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BEGIN:VEVENT
DTSTAMP:20260427T184217Z
DTSTART;VALUE=DATE-TIME:20230228T170000
DTEND;VALUE=DATE-TIME:20230228T180000
SUMMARY:YRM W8 - Alicia Gill on Bayesian Inference of the Reproduction Nu
 mber from Epidemic and Genomic Data
TZID:Europe/London
UID:20230228-8a1785d7857d17ea018582ef4c797e12@warwick.ac.uk
CREATED:20230227T072202Z
DESCRIPTION:Abstract: The reproduction number R(t) represents the average
  number of new infections caused by a single infected individual at time
  t. Estimation of the reproduction number R(t) is of vital importance du
 ring an epidemic outbreak\, for example\, to decide whether to implement
  control measures and to determine their effects once implemented. Typic
 ally\, the reproduction number R(t) is inferred using only epidemic data
 \, such as prevalence per day. However\, prevalence data is often noisy\
 , partially observed and biased. Genomic data is therefore increasingly 
 being used to understand infectious disease epidemiology. We take a Baye
 sian approach to this problem to find the trajectory of R(t) given a dat
 ed phylogeny and partial prevalence data using particle Markov chain Mon
 te Carlo methods. We have implemented a particle marginal Metropolis--Ha
 stings algorithm with backward simulation to jointly infer the hyper-par
 ameters of the model\, the latent epidemic and the trajectory of R(t). T
 he performance of this approach is analysed using simulated data. These 
 simulations show that incorporating genomic data as well as epidemic dat
 a improves inference in a variety of cases.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20230227T072202Z
ORGANIZER;CN=Claudia Viaro:
END:VEVENT
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