Towards a ubiquitous real-time COVID-19 detection system

Abstract

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Citation

Sbai, M., Taktak, H. and Moussa, F. (2022), "Towards a ubiquitous real-time COVID-19 detection system", International Journal of Pervasive Computing and Communications, Vol. 18 No. 2, pp. 211-225. https://doi.org/10.1108/IJPCC-07-2020-0087