OBJECTIVES: Numerous digital surveillance tools were developed during the COVID-19 pandemic to support public health decisions. Yet, those ad-hoc tools were essentially for short-term insights, more reactive than predictive, and not generalisable to be used to predict hospital capacities or shortages of medical supplies in real-time. To overcome these limitations, we developed, under a public-private consortium, a transnational predictive platform to detect the first signs of respiratory epidemics, monitor progression, and assist in defining and evaluating appropriate measures.

METHODS: We developed AIOLOS, a web-based AI-powered monitoring and decision support tool, using integrative modeling and simulations informed by multiple data sources (wastewater, social media, mobility data). Trained for now on COVID-19 historical data in France and Germany at both regional and national levels, this platform aims at detecting early signs of a new viral epidemic, monitoring its spread, and informing public health decisions to optimize its social, public health and economic impact.

RESULTS: The first project year was dedicated to the development, testing and calibration of models while engaging and aligning across partners and stakeholders. A first version of the tool was developed and dashboarded with visuals, delivering promising preliminary results , e.g. highlighting and quantifying the value of wastewater data in predicting pandemic waves, and the impact of social distancing and vaccination on epidemics.

CONCLUSIONS: Improvements will be made in the coming year e.g. by integrating new data sources and partners, accounting for more pathogens and respiratory viruses and enabling real-time prediction updates. AIOLOS stands as a serious candidate to become an EU-wide public health decision support tool.