AI-Enhanced Early Warning System for Respiratory Pandemic Threats

COVID-19 exposed serious limitations in early detection and coordinated response to emerging respiratory pandemics. While vast data sources exist (syndromic surveillance, mobility, social signals), they are underused for predictive AI-driven preparedness systems that are fast, scalable, and regionally actionable.

To develop and deploy an AI-powered early warning platform that enables local and national public health bodies to anticipate, assess, and act on emerging respiratory infectious threats (COVID variants, influenza, RSV) through multimodal data fusion and real-time AI analytics.

The primary objective of this project is to develop an AI-powered early warning system that enables timely detection and response to respiratory pandemic threats by integrating and analyzing diverse, real-world data streams.

To achieve this, the project will first build a multimodal data fusion infrastructure capable of combining syndromic surveillance ( symptom reports, GP visit data), anonymized electronic health records, human mobility trends, social media signals, and environmental indicators such as air quality.

This system will support dynamic, region-specific data ingestion using open standards to ensure compatibility and interoperability. A central component of the project is the development of an AI-based outbreak detection and forecasting engine.

This engine will apply lightweight, privacy-preserving machine learning models to identify anomalies, forecast disease spread over short- and long-term horizons, and generate actionable risk insights. The use of explainable AI (XAI) techniques will ensure that model predictions are interpretable, highlighting contributing factors behind each alert or forecast and supporting evidence-based decisions by public health officials.

To operationalize these insights, the project will design an interactive decision-support dashboard tailored for health authorities. The dashboard will visualize risk levels, confidence scores, outbreak hotspots, and AI-generated recommendations in an intuitive format, including tiered alerts and push notifications via mobile or email.

This interface aims to bridge the gap between data scientists and public health responders, making complex analytics accessible and actionable. The platform will be piloted and validated in two European cities or regions with varying levels of digital readiness (Dublin and Riga).

This real-world validation will assess the accuracy, usability, and policy relevance of the system under practical constraints. Public health professionals will be involved throughout the pilot to ensure that the tool meets their operational needs and integrates with existing workflows. Finally, the project will emphasize interoperability, ethics, and governance. All data processing will comply with GDPR and other relevant EU frameworks, using privacy-preserving techniques such as federated learning or edge processing where necessary.

Ethical experts will be engaged to establish transparent governance models and support public communication, helping to build trust in AI-based early warning systems. Together, these objectives will provide a comprehensive, practical, and trustworthy solution for respiratory pandemic preparedness in Europe.

The project is designed to support a range of practical and high-impact use cases that are critical for effective pandemic preparedness and response. One primary use case is the early detection of unusual spikes in influenza-like illness, RSV, or other respiratory infections based on aggregated primary care visit data and real-time symptom reporting. By identifying these trends ahead of traditional surveillance methods, health authorities can act faster to contain outbreaks.

Technology Stack

Our project will leverage a modern and modular technology stack that balances analytical power, scalability, and data privacy. At the core of the system are advanced AI models such as LSTM networks for temporal trend analysis, XGBoost and random forests for classification, and transformer-based architectures for integrating multimodal inputs. For spatial and temporal forecasting, the system will employ spatiotemporal models capable of learning from geo-tagged health and mobility data. To ensure transparency and user trust, explainable AI (XAI) tools like decision trees will be incorporated to interpret model outputs.

On the user-facing side, the dashboard and alert system will be built using Python-based frameworks such as Streamlit or Dash (Plotly), offering a lightweight yet powerful interface for health authorities.

Deliverables:

D1.1 - Multimodal data integration framework - Month 6

D2.1 - AI prediction models validated on historical outbreaks - Month 12

D3.1 - Public health dashboard prototype - Month 15

D4.1 - Real-world pilot evaluation report (Dublin, Riga) - Month 30

D5.1 - Final toolkit for deployment and policy integration - Month 36