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SUMMARY:Towards Probabilistic Domain-Specific Languages for Infectious Dis
 ease Modelling - Simon  Fowler (University of Glasgow)
DTSTART:20240820T113000Z
DTEND:20240820T120000Z
UID:TALK219919@talks.cam.ac.uk
DESCRIPTION:Modelling epidemics is challenging: realistic models require p
 ractitioners to be well-versed in epidemiology\,&nbsp\;and&nbsp\;computati
 onal statistics\,&nbsp\;and&nbsp\;computer science. The result is that mod
 elling is in the hands of the few\; that models are slow to code and their
  results opaque\; and that there are substantial skills barriers in traini
 ng newcomers to epidemiology.\nIn this talk\, I will give an overview of h
 ow programming languages research can help with these challenges.&nbsp\;Do
 main-specific languages&nbsp\;are programming languages targeted at a part
 icular problem domain\, and allow developers to write succinct and targete
 d code that can be compiled in a performant way. There have been several p
 romising pilot DSLs targeting infectious disease modelling\, particularly 
 those based on reactive programming. We hypothesise that main missing piec
 e of the jigsaw is support for probabilistic programming functionality\, e
 specially support for model parameter inference and the ability to deal wi
 th censored data. I will describe early thoughts on a proposed system\,&nb
 sp\;Amethyst\, that aims to serve as a new domain-specific language for Ba
 yesian infectious disease models\, using a declarative and compositional a
 pproach to support rapid and reliable model development .\n&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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