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.

McCabe, R. et al. (2023) “Alternative epidemic indicators for COVID-19 in three settings with incomplete death registration systems”, Science Advances, 9(23).

Sinha, S. et al. (2023) “Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database”, European Journal of Cardio-Thoracic Surgery, 63(6), p. ezad183.

Eales, O. et al. (2023) “Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England”, PLoS Biology, 21(5).

Augustin, D. et al. (2023) “Filter inference: a scalable nonlinear mixed effects inference approach for snapshot time series data”, PLoS Computational Biology, 19(5).

Holmes, C. and Walker, S. (2023) “Statistical inference with exchangeability and martingales”, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 381(2247), p. 20220143.

Zhang, B. et al. (2023) “Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits”, Nature Genetics, 55, pp. 768–776.

Dunger‐Baldauf, C. et al. (2023) “Generating the Right Evidence at the Right Time: Principles of a New Class of Flexible Augmented Clinical Trial Designs”, Clinical Pharmacology & Therapeutics, 113(5), pp. 1132–1138.

Donnelly, C. (2023) “Sir David Cox—A Life Well Lived”, Harvard data science review, 5(2).

Kim, Y., Donnelly, C. and Nouvellet, P. (2023) “Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England”, Nature Communications, 14(1).

Elliott, P. et al. (2023) “Design and implementation of a national SARS-CoV-2 monitoring program in England: REACT-1 Study”, American Journal of Public Health, 113(5), pp. 545–554.