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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.

Hayes, S. et al. (2022) “Understanding the incidence and timing of rabies cases in domestic animals and wildlife in south-east Tanzania in the presence of widespread domestic dog vaccination campaigns”, Veterinary Research, 53(1).
Gallagher, C. et al. (2022) “Epidemiological agent-based modelling software (Epiabm).”
Eales, O. et al. (2022) “SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2”, BMC Infectious Diseases, 22(1), p. 647.
Charniga, K. et al. (2022) “Estimating Zika virus attack rates and risk of Zika virus-associated neurological complications in Colombian capital cities with a Bayesian model”, Royal Society Open Science, 9(11).
Eales, O. et al. (2022) “Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021”, PLoS Computational Biology, 18(11).
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.