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SUMMARY:Bayesian inference for compartmented epidemiological models with i
 mperfect surveillance - Patrick Pietzonka\, DAMTP
DTSTART:20210119T130000Z
DTEND:20210119T140000Z
UID:TALK156049@talks.cam.ac.uk
CONTACT:Patrick Pietzonka
DESCRIPTION:Zoom link: https://maths-cam-ac-uk.zoom.us/j/94018037756\n\nI 
 will present a scheme through which stochastic compartmented models for th
 e spread of an epidemic can be specified and calibrated in a fully Bayesia
 n fashion using surveillance data. In order to account for imperfections i
 n the reported data\, we integrate in our models the process by which indi
 viduals get tested\, which is informed by available data on the number of 
 tests performed. For the likelihood estimation\, we take into account all 
 sources of stochasticity in both the infection and the testing process\, a
 s well as resulting correlations in the data. I will illustrate this schem
 e using the latest data for the covid19 pandemic in France\, Germany\, and
  the UK\, allowing us to discuss the effect of non-pharmaceutical interven
 tions. As an outlook\, I will show how vaccinations can be modeled as a fi
 nite resource similar to testing. \n
LOCATION:via zoom\, meeting ID 940-1803-7756
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