Chris Pooley

I am primarily interested in developing statistically sound approaches to parameter inference in dynamic and spatio-temporal stochastic models. I joined EPIC in 2021 but have spent much of the time since developing a nationwide inference algorithm to analyse the COVID-19 epidemic (github.com/ScottishCovidResponse/BEEPmbp). As COVID ends, I hope to transfer what I have learned into developing robust tools useful to EPIC to help predict the behaviour of animal diseases and inform better disease control measures. My previous theoretical work has largely been focused on population-level and individual-level dynamic process-based models using and developing a variety of techniques: data augmentation MCMC, particle methods, model and posterior-based proposals, multi-temperature MCMC, novel ABC and reliable approaches to model selection. These have allowed me to analyse disease transmission data from aquaculture, livestock experiments and wildlife populations as well as phylodynamic analyses of the pathogens themselves. In addition, I have a keen interest in developing software tools to allow these sophisticated inference techniques to be readily available to the wider scientific audience (e.g. see theiteam.github.io).


Exact Bayesian inference of epidemiological parameters from mortality data: application to African swine fever virus. David A. Ewing, Christopher M. Pooley, Kokouvi M. Gamado, Thibaud Porphyre and Glenn Marion

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