Publications (Statistical Theory and Methodology) | Department of Statistics - University of Oxford

Publications

Jersakova, R. et al. (2022) “Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag”, Journal of the Royal Statistical Society Series C (Applied Statistics), 71(4), pp. 834–860.

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

Eales, O. et al. (2022) “Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England”, Nature Communications, 13(1), pp. 4375–4375.

Longini, I. et al. (2022) “A platform trial design for preventive vaccines against Marburg virus and other emerging infectious disease threats”, Clinical Trials, 19(6), pp. 647–654.

Elliott, P. et al. (2022) “Twin peaks: The Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England”, Science, 376(6600), p. eabq4411.

Chadeau-Hyam, M. et al. (2022) “Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study”, EClinicalMedicine, 48, p. 101419.

Benedetto, U. et al. (2022) “Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis”, Journal of Thoracic and Cardiovascular Surgery, 163(Int Stat Rev 80 2012), pp. 2075 – 2087.e9.

Parag, K., Thompson, R. and Donnelly, C. (2022) “Are epidemic growth rates more informative than reproduction numbers?”, Journal of the Royal Statistical Society: Series A, 185(S1), pp. S5 - S15.

Penn, M. and Donnelly, C. (2022) “Analysis of a double Poisson model for predicting football results in Euro 2020”, PLoS One, 17(5).

Lee, H. et al. (2022) “Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility”, arXiv.