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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:20260804T022659Z
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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SUMMARY:Estimating epidemiological delay distributions: from R/Stan to Jul
 ia - Sam Abbott
DTSTART;TZID=Europe/Berlin:20260814T144500
DTEND;TZID=Europe/Berlin:20260814T150000
DTSTAMP:20260804T022659Z
UID:pretalx-juliacon-2026-ZUMSFD@pretalx.com
DESCRIPTION:Delay distributions describe the time between epidemiological 
 events\, such as infection to symptom onset or symptom onset to hospitalis
 ation.\nEstimating these distributions from outbreak data is difficult bec
 ause both the primary event (e.g. infection) and the secondary event (e.g.
  symptom onset) are usually only known to have occurred within a time wind
 ow\, such as a day.\nReal-time outbreak data is also often right-truncated
  as longer delays have not yet been observed.\nIgnoring double interval ce
 nsoring and truncation biases parameter estimates which are then used for 
 forecasting and transmission modelling.\n\nAdjusting distributions for pri
 mary event censoring addresses this by integrating the delay CDF over the 
 primary event window\, weighted by the density of when\, within the window
 \, the event occurred.\nThis can then be combined with truncation and seco
 ndary interval-censoring adjustments to produce a double-interval-censored
  and right-truncation-adjusted distribution.\n\nIn this talk\, we present 
 [CensoredDistributions.jl](https://censoreddistributions.epiaware.org)\, w
 hich implements these adjustments as `primary_censored`\, `interval_censor
 ed`\, and `double_interval_censored`\, composable [Distributions.jl](https
 ://github.com/JuliaStats/Distributions.jl) wrappers.\nMultiple dispatch se
 lects closed-form CDFs for delay and primary event distribution pairs wher
 e these are available\, and falls back to numerical integration otherwise.
 \nWe demo the package standalone and with [Turing.jl](https://turinglang.o
 rg/) for parameter estimation.\n\nWe then compare to [primarycensored](htt
 ps://primarycensored.epinowcast.org)\, our equivalent R package\, which al
 so ships a duplicate set of [Stan](https://mc-stan.org/) functions so user
 s can fit models in either language.\nMaintaining two parallel implementat
 ions required reimplementing distribution functions in Stan\, building too
 ling to vendor Stan code into downstream projects\, and replacing types wi
 th integer distribution identifiers.\nStan's integral solver was also unst
 able for this problem\, so we had to recast it as an ODE.\nJulia's multipl
 e dispatch and ecosystem composability eliminates all of this.\n\nWe then 
 summarise our plans to build a composed Julia version of our [epidist](htt
 ps://epidist.epinowcast.org) R package\, using CensoredDistributions.jl as
  a foundation with Turing.jl submodels for partially pooled and flexible d
 elay estimation.
LOCATION:Muschel — N2
URL:https://pretalx.com/juliacon-2026/talk/ZUMSFD/
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SUMMARY:Composable probabilistic models can lower barriers to rigorous inf
 ectious disease modelling - Sam Abbott
DTSTART;TZID=Europe/Berlin:20260814T164500
DTEND;TZID=Europe/Berlin:20260814T170000
DTSTAMP:20260804T022659Z
UID:pretalx-juliacon-2026-AMFLYU@pretalx.com
DESCRIPTION:Recent outbreaks of Ebola\, COVID-19 and mpox\, alongside rout
 ine surveillance of endemic pathogens\, have demonstrated the value of mod
 elling for synthesising data to inform decision making. For modelling evid
 ence to effectively inform policy it must be timely\, rigorous\, and colla
 borative\, yet current approaches struggle to be all three. Methods broadl
 y fall into approaches that chain separate models together\, offering flex
 ibility but losing information and introducing bias\, or approaches that r
 igorously 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 d
 evelopment. We outline design considerations for a composable infectious d
 isease modelling framework and present a proof of concept domain-specific 
 language built on the Turing.jl probabilistic programming language in Juli
 a with an R interface. We demonstrate our approach conceptually using mode
 ls from published epidemiological analyses\, and in practice through a wor
 ked autoregressive example. We replicate three published analyses\, compos
 ing elements of our autoregressive example with shared and novel component
 s: a COVID-19 analysis for South Korea using a renewal process\, adding co
 mponents 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 t
 his potential. Our approach enables interdisciplinary collaboration by low
 ering technical barriers for domain experts to contribute specialised comp
 onents\, supporting both routine surveillance and outbreak response. For m
 ulti-model efforts\, common components enable attribution of differences t
 o assumptions rather than implementation. Our approach is also well suited
  for large language model assisted model construction. This study demonstr
 ates that a composable modelling approach has the potential to incorporate
  diverse modelling approaches and domain knowledge across different infect
 ious disease contexts.
LOCATION:Muschel — N3
URL:https://pretalx.com/juliacon-2026/talk/AMFLYU/
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