Publications (Statistical Genetics and Epidemiology) | Department of Statistics

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.

Fu, H. et al. (2021) “Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China”, International Journal of Infectious Diseases, 102, pp. 463–471.

Wilde, H. et al. (2021) “Foundations of Bayesian Learning from Synthetic Data”, in Proceedings of Machine Learning Research, pp. 541–549.

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

Zaidi, S. et al. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.

Lees, R. et al. (2021) “REVIEWING THE EVIDENCE FOR AND AGAINST SELECTION OF SPECIFIC PYRETHROIDS FOR PROGRAMMATIC PURPOSES”, in AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE, pp. 432–432.

Nash, R. et al. (2021) “Systematic review of the entomological impact of insecticide-treated nets evaluated using experimental hut trials in Africa”, Current Research in Parasitology and Vector-Borne Diseases, 1.

Willetts, M. et al. (2021) “IMPROVING VAES’ ROBUSTNESS TO ADVERSARIAL ATTACK”, in ICLR 2021 - 9th International Conference on Learning Representations.

Ghalebikesabi, S. et al. (2021) “Deep Generative Missingness Pattern-Set Mixture Models”, in Proceedings of Machine Learning Research, pp. 3727–3735.

Cooper, B. et al. (2021) “The burden and dynamics of hospital-acquired SARS-CoV-2 in England”, Research Square.

Verity, R. et al. (2021) “COVID-19 and the difficulty of inferring epidemiological parameters from clinical data – Authors’ reply”, The Lancet Infectious Diseases, 21(1), p. 28.