Objective. The COVID-19 is considered to be a highly contagious pathogen. Since December 2019, an orthocoronavirinae virus has caused pneumonia identified as a new type of respiratory infection. This respiratory syndrome has quickly spread to all countries around the world to the point where WHO has declared it pandemic. To date, the source of this virus is not well known, especially since there are no standards for its diagnosis and treatment. Several factors are involved in the spread of the disease. This study tries to make a contribution to the analysis of its spread and the people likely to be affected. Also, the immune response of patients differs from one patient to another, which makes analysis very complex by classical mathematical techniques. As long as the several uncertainties persist to date concerning it, we propose to analyze the relevant factors using fuzzy logic. As this logic takes into account the imprecise and the uncertain, we consider that its application in this area proves to be adequate.
Methods. Based on the factors reported in different studies concerning this disease established to date as well as the characteristics of the people affected, we have established a fuzzy logic analysis system. The input variables of the system represent the age of the affected patients, their comorbidity (ie sub-adjacent diseases, the confinement degree, the screening policy and the availability of control means) and the output variable expresses the disease rate.
Results. By doing so, the uncertainties linked to the very nature of the individuals affected, the uncertainties and imprecision inherent in ignorance of the disease and its mechanisms are thus compensated. Once the rule base has been established from actual cases recorded, it becomes possible to predict the degree of certainty of the COVID-19 infringement.
Conclusion. Several unknowns still persist as to the origin of the virus, its mechanism, its spread and the lack of a vaccine and even less treatment, the WHO declared it a pandemic. This study takes up certain factors recorded and reported by previous studies to give a preventive analysis. The application of a fuzzy system overcomes these inaccuracies. The proposed system remains extensible to other factors not supported in this study and which may prove to be relevant over time.