https://ojwatson.co.uk/tags/software/softwareSource Themes Academic (https://sourcethemes.com/academic/)en-us© 2026 OJ WatsonMon, 16 Feb 2026 00:00:00 +0000https://ojwatson.co.uk/img/headers/newyork.jpghttps://ojwatson.co.uk/tags/software/https://ojwatson.co.uk/2026/02/16/ai-epidemic-modelling-nature-2025/Mon, 16 Feb 2026 00:00:00 +0000https://ojwatson.co.uk/2026/02/16/ai-epidemic-modelling-nature-2025/<p>I recently contributed to <strong>Nature (2025)</strong> on artificial intelligence for infectious disease epidemic modelling. The paper reviews how modern AI methods can support epidemic forecasting, surveillance, intervention planning, and model acceleration while still needing careful public-health interpretation.</p> <ul> <li>Paper: <a href="https://doi.org/10.1038/s41586-024-08564-w">https://doi.org/10.1038/s41586-024-08564-w</a></li> <li>Related tool: <a href="https://ojwatson.co.uk/project/emidm/">emidm</a></li> <li>Related research section: <a href="https://ojwatson.co.uk/research/#ai-enabled-epidemic-modelling">AI-enabled epidemic modelling</a></li> </ul> <p>Alongside this, my group continues work on emulator-based approaches that improve calibration speed and scenario exploration for policy-facing analyses. The aim is practical: make complex infectious disease models easier to interrogate under uncertainty, without losing sight of the assumptions that make those models useful.</p>