| kumar et al. (2003) | IoT | Machine learning | |
| Lo and Sim (2021) | Smartphone apps framework | Manual and digital | |
| Li et al. (2020b) | Household cohort study | Statistical analysis | |
| Yi et al. (2021) | Cellular | Deep neural networks | |
| Khatib et al. (2021) | Statistical analysis | ||
| Shi et al. (2021) | Smartphone | Social network, mobility, Susceptible Exposed Infected Recovered (SEIR) | |
| Tu et al. (2021) | Wifi | Convolutional neural network | |
| Outdoor | Malloy et al. (2022) | Contact graph | |
| Maghdid and Ghafoor (2020) | GPS | Clustering | |
| Elbir et al. (2020) | Vehicular network | Machine and federated learning | |
| Otoom et al. (2020) | Machine learning | ||
| Gupta et al. (2021) | IoT | Machine learning and cloud computing | |
| Ting et al. (2020) | deep learning | ||
| Schmidtke (2020) | Tracking application | Proximity, fingerprinting, and triangulation |