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.

Ward, H. et al. (2023) “Design and implementation of a national program to monitor the prevalence of SARS-CoV-2 IgG antibodies in England using self-testing: the REACT-2 study”, American Journal of Public Health, 113(11), pp. 1201–1209.

Saada, J. et al. (2023) “Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks”, Molecular Biology and Evolution, 40(10), p. msad211.

Johnson, R. et al. (2023) “Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics”, p. 2023.09.28.559904.

Parag, K., Cowling, B. and Lambert, B. (2023) “Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying”, Proceedings of the Royal Society B, 290(2007), p. 20231664.

Hunter, D. and Holmes, C. (2023) “Where medical statistics meets artificial intelligence”, New England Journal of Medicine, 389(13), pp. 1211–1219.

Yang, J. et al. (2023) “Determining herd immunity thresholds for hepatitis A virus transmission to inform vaccination strategies among people who inject drugs in 16 US states”, Clinical Infectious Diseases, 78(4), pp. 976–982.

Wharrie, S. et al. (2023) “HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes”, Bioinformatics, 39(9), p. btad535.

Carrera, J.-P. et al. (2023) “Madariaga and Venezuelan equine encephalitis virus seroprevalence in rodent enzootic hosts in Eastern and Western Panama.”, bioRxiv [Preprint].

Murphy, C. et al. (2023) “Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2257).

Whitehead, M. et al. (2023) “Making the invisible visible: what can we do about biased AI in medical devices?”, The BMJ, 382, p. p1893.