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Using AI to Strengthen Infectious Disease Surveillance Systems

Using AI to Strengthen Infectious Disease Surveillance Systems

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 🎙️ Episode Title Using AI to Strengthen Infectious Disease Surveillance Systems --- 🧠 Episode Summary In this episode of The Innovation Forum AI Podcast, Oliver Morgan speaks with Swapnil Mishra, from the National University of Singapore (NUS). Swapnil explains how AI and infectious disease modelling can move beyond academic exercises and become embedded within real public health workflows. Drawing on examples from dengue surveillance, vector-borne disease risk assessment, mobility data integration, and behavioral modelling, he discusses how AI can help public health agencies interpret surveillance data, combine fragmented datasets, and make decisions under uncertainty. The conversation explores the trade-offs between mechanistic models, Bayesian approaches, and machine learning; the role of hierarchical modelling in understanding local heterogeneity; and why separating transmission dynamics from reporting bias is essential for responsible epidemic intelligence. Swapnil also emphasizes that AI should augment public health professionals rather than replace them, and that the real transformation lies in strengthening institutional capacity for infectious disease decision making. --- 💬 Guest Dr. Swapnil Mishra is an Assistant Professor at the Saw Swee Hock School of Public Health at the National University of Singapore (NUS) and Deputy Director of the Centre for Epidemic Research and Modelling (CERM) and the AI for Public Health programme (AI4PH). His research focuses on infectious disease modelling, Bayesian statistics, and machine learning applied to epidemic and pandemic intelligence. He previously contributed modelling advice during the COVID-19 pandemic and works at the intersection of epidemiology, data science, and public policy. --- 🌐 Resources and References - NUS Saw Swee Hock School of Public Health: https://sph.nus.edu.sg/; https://www.linkedin.com/school/saw-swee-hock-school-of-public-health/?originalSubdomain=sg - Centre for Epidemic Research and Modelling: https://cerm.nus.edu.sg/ - Artificial intelligence for public health can harness data for healthier populations: https://www.nature.com/articles/s44360-025-00005-w.pdf - Estimating dengue force of infection from age-stratified surveillance data in Java, Indonesia: https://royalsocietypublishing.org/rsif/article/22/232/20250445/356293 - Incorporating human mobility to enhance epidemic response and estimate real-time reproduction numbers: https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1013642 - Mitigating risks of malaria and other vector-borne diseases in the new capital city of Indonesia: https://www.nature.com/articles/s41467-024-54891-x --- 🎵 Music Credits Intro and outro music from Podcastle Stock Audio. Track: ‘Nairobi Nights’. License code: XUUYW3CDJ38EEGHM. --- ⚠️ 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 to help us reach more public health professionals exploring the future of AI.
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