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.

Parag, K., Thompson, R. and Donnelly, C. (2022) “Authors’ reply to the discussion of ‘Are epidemic growth rates more informative than reproduction numbers?’ by Parag et al. in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 Transmission: 9 June 2021”, Journal of the Royal Statistical Society: Statistics in Society Series A, 185(S1), pp. S55 - S60.

Creswell, R. et al. (2022) “A Bayesian nonparametric method for detecting rapid changes in disease transmission”, Journal of Theoretical Biology, 558.

Whitaker, M. et al. (2022) “Variant-specific symptoms of COVID-19 in a study of 1,542,510 adults in England”, Nature Communications, 13.

Dankwa, E., Brouwer, A. and Donnelly, C. (2022) “Structural identifiability of compartmental models for infectious disease transmission is influenced by data type”, Epidemics, 41.

Unwin, H. et al. (2022) “Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak”, Epidemics, 41.

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.