2026-08-14 –, Muschel — N3 All times in Europe/Berlin
Recent outbreaks of Ebola, COVID-19 and mpox, alongside routine surveillance of endemic pathogens, have demonstrated the value of modelling for synthesising data to inform decision making. For modelling evidence to effectively inform policy it must be timely, rigorous, and collaborative, yet current approaches struggle to be all three. Methods broadly fall into approaches that chain separate models together, offering flexibility but losing information and introducing bias, or approaches that rigorously analyse all data together but cannot be separated into reusable parts. Composable models, where components can be reused across contexts, can be both rigorous and flexible, enabling rapid collaborative model development. We outline design considerations for a composable infectious disease modelling framework and present a proof of concept domain-specific language built on the Turing.jl probabilistic programming language in Julia with an R interface. We demonstrate our approach conceptually using models from published epidemiological analyses, and in practice through a worked autoregressive example. We replicate three published analyses, composing elements of our autoregressive example with shared and novel components: a COVID-19 analysis for South Korea using a renewal process, adding components for reporting delays and day-of-week effects to replicate EpiNow2 for real-time nowcasting, and an ordinary differential equation analysis of influenza outbreak data. We then discuss strengths, limitations, and alternative approaches. We find that our proof of concept can address the tension between rigour and flexibility, though work remains to realise this potential. Our approach enables interdisciplinary collaboration by lowering technical barriers for domain experts to contribute specialised components, supporting both routine surveillance and outbreak response. For multi-model efforts, common components enable attribution of differences to assumptions rather than implementation. Our approach is also well suited for large language model assisted model construction. This study demonstrates that a composable modelling approach has the potential to incorporate diverse modelling approaches and domain knowledge across different infectious disease contexts.
I am an Assistant Professor at the London School of Hygiene & Tropical Medicine. I did my PhD in the optimal usage of the BCG vaccine, transitioning to work on real-time modelling of infectious disease outbreaks on the 3rd of January 2020. Four days later, I switched to work on what was then known as 2019-NCoV. I did early work on the size and scale of the initial outbreak, tracking transmissions in different countries, and exploring the potential role of different interventions. Throughout the pandemic, I ran a dashboard that was used by over a million people. I also provided estimates, forecasts, and analyses weekly to the UK government advisory bodies. I developed the tools and methods we used into open source software and these were used by upwards of 30 public health agencies around the world. I have continued to work in this area with a focus on improving tools and methods used both in research and in public health practice. I have recently transitioned to Julia for my work and am exploring how to propogate Julia based tools to the users of our current tooling and to the wider infectious disease modelling community.