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SUMMARY:Composable probabilistic models can lower barriers to rigorous inf
 ectious disease modelling - Sam Abbott
DTSTART;TZID=Europe/Berlin:20260814T164500
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DTSTAMP:20260804T031039Z
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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