Composable probabilistic models can lower barriers to rigorous infectious disease modelling

Abstract

Recent outbreaks of Ebola, COVID-19 and mpox, and routine surveillance of endemic pathogens such as influenza, have demonstrated the value of modelling for synthesising data to inform decision making. Effective models require integration of expert domain knowledge from multiple domains and outputs to be timely enough to inform policy yet current modelling approaches create barriers to meeting these goals. Methods used to synthesise available data broadly fall into approaches that chain separate models together, offering flexibility but losing information and potentially introducing bias, or rigorous joint models that are often monolithic and difficult to adapt. These barriers have prevented advances across multiple settings where models could have provided actionable insights. Composable models where components can be reused across different contexts and combined in various configurations whilst maintaining statistical rigour could address these limitations. We outline proposed requirements for a composable infectious disease modelling framework and present a proof of concept that addresses these requirements through composable epidemiological components built on Julia's type system and Turing.jl. We demonstrate a prototype R interface showing how such frameworks can bridge software ecosystems. Three case studies show how latent process components can be composed with epidemiological models to estimate time-varying reproduction numbers. The first replicates a COVID-19 analysis for South Korea using a renewal process. The second extends these components with reporting delays and day-of-week effects to replicate EpiNow2, a real-time nowcasting tool. The third replicates an ordinary differential equation model analysis of influenza outbreak data. We then discuss strengths, limitations, and alternative approaches. Our approach demonstrates promise for enabling interdisciplinary collaboration by lowering technical barriers for domain experts to contribute directly to model development. Future work is needed to solve remaining composability challenges, explore other options, expand the component library, and explore opportunities for large language model assisted model construction.

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📑 Supplementary Information: Case Studies (HTML)

📦 EpiAware.jl Documentation | 💻 Code

📦 EpiAwareR Documentation | 💻 Code

Citation

Paper citation information will be added upon publication.

If reusing the code, please cite using DOI: 10.5281/zenodo.17884675. See CITATION.cff for full citation information.

Running the analysis

Prerequisites

1. Quarto: Follow the instructions at quarto.org to install Quarto.

2. Julia: Follow the instructions at juliaup to install Julia using the official Julia version manager.

3. R (optional, for EpiAwareR): Install R (version 4.0.0 or higher) if you want to run the EpiAwareR vignettes.

4. Task (optional): Install Task for automated workflow management.

Using Task (Recommended)

task

This automatically handles git submodules, Julia environment setup, Quarto extension installation, and document rendering.

Available tasks:

• task - Render all documents (default)
• task render-case-studies - Render case studies (generates figures)
• task render-index - Render main document
• task preview - Preview document with live reload
• task repl - Launch Julia REPL with project environment
• task install-extensions - Install Quarto extensions
• task renv-restore - Restore R environment from renv.lock
• task setup-epiawarer - Set up EpiAwareR with Julia backend
• task --list - Show all available tasks

Manual Execution

1. Install Julia 1.11.6 (we expect any version of 1.11 to work well):

   juliaup add 1.11.6
   juliaup override set 1.11.6

2. Initialise git submodules:

   git submodule update --init --recursive

3. Set up Julia environment:

   julia +1.11.6

   Then in the Julia REPL:

   using Pkg
   Pkg.activate(".")
   Pkg.instantiate()

4. Render the document:

   quarto render

Running interactively

Case Studies (Julia)

The case studies in case-studies.qmd can be run interactively using Quarto's preview mode:

1. Set up the environment:

   task instantiate

2. Preview with live reload:

   task preview

   Or open case-studies.qmd directly in VS Code or another editor with Quarto support.

EpiAwareR vignettes (R)

The EpiAwareR/ submodule contains R vignettes demonstrating the R interface to EpiAware:

• EpiAwareR.Rmd - Getting started guide
• mishra-case-study.Rmd - Replicating Mishra et al. (2020) COVID-19 analysis
• epinow2-comparison.Rmd - Comparison with EpiNow2 workflows

To run the vignettes interactively:

1. Set up EpiAwareR with Julia backend:

   task setup-epiawarer

2. Open the vignettes in RStudio or VS Code:

   EpiAwareR/vignettes/EpiAwareR.Rmd
   EpiAwareR/vignettes/mishra-case-study.Rmd
   EpiAwareR/vignettes/epinow2-comparison.Rmd
   

3. Run code chunks interactively using your IDE's execution features.