# An Ontological Approach for Early Detection of Suspected COVID-19 among COPD Patients
## 1 Introduction  
Since 2019, the world has been affected by the coronavirus 19 (COVID-19) or SARS-CoV-2 (the name of the virus) pandemic, which has caused many deaths. Although all people are exposed, patients with chronic diseases remain the most vulnerable since they face a high risk of death. The mandatory social-distancing measures that have been implemented to combat the high infectiousness of the disease have increasingly encouraged the use of telemedicine. Telemedicine is also used to assist patients from their homes or the hospital. These patients often suffer from breathing difficulties caused by chronic obstructive pulmonary disease (COPD).  
Chronic obstructive pulmonary disease (COPD) encompasses a group of lung conditions that cause breathing difficulties, due to damage to and inflammation of the lungs and airways. The disease consists of emphysema associated with chronic bronchitis [1]. Its common symptoms are increased breathlessness that may occur during exercise or at night, persistent coughing, persistent chest infection, and wheezing [1]. COPD is usually associated with a long history of cigarette smoking. According to [PERSON] [2], COPD patients may experience exacerbations and serious complications after hospital discharge. Exacerbations can be identified and treated. However, within the context of the COVID-19 pandemic, COPD patients may either experience exacerbations or contract COVID-19. Real-time detection of suspected cases would enable doctors to manage cases of COVID-19 and provide appropriate care, different from exacerbation care.  
A mechanism of adaptation is a set of approaches to provide appropriate service based on context. [PERSON] [3] reported that a ubiquitous system provides service anywhere, anytime, depending on the context. In this article, we propose an ontology-based mechanism of adaptation, to identify early COPD patients suspected of having COVID-19. Our ontology is composed of five ontologies reused from the Ajami [4] project, and five ontologies built during our project. Our proposal enables us to establish a generic detection process with the Semantic Web Rule Language (SWRL) engine, at the earliest possible stage, for any abnormal evolution, and, consequently, improves the medical diagnosis for COPD patients with COVID-19 in particular, for elderly patients.  
Protege [5] is an ontology building tool in addition to using \"implement\" language. We propose a solution based on ontology because ontology allows us to manipulate several rules as well as generate alerts and other rules, using the power of the inference engine integrated into the Semantic Web Rule Language (SWRL) language of the Protege [5] platform. In addition, ontology can be used to extract knowledge from other questionnaires or ontologies, enriching the COPD medical domain.  
Our solution is to design a COVID-19 ontology model, as well as an alert system combining vital sign parameters and symptom parameters, to identify suspected early cases of COPD patients with COVID-19. With the power of the SWRL reasoning engines of the Protege [5] Ontology Web Language Description Logics (OWL/DL) tool, this alert system uses standard COVID-19 test criteria [6] and dynamically collected data from the COPD patient context to detect suspected cases. First, if the results of the assessment of vital signs (temperature, fever, and Spo2) are positive, the alert system automatically proposes a questionnaire form to the patient or nurse staff. This questionnaire based on the symptom parameters invites patients to answer a question about their health state.  
Finally, this article is organized as follows: In Section 2, we describe the state-of-the-art and methods; in Section 3, we present an architecture for COVID-19 suspected detection; in Section 4, we provide the experimental results; in Section 5, we discuss contributions and limitations; and in Section 6, we state our conclusions and future work.