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SUMMARY:Building a composable Julia ecosystem for infectious disease model
 ling: a roadmap\, challenges\, and questions - Sam Abbott
DTSTART;TZID=Europe/Berlin:20260814T143000
DTEND;TZID=Europe/Berlin:20260814T144500
DTSTAMP:20260804T031039Z
UID:pretalx-juliacon-2026-NEWC8H@pretalx.com
DESCRIPTION:Infectious disease models that integrate multiple data sources
  provide better evidence for outbreak response than chains of separate mod
 els\, but building them is slow and requires expertise across domains.\nCo
 mposable modelling\, where validated components combine into joint models 
 that properly propagate uncertainty\, addresses this but requires an ecosy
 stem of reusable infectious disease model components.\nWe believe Julia is
  the best language for this ecosystem due to its type system\, multiple di
 spatch\, automatic differentiation support\, and existing scientific compu
 ting infrastructure ([SciML](https://sciml.ai)\, [Turing.jl](https://turin
 glang.org)\, [Distributions.jl](https://github.com/JuliaStats/Distribution
 s.jl))\, which provide the foundations composable modelling needs.\nIn thi
 s talk\, we present the [EpiAware](https://github.com/EpiAware) roadmap fo
 r creating and sustaining that ecosystem\, our current progress\, and our 
 questions for the Julia community.\n\nIn R\, we have built the [epinowcast
 ](https://github.com/epinowcast) ecosystem (packages\, community forum\, s
 eminar series) and developed several other widely used packages including 
 [EpiNow2](https://github.com/epiforecasts/EpiNow2) and [scoringutils](http
 s://github.com/epiforecasts/scoringutils).\nWe want to create something eq
 uivalent in Julia: a domain-focused ecosystem in the mould of [SciML](http
 s://sciml.ai) or [Turing.jl](https://turinglang.org)\, with the community 
 infrastructure of [rOpenSci](https://ropensci.org) and the domain specific
 ity of [SpeedyWeather.jl](https://github.com/SpeedyWeather/SpeedyWeather.j
 l).\n\nSo far\, we have [CensoredDistributions.jl](https://github.com/EpiA
 ware/CensoredDistributions.jl)\, which handles common biases in epidemiolo
 gical delay distributions\, and an R interface prototype ([EpiAwareR](http
 s://github.com/sbfnk/EpiAwareR)).\nWe initially plan to implement packages
  covering distribution extensions for epidemiological use\, delay and gene
 ration time estimation\, disease dynamics components\, and forecast evalua
 tion\, alongside a centralised documentation site.\n\nAt the package level
 \, we need to answer questions about what makes a good Julia package in ou
 r ecosystem: consistent documentation via [DocStringExtensions](https://gi
 thub.com/JuliaDocs/DocStringExtensions.jl) and [DocumenterCiterepress](htt
 ps://github.com/JuliaDocs/DocumenterCitations.jl)\, robust testing with [A
 qua.jl](https://github.com/JuliaTesting/Aqua.jl) and [JET.jl](https://gith
 ub.com/aviatesk/JET.jl)\, automatic differentiation backend testing via [D
 ifferentiationInterfaceTest](https://github.com/gdalle/DifferentiationInte
 rface.jl)\, and where we need package extensions (e.g. for [Turing.jl](htt
 ps://turinglang.org) integration).\n\nAt the ecosystem level\, we need to 
 understand how to manage releases so that package versions work together\,
  how to run reverse dependency checks before publishing\, how to set up sh
 ared CI and centralised documentation across many packages\, and how to he
 lp users understand which automatic differentiation backends are compatibl
 e when they combine multiple packages.
LOCATION:Muschel — N2
URL:https://pretalx.com/juliacon-2026/talk/NEWC8H/
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