Code and data for: The case for composable probabilistic infectious disease models

Effective models require integration of expert domain knowledge from multiple domains such as clinical medicine, environmental science, behavioural research, and public health, yet current modelling approaches create barriers to this integration. Methods used to synthesise available data broadly fall into pipeline approaches that chain separate models together, losing information and potentially introducing bias, or 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. In this work, we start by outlining proposed requirements for a composable infectious disease modelling framework and then present a proof of concept that addresses these requirements through composable epidemiological components built on Julia's type system and Turing.jl.