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BEGIN:VEVENT
DTSTAMP:20260614T011407Z
DTSTART;VALUE=DATE-TIME:20170317T140000
DTEND;VALUE=DATE-TIME:20170317T150000
SUMMARY:CRiSM seminar: Paul Birrell (MRC Biostatistics Unit\, Cambridge) 
 "Towards Computationally Efficient Epidemic Inference"
TZID:Europe/London
UID:20170317-8a17841a5ab7af35015ad66884d369f5@warwick.ac.uk
CREATED:20170316T091738Z
DESCRIPTION:In a pandemic where infection is widespread\, there is no dir
 ect observation of the infection processes. Instead information comes fr
 om a variety of surveillance data schemes that are prone to noise\, cont
 amination\, bias and sparse sampling. To form an accurate impression of 
 the epidemic and to be able to make forecasts of its evolution\, therefo
 re\, as many of these data streams as possible need to be assimilated in
 to a single integrated analysis. The result of this is that the transmis
 sion model describing the infection process and the linked observation m
 odels can become computationally demanding\, limiting the capacity for s
 tatistical inference in real-time. I will discuss some of our attempts a
 t making the inferential process more efficient\, with particular focus 
 on dynamic emulation\, where the computationally expensive epidemic mode
 l is replaced by a more readily evaluated proxy\, a time-evolving Gaussi
 an process trained on a (relatively) small number of model runs at key i
 nput values\, training that can be done a priori. Wine and cheese recept
 ion afterwards
LOCATION:B1.01\, Zeeman Building
CATEGORIES:Seminars,Social events
LAST-MODIFIED:20170316T123121Z
ORGANIZER;CN="":
END:VEVENT
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