Composable probabilistic models can lower barriers to rigorous infectious disease modelling

The case for composition, and what is still missing

Why composition might be how modelling evidence gets to be timely, rigorous and collaborative at once, and what is still missing.

JuliaCon 2026 · Friday 14 August 2026, 16:45–17:00 (Europe/Berlin) · Muschel — N3 · Sam Abbott

Slides ComposableTuringIDModels.jl Approaches and requirements Session on pretalx

Abstract

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.

Resources

Papers and writing

  • What we want from an approach. The twelve design considerations this work is built to meet, and the two approaches we are taking.
  • The paper this talk is drawn from, Composable probabilistic models can lower barriers to rigorous infectious disease modelling, is in CDC clearance. There is nothing to link to yet, and the slides cite its figures by number.
  • A workflow for infectious disease modelling. Related work on how these models get built and checked. It is the subject of the roadmap talk, not this one.

Code

The R equivalent

  • EpiAwareR. Sebastian Funk’s prototype R interface to the Julia ecosystem.