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Publications

Tirumala, D. et al. (2022) “Behavior Priors for Efficient Reinforcement Learning”, Journal of Machine Learning Research, 23.
Ezanno, P. et al. (2022) “The African swine fever modelling challenge: Model comparison and lessons learnt”, Epidemics, 40, pp. 100615 – 100615.
Parag, K., Donnelly, C. and Zarebski, A. (2022) “Quantifying the information in noisy epidemic curves”, Nature Computational Science, 2(9), pp. 584–594.
Eales, O. et al. (2022) “Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number”, Epidemics, 40, p. 100604.
Dankwa, E. et al. (2022) “Stochastic modelling of African swine fever in wild boar and domestic pigs: epidemic forecasting and comparison of disease management strategies”, Epidemics, 40.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), pp. e3001723 - e3001723-.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), pp. e3001723 - e3001723-.
Elliott, P. et al. (2022) “Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England”, Nature Communications, 13(1), pp. 4500–4500.
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.
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.