In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.
