Artificial Intelligence (AI) Tools for Outbreak Detection and Response: A Transnational Platform for Surveillance, Monitoring and Decision Support
Génin M1, Botz J2, Coudeville L3, Eisenlauer M4, Fauvel T1, Huschka F4, Lambert N1, Wang D2, Bosch Castells V3, Commaille-Chapus C5, Frandji B6, Haberstroh M4, Robin JY5, Roehn P7, Sippel H4, Thiele P7, Thommes E8, Weber C4, Amzal B1, Fröhlich H2, Kannt A9, Mahé C3
1Quinten Health, Paris, Paris, France, 2Fraunhofer SCAI, Sankt Augustin, Germany, 3Sanofi, Lyon, France, 4umlaut consulting GmbH part of accenture, Aachen, Germany, 5Impact Healthcare, Paris, France, 6CompuGroup Medical, Nanterre, France, 7Docmetric GmbH, Koblenz, Germany, 8Sanofi Vaccines, Toronto, ON, Canada, 9Fraunhofer ITMP, Frankfurt, Germany
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.
Conference/Value in Health Info
Value in Health, Volume 26, Issue 11, S2 (December 2023)
Methodological & Statistical Research, Real World Data & Information Systems
Artificial Intelligence, Machine Learning, Predictive Analytics, Data Protection, Integrity, & Quality Assurance
No Additional Disease & Conditions/Specialized Treatment Areas