Artificial Intelligence for predictive surveillance of Infectious Disease Outbreaks

The rising rate and speed of spread of infectious disease outbreaks require surveillance systems that precede traditional reporting flows. In this paper, we conduct a systematic exploration of the design, development, and assessment of a predictive surveillance framework based on artificial intelligence technologies for outbreak detection and short-term forecasting. The suggested approach entails the integration of multiple types of data sources – open source epidemic intelligence, syndromic data, mobility data, and climatic variables – into a unified pipeline encompassing natural language processing for event extraction, machine learning ensembles for risk classification, and temporal deep learning for trajectory forecasting. An AI framework involving Bidirectional Encoder Representations from Transformers (BERT) technology for signal classification and Gated Recurrent Unit (GRU) network for case forecasting is described in algorithmic pseudocode and illustrated via system diagrams. Retrospective analysis in multiple outbreak cases indicates that the ensembling technique yields better discrimination than single-model approaches, whereas the GRU model decreases forecast error in early outbreak growth stages. Results are summarized in a comparative table of different models' configurations. We conclude the paper with an identification of practical hurdles in the deployment of an AI-based surveillance system and the consideration of governance strategies for such systems.