George Streftaris

Learn More
Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations(More)
We propose an efficient and accurate approximate Bayesian Markov chain Monte Carlo methodology for the estimation of event rates under an overdispersed Poisson distribution. A Gibbs sampling algorithm is derived, based on a log-normal/gamma mixture density that closely approximates the conditional distribution of the Pois-son parameters. This involves a(More)
  • 1