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Putting Problem-Driven AI into Practice in a European Public Health Agency

Putting Problem-Driven AI into Practice in a European Public Health Agency

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 🎙️ Episode Title Putting Problem-Driven AI into Practice in a European Public Health Agency --- 🧠 Episode Summary In this episode of The Innovation Forum AI Podcast, Oliver speaks with Laura Espinosa, Epidemic Intelligence Expert at the European Centre for Disease Prevention and Control (ECDC), about what it really means to apply AI inside a European public health agency. Rather than starting with technology, Laura explains why AI initiatives must begin with clearly defined public health problems. Drawing on ECDC’s experience integrating automation, machine learning, and language models into real epidemic intelligence workflows, she discusses which AI applications actually survive the transition from concept to routine use. The conversation explores early detection using open and social data, machine learning for prioritizing signals and detecting anomalies, and the often-overlooked impact of automation in improving routine surveillance and reporting. Laura also reflects on the use of large language models for unstructured data, the trade-offs between open-source and commercial tools, and the importance of interdisciplinary collaboration in making AI work in practice. The episode highlights a central lesson: in public health, AI succeeds not because it is sophisticated, but because it is purposeful, explainable, and embedded into real workflows. --- 💬 Guest Laura Espinosa is an Epidemic Intelligence Expert at the European Centre for Disease Prevention and Control (ECDC), where she leads the implementation of AI, automation, and data-driven methods to support public health surveillance and epidemic intelligence workflows. Her work spans early threat detection using open-source information, machine learning for signal prioritization, automation of long-term monitoring systems, and the responsible integration of language models into public health practice. She holds a PhD from EPFL, where she focused on the use of AI and language models to analyze public health-related discourse in unstructured data. --- 🌐 Resources and References - ECDC: https://www.ecdc.europa.eu/ - Episomer GitHub repository: https://github.com/EU-ECDC/episomer. - Evaluation of epitweetr (manual vs automated monitoring of Twitter): https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.39.2200177 - Using artificial intelligence to improve epidemic intelligence processes: development and impact measurement of R templates for validating and summarising public health threats (2023 ESCAIDE's abstract book): https://www.escaide.eu/en/publications-data/escaide-2023-abstract-book - Finding the needle in the haystack: using machine learning to detect signals of public health threats in the Epidemic Intelligence from Open Sources (2025 ESCAIDE's abstract book): https://www.escaide.eu/en/publications-data/escaide-2025-abstract-book - Laura Espinosa's PhD thesis "Leveraging social media data and large language models for understanding public health behaviours in the context of infectious diseases and vaccination": https://infoscience.epfl.ch/entities/publication/6a06987d-d2c0-4a59-8a8c-d0e3e2fe6621 --- 🎵 Music Credits Intro and outro music from Podcastle Stock Audio. Track: ‘Nairobi Nights’. License code: 5R9AFJJRMWJAVLNB. --- ⚠️ Disclaimer This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative: https://pandemichub.who.int/news-room/innovation-forum. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice. --- 📲 Listen and Subscribe The Innovation Forum AI Podcast is available on YouTube, Spotify, Apple Podcasts, and Amazon Music. You can find a written summary of this episode here: https://substack.com/@omorgan? Follow, rate, and share!
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