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.

Creswell, R. et al. (2023) “Understanding the impact of numerical solvers on inference for differential equation models”, arXiv.

Penn, M. and Donnelly, C. (2023) “Optimality of maximal-effort vaccination”, Bulletin of Mathematical Biology, 85.

Penn, M. et al. (2023) “Intrinsic randomness in epidemic modelling beyond statistical uncertainty”, Communications Physics, 6.

Gallagher, K. et al. (2023) “Identification and attribution of weekly periodic biases in epidemiological time series data.”

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

Pons-Salort, M. et al. (2023) “Changes in transmission of Enterovirus D68 (EV-D68) in England inferred from seroprevalence data”, eLife, 12, p. e76609.

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.