Public Health Security Systems Empowered by Artificial Intelligence for Early Monitoring and Prevention of Epidemics

Abstract

Artificial intelligence (AI) methods have been extensively used for detecting and predicting Infectious Disease (ID) outbreaks as time series and modeling and evaluating Public Health responses. The significant tasks of PH monitoring and intervention present distinct technical difficulties, including limited data availability, absence of sufficient positive training examples, challenges in establishing benchmarks, measuring the effectiveness of management policies, complex relationships between spatial and time series elements, and more detailed risk assessments involving interaction and social networks. Conventional PH monitoring mainly depends on statistical methods. In recent years, there has been a significant expansion of approaches that AI enables. This research presents an AI method called Early Monitoring and Prevention of Epidemics (AI-EMPE) for enhancing PH security systems. The suggested approach converts a substantial amount of collected PH security incidents into separate incident characteristics and utilizes a Deep Learning (DL)--based detection technique to enhance EMPE. AI-EMPE incorporates sophisticated AI methods, including integrated Convolutional Neural Networks (CNN) and Backpropagation Neural Networks (BP-NN). These findings indicate that the integrated method is the most efficient in improving PH security systems.