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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.

Mullins, E. et al. (2022) “Tracking the incidence and risk factors for SARS-CoV-2 infection using historical maternal booking serum samples”, PLoS ONE, 17(9).
Parag, K., Donnelly, C. and Zarebski, A. (2022) “Quantifying the information in noisy epidemic curves”, Nature Computational Science, 2(9), pp. 584–594.
Ezanno, P. et al. (2022) “The African swine fever modelling challenge: Model comparison and lessons learnt”, Epidemics, 40, p. 100615.
Eales, O. et al. (2022) “Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number”, Epidemics, 40, p. 100604.
Lambert, B. et al. (2022) “Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals”, PLOS ONE, 17(8), p. e0272442.
Creswell, R. et al. (2022) “Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233).
Dankwa, E. et al. (2022) “Stochastic modelling of African swine fever in wild boar and domestic pigs: epidemic forecasting and comparison of disease management strategies”, Epidemics, 40.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), p. e3001723.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), p. e3001723.
Elliott, P. et al. (2022) “Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England”, Nature Communications, 13(1), p. 4500.