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.

McCrone, J. et al. (2022) “Context-specific emergence and growth of the SARS-CoV-2 Delta variant”, Nature, 610(7930), pp. 154–160.

Lambert, B. et al. (2022) “Autocorrelated measurement processes and inference for ordinary differential equation models of biological systems”, arXiv.

Chadeau-Hyam, M. et al. (2022) “Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys.”, The Lancet regional health. Europe, 21, p. 100462.

Menkir, T. and Donnelly, C. (2022) “The impact of repeated rapid test strategies on the effectiveness of at-home antiviral treatments for SARS-CoV-2”, Nature Communications, 13(1), p. 5283.

Martin, N. et al. (2022) “A graph based neural network approach to immune profiling of multiplexed tissue samples”, pp. 3063–3067.

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).

Eales, O. et al. (2022) “Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number”, Epidemics, 40, p. 100604.

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.

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.