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Publications

Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Nicholson, G. et al. (2022) “Interoperability of statistical models in pandemic preparedness: principles and reality.”, Statistical Science, 37(2), pp. 183–206.
Falck, F. et al. (2022) “Multi-facet clustering variational autoencoders”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 8676–8690.
Falck, F. et al. (2022) “Multi-facet clustering variational autoencoders”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 8676–8690.
Willetts, M. et al. (2022) “Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.”, Scientific reports, 12(1), p. 6024.
Parag, K. and Donnelly, C. (2022) “Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers”, PLoS Computational Biology, 18(4).
Chadeau-Hyam, M. et al. (2022) “SARS-CoV-2 infection and vaccine effectiveness in England (REACT-1): a series of cross-sectional random community surveys”, The Lancet Respiratory Medicine, 10(4), pp. 355–366.
Oravkin, E. and Rebeschini, P. (2022) “On optimal interpolation in linear regression”, in. Neural Information Processing Systems Foundation, pp. 29116–29128.
Unsworth, H. et al. (2022) “Building an evidence standards framework for artificial intelligence-enabled digital health technologies”, The Lancet Digital Health, 4(4), pp. e216 - e217.
Unwin, H. et al. (2022) “Global, regional, and national minimum estimates of children affected by COVID-19-associated orphanhood and caregiver death, by age and family circumstance up to Oct 31, 2021: an updated modelling study.”, The Lancet. Child & adolescent health, 6(4), pp. 249–259.