Code and data for: Composable probabilistic models can lower barriers to rigorous infectious disease modelling

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.