Towards a ubiquitous real-time COVID-19 detection system

In view of the intensive spread of Coronavirus disease 2019 (COVID-19) and in order to reduce the rate of spread of this disease; the objective of this article is to propose an approach to detect in real time suspect person of Coronavirus disease 2019 (COVID-19).

The ubiquitous computing offers a new opportunity to reshape the form of conventional solutions for personalized services according to the contextual situations of each environment. The health system is seen as a key part of ubiquitous computing, which means that health services are available anytime, anywhere to monitor patients based on their context. This paper aims to design and validate a contextual model for ubiquitous health systems designed to detect in real time suspect person of COVID-19, to reduce the propagation of this infectious disease and to take the necessary instructions.

This paper presents the performance results of the COVID-19 detection approach. Thus, the reduction of the COVID-19 propagation rate thanks to the real-time intervention of the system.

Following the COVID-19 pandemic spread, the authors tried to find a solution to detect the disease in real time. In this paper, a real-time COVID-19 detection system based on the ontological description supported by Semantic Web Rule Language (SWRL) rules was developed. The proposed ontology contains all relevant concepts related to COVID-19, including personal information, location, symptoms, risk factors, laboratory test results and treatment planning. The SWRL rules are constructed from medical recommendations.