Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey

Abstract

Infectious diseases usually have the characteristics of rapid spread with a large impact range. Once they break out, they will cause a large area of infection, which creates tremendous health and security risks. Thus, early infectious disease monitoring and prevention are critical. Current surveillance systems can predict the incidence of infectious diseases to a certain extent. However, the diversity, inaccuracy and incompleteness of the data collected by sensors make it difficult to obtain accurate monitoring results. Moreover, the limited local resources of a monitoring system cannot process the increasing volume of data in a timely manner. To address these challenges, fuzzy logic and edge computing have been applied to infectious disease monitoring in recent years. This paper presents a comprehensive review of infectious disease monitoring technologies based on fuzzy logic and edge computing. Fuzzy neural networks in infectious disease surveillance are introduced in detail, followed by a brief study of applications of fuzzy systems in infectious disease surveillance. Finally, improvements in existing disease detection systems based on the combination of edge computing and fuzzy logic are described. The review shows that edge computing and fuzzy logic are complementary and that their combination greatly improves the processing efficiency and the storage space of the data. At the same time, with edge computing as the carrier, the combination of fuzzy logic, neural networks, expert systems and other technologies can effectively carry out disease prediction and diagnosis.