Publications

Walker, P. et al. (2020) “The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries”, Science, 369(6502), pp. 413–422.

Jeffrey, B. et al. (2020) “Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK”, Wellcome Open Research, 5, p. 170.

Jiang, X., Holmes, C. and McVean, G. (2020) “The impact of age on genetic risk for common diseases”, p. 2020.07.17.208280.

Hawryluk, I. et al. (2020) “Inference of COVID-19 epidemiological distributions from Brazilian hospital data”, p. 2020.07.15.20154617.

Parag, K. et al. (2020) “An exact method for quantifying the reliability of end-of-epidemic declarations in real time”, p. 2020.07.13.20152082.

Zhou, Y. et al. (2020) “Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support”, in ICML 2020. ICML Proceedings.

Investigators, R. et al. (2020) “Community prevalence of SARS-CoV-2 virus in England during May 2020: REACT study”, p. 2020.07.10.20150524.

Miguel, E. et al. (2020) “A systemic approach to assess the potential and risks of wildlife culling for infectious disease control”, Communications Biology, 3.

Parag, K. and Donnelly, C. (2020) “Using information theory to optimise epidemic models for real-time prediction and estimation”, PLOS Computational Biology, 16(7), p. e1007990.

Richards, D., Rebeschini, P. and Rosasco, L. (2020) “Decentralised Learning with Random Features and Distributed Gradient Descent”, arXiv.