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.

Camuto, A. et al. (2021) “Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections”, in Proceedings of Machine Learning Research, pp. 1249–1260.

Payne, A. et al. (2021) “Burns Management at A UK Major Trauma Centre During The COVID-19 Lockdown”, in BRITISH JOURNAL OF SURGERY.

Thompson, H. et al. (2020) “SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China”, Journal of Travel Medicine, 27(8), p. taaa135.

Riley, S. et al. (2020) “REACT-1 round 7 updated report: regional heterogeneity in changes in prevalence of SARS-CoV-2 infection during the second national COVID-19 lockdown in England”, p. 2020.12.15.20248244.

Willetts, M., Roberts, S. and Holmes, C. (2020) “Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 5286–5295.

Unwin, H. et al. (2020) “State-level tracking of COVID-19 in the United States”, Nature Communications, 11(1), p. 6189.

Riley, S. et al. (2020) “REACT-1 round 7 interim report: fall in prevalence of swab-positivity in England during national lockdown”, p. 2020.11.30.20239806.

Grassly, N. et al. (2020) “Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study”, The Lancet Infectious Diseases, 20(12), pp. 1381–1389.

Watson, J. and Holmes, C. (2020) “Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error”, Trials, 21(1), p. 156.

Watson, J. and Holmes, C. (2020) “Correction to: Graphing and reporting heterogeneous treatment effects through reference classes”, Trials, 21(1), p. 865.