Anomaly detection and early risk identification in digital disaster response-based on deep learning in public health

IntroductionIn the evolving landscape of disaster response, integrating advanced digital technologies is critical to enhancing the efficiency and effectiveness of public health systems. Traditional anomaly detection methods often fall short due to their inability to handle the dynamic, heterogeneous, and real-time nature of disaster-related data. These methods typically rely on static models that struggle with integrating continuous data streams from diverse sources like hospitals, emergency services, social media, and environmental sensors. As a result, they often fail to capture sudden shifts in disease patterns, environmental conditions, or population movements, leading to delayed risk identification and suboptimal decisions. The increasing frequency and complexity of natural disasters and pandemics underscore the need for flexible, adaptive systems capable of learning from evolving data. Recent advances in machine learning, artificial intelligence, and big data analytics offer promising tools to address these limitations by enabling real-time, high-dimensional data analysis. In recent years, the integration of advanced digital technologies has become essential for improving pub