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BEGIN:VEVENT
DTSTAMP:20260415T190511Z
DTSTART;VALUE=DATE-TIME:20230613T170000
DTEND;VALUE=DATE-TIME:20230613T180000
SUMMARY:YRM Week 8 - Hannah Bensoussane on Bayesian individual-level infe
 ctious disease modelling
TZID:Europe/London
UID:20230613-8a1785d78895e6400188b401c9345227@warwick.ac.uk
CREATED:20230613T090635Z
DESCRIPTION:Title: Bayesian individual-level infectious disease modelling
 : inhomogeneous mixing and an automated approach for updating infection 
 times Abstract: Fitting mathematical models to epidemic data is challeng
 ing because the transmission process is largely unobserved. Add into the
  mix that the characteristics of an individual can affect their ability 
 to catch and transmit infectious diseases and resulting models can be bo
 th complex and computationally costly. Here we develop a model that allo
 ws us to make inference about underlying parameters that drive epidemics
  whilst accounting for individual-level covariates (e.g. age) and shared
  covariates – living in the same house\, for example. The use of an adap
 tive MCMC algorithm featuring novel approaches to updating unknown infec
 tion/infectious times allows us to estimate model parameters and identif
 y characteristics that affect transmission in a fashion that is computat
 ionally viable. Model performance is assessed through a variety of simul
 ation work
LOCATION:
URL:
ATTACH:
CATEGORIES:
LAST-MODIFIED:20230613T090635Z
ORGANIZER;CN=Claudia Viaro:
END:VEVENT
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