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Publications by Statistical Genetics and Epidemiology

Our research spans areas of statistical genetics, in particular the development of powerful statistical approaches to analyse genetic data, as well as studying infectious diseases.

Lambert, B. and Vehtari, A. (2022) “R*: A Robust MCMC Convergence Diagnostic with Uncertainty Using Decision Tree Classifiers”, Bayesian Analysis, 17(2), pp. 353–379.
Parag, K., Thompson, R. and Donnelly, C. (2022) “Are epidemic growth rates more informative than reproduction numbers?”, Journal of the Royal Statistical Society: Series A, 185(S1), pp. S5 - S15.
Penn, M. and Donnelly, C. (2022) “Analysis of a double Poisson model for predicting football results in Euro 2020”, PLoS One, 17(5).
van der Vegt, S. et al. (2022) “Learning transmission dynamics modelling of COVID-19 using comomodels”, Mathematical Biosciences, 349.
Madge, O. et al. (2022) “The COVID-19 pandemic is associated with reduced survival after pancreatic ductal adenocarcinoma diagnosis: A single-centre retrospective analysis”, Journal of Clinical Medicine, 11(9).
Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Falck, F. et al. (2022) “Multi-facet clustering variational autoencoders”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 8676–8690.
Falck, F. et al. (2022) “Multi-facet clustering variational autoencoders”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 8676–8690.
Taylor, L. et al. (2022) “Movement behaviour after birth demonstrates precocial abilities of African savannah elephant, Loxodonta africana, calves”, Animal Behaviour, 187, pp. 331–353.