Determining Risk Score in Epidemic Diseases Using Fuzzy Logic: The Case of Covid-19

Throughout human history, regional and global epidemics have had lasting impacts on the social, economic, and healthcare structures of societies. Most notably, the recent Covid-19 pandemic, which affected the entire world, has once again highlighted the critical importance not only of individual precautions but also of coordinated, data-driven public health strategies. In this study, based on insights gained from previous outbreaks, a fuzzy logic-based decision support system was developed to quantitatively assess the individual risk level of contracting the disease. The proposed model calculates a personal risk score by evaluating fundamental preventive factors such as adherence to hygiene practices, avoidance of crowded environments, mask usage, and vaccination status. The system has been implemented as a user-friendly application in the Microsoft Visual Studio environment and is designed to operate both online and in integration with mobile/desktop devices. The aim of this application is to enhance individual awareness, support more effective personal preventive measures, and contribute to public-level efforts in epidemic control. In this regard, the study presents an innovative, dynamic, and user-oriented approach that can support decisionmakers not only in the management of current pandemics but also in responding to similar future health crises.