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

Experimental results across multiple datasets (EM-DAT, FEMA, UNOSAT, Earthquake) demonstrate that our proposed model improves anomaly detection performance by 23% in precision and reduces false alarms by 31% compared to baseline models. The method combines LSTM and transformer-based architectures to effectively analyze spatiotemporal data, offering both high accuracy and interpretability for public health experts.