Abstract: The COVID-19 pandemic has remained a global health crisis following the declaration by the World Health Organization. As a result, a number of mechanisms to contain the pandemic have been devised. Popular among these are contact tracing to identify contacts and carry out tests on them in order to minimize the spread of the coronavirus. However, manual contact tracing is tedious and time consuming. Therefore, contact tracing based on mobile applications have been proposed in literature. In this paper, a cross platform contact tracing mobile application that uses deep neural networks to determine contacts in proximity is presented. The application uses Bluetooth Low Energy technologies to detect closeness to a Covid-19 positive case. The deep learning model has been evaluated against analytic models and machine learning models. The proposed deep learning model performed better than analytic and traditional machine learning models during testing.

Josephat Kalezhi, Mathews Chibuluma, Christopher Chembe, Victoria Chama, Francis Lungo and Douglas Kunda, “A Cross Platform Contact Tracing Mobile Application for COVID-19 Infections using Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130872

@article{Kalezhi2022,
title = {A Cross Platform Contact Tracing Mobile Application for COVID-19 Infections using Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130872},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130872},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Josephat Kalezhi and Mathews Chibuluma and Christopher Chembe and Victoria Chama and Francis Lungo and Douglas Kunda}
}