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.

Team, T. et al. (2018) “Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study”, The Lancet, 392(10143), pp. 213–221.

Jewson, J., Smith, J. and Holmes, C. (2018) “Principles of Bayesian inference using general divergence criteria”, Entropy, 20(6), p. 442.

Holmes, C. and Watson, J. (2018) “Machine learning for randomised controlled trials: identifying treatment effectheterogeneity with strict control of type I error”, p. 330795.

Calder, M. et al. (2018) “Computational modelling for decision-making: where, why, what, who and how”, Royal Society Open Science, 5(6), p. 172096.

Brown, S. et al. (2018) “High-throughput mouse phenomics for characterizing mammalian gene function”, Nature Reviews Genetics, 19(6), pp. 357–370.

Donnelly, C. et al. (2018) “Four principles to make evidence synthesis more useful for policy”, Nature, 558(7710), pp. 361–364.

Radhakrishnan, S. et al. (2018) “A question of accessibility: modelling the effects of demography and accessibility for vaccination on elimination of rabies in free-ranging dog populations in India..”

Willetts, M. et al. (2018) “Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants”, Scientific Reports, 8.

Glaire, M. et al. (2018) “Tumour-infiltrating CD8 + lymphocytes as a prognostic marker in colorectal cancer: A retrospective, pooled analysis of the QUASAR2 and VICTOR trials.”, Journal of Clinical Oncology. American Society of Clinical Oncology (ASCO).

Anders, K. et al. (2018) “Cluster-randomized test-negative design trials: A novel and efficient method to assess the efficacy of community-level Dengue interventions”, American Journal of Epidemiology, 187(9), pp. 2021–2028.