George Streftaris

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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)
A standard approach to the fitting of stochastic mortality models is to maximise a likelihood function underpinned by an assumption that deaths follow a conditionally independent Pois-son distribution. This, in turn, has led researchers to develop increasingly complex models in an effort to improve in-sample explanatory power. This paper, using the(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)
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