Publications (Statistical Genetics and Epidemiology) | Department of Statistics

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.

Chen, S. et al. (2025) “Mass testing for discovery and control of COVID-19 outbreaks in adult social care: an observational study and cost-effectiveness analysis of 14 805 care homes in England”, BMJ Public Health, 3(1).

Steyn, N. et al. (2025) “A primer on inference and prediction with epidemic renewal models and sequential Monte Carlo”, arXiv.

Bajaj, S., Thompson, R. and Lambert, B. (2025) “A renewal-equation approach to estimating R t and infectious disease case counts in the presence of reporting delays”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383(2292).

Ghosh, M. et al. (2025) “Assessing the performance of compartmental and renewal models for learning $R_{t}$ using spatially heterogeneous epidemic simulations on real geographies”, arXiv.

Steyn, N. et al. (2025) “Pandemic-risk-related behaviour change in England from June 2020 to March 2022: REACT-1 study among over 2 million people”, medRxiv.

Awuah, A.-A. et al. (2025) “Seroprevalence of dengue in the Democratic Republic of Congo, Ghana and Senegal: results of the SERODEN study”, International Journal of Infectious Diseases, 152, pp. 107428 – 107428.

Vicco, A. et al. (2025) “Innovative methodological approach to assess dengue transmission: findings from the SERODEN study in Africa”, International Journal of Infectious Diseases, 152, pp. 107423 – 107423.

Yiu, A. et al. (2025) “Semiparametric posterior corrections”, Journal of the Royal Statistical Society, Series B (Statistical Methodology), 87(4), pp. 1025–1054.

Lazaridis, I. et al. (2025) “Author Correction: The genetic origin of the Indo-Europeans”, Nature, pp. 1–1.

Gallagher, K. et al. (2025) “Identification and attribution of weekly periodic biases in global epidemiological time series data”, BMC Research Notes, 18(1).