Oxford university logo and department of statistics logo

To Department of Statistics Site

 Menu

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.

Kamau, E. et al. (2025) “The Mathematics of Serocatalytic Models with applications to public health data”, medRxiv.
Ter-Minassian, L. et al. (2025) “Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation”, in Proceedings of Machine Learning Research, pp. 1900–1908.
Mills, C. and Donnelly, C. (2024) “Climate-based modelling and forecasting of dengue fever in three endemic departments of Peru”, PLoS Neglected Tropical Diseases, 18(12).
Doohan, P. et al. (2024) “Lassa fever outbreaks, mathematical models, and disease parameters: a systematic review and meta-analysis”, The Lancet Global Health, 12(12), pp. e1962 - e1972.
Kamau, E. et al. (2024) “Enterovirus A71 and coxsackievirus A6 circulation in England, UK, 2006–2017: A mathematical modelling study using cross-sectional seroprevalence data”, PLoS Pathogens, 20(11).
Rivera, L. et al. (2024) “Characteristics of Madariaga and Venezuelan equine encephalitis virus infections, Panama”, Emerging Infectious Diseases, 30(14S), pp. S94 - S104.
Bajaj, S. et al. (2024) “COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys”, The Lancet Digital Health, 6(11), pp. e778 - e790.
Bajaj, S. et al. (2024) “COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys”, The Lancet Digital Health, 6(11), pp. e778 - e790.
Bajaj, S. et al. (2024) “COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys”, The Lancet Digital Health, 6(11), pp. e778 - e790.
Liley, J. et al. (2024) “Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland”, npj Digital Medicine, 7(1), p. 302.