| Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe S Flaxman, S Mishra, A Gandy, HJT Unwin, TA Mellan, H Coupland, ... Nature 584 (7820), 257-261, 2020 | 4403 | 2020 |
| Age groups that sustain resurging COVID-19 epidemics in the United States M Monod, A Blenkinsop, X Xi, D Hebert, S Bershan, S Tietze, M Baguelin, ... Science 371 (6536), eabe8372, 2021 | 388 | 2021 |
| Imperial College COVID-19 Response Team S Flaxman, S Mishra, A Gandy, HJT Unwin, TA Mellan, H Coupland, ... Estimating the effects of non-pharmaceutical interventions on COVID-19 in …, 2020 | 321 | 2020 |
| Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. 2020 S Flaxman, S Mishra, A Gandy, H Unwin, H Coupland, T Mellan, H Zhu, ... URL{https://www. imperial. ac. uk/media/imperial-college/medicine/sph/ide …, 2021 | 218 | 2021 |
| State-level tracking of COVID-19 in the United States HJT Unwin, S Mishra, VC Bradley, A Gandy, TA Mellan, H Coupland, ... Nature communications 11 (1), 6189, 2020 | 203 | 2020 |
| Report 21: Estimating COVID-19 cases and reproduction number in Brazil TA Mellan, HH Hoeltgebaum, S Mishra, C Whittaker, RP Schnekenberg, ... | 140 | 2020 |
| Imperial College COVID-19 Response Team, Ghani AC, Donnelly CA, Riley S, Vollmer MAC, Ferguson NM, Okell LC, Bhatt S (2020) Estimating the effects of non-pharmaceutical … S Flaxman, S Mishra, A Gandy, HJT Unwin, TA Mellan, H Coupland, ... Nature 584 (7820), 257-261, 0 | 81 | |
| Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling S Mishra, JA Scott, DJ Laydon, S Flaxman, A Gandy, TA Mellan, ... Scientific Reports 11 (1), 16342, 2021 | 71 | 2021 |
| Report 20: Using mobility to estimate the transmission intensity of COVID-19 in Italy: A subnational analysis with future scenarios MAC Vollmer, S Mishra, HJT Unwin, A Gandy, TA Mellan, V Bradley, ... MedRxiv, 2020.05. 05.20089359, 2020 | 47 | 2020 |
| Inference of COVID-19 epidemiological distributions from Brazilian hospital data I Hawryluk, TA Mellan, H Hoeltgebaum, S Mishra, RP Schnekenberg, ... Journal of the Royal Society Interface 17 (172), 20200596, 2020 | 39 | 2020 |
| Markovian gaussian process variational autoencoders H Zhu, C Balsells-Rodas, Y Li International Conference on Machine Learning, 42938-42961, 2023 | 28 | 2023 |
| A COVID‐19 model for local authorities of the United Kingdom S Mishra, JA Scott, DJ Laydon, H Zhu, NM Ferguson, S Bhatt, S Flaxman, ... Journal of the Royal Statistical Society: Series A (Statistics in Society …, 2022 | 27 | 2022 |
| Mammalian adaptation of influenza A (H7N9) virus is limited by a narrow genetic bottleneck. Nat Commun 6: 6553 H Zaraket, T Baranovich, BS Kaplan, R Carter, MS Song, JC Paulson, ... | 25 | 2015 |
| Subnational analysis of the COVID-19 epidemic in Brazil TA Mellan, HH Hoeltgebaum, S Mishra, C Whittaker, RP Schnekenberg, ... MedRxiv, 2020.05. 09.20096701, 2020 | 20 | 2020 |
| A sub-national analysis of the rate of transmission of COVID-19 in Italy MAC Vollmer, S Mishra, HJT Unwin, A Gandy, TA Mellan, V Bradley, ... | 20 | 2020 |
| The emergence of pandemic influenza viruses. Protein Cell 1: 9–13 Y Guan, D Vijaykrishna, J Bahl, H Zhu, J Wang, GJ Smith | 20 | 2010 |
| Grassmann Stein Variational Gradient Descent X Liu, H Zhu, JF Ton, G Wynne, A Duncan International Conference on Artificial Intelligence and Statistics, PMLR 151 …, 2022 | 19 | 2022 |
| Convolutional neural processes for inpainting satellite images A Pondaven, M Bakler, D Guo, H Hashim, M Ignatov, H Zhu arXiv preprint arXiv:2205.12407, 2022 | 14 | 2022 |
| VAE: a stochastic process prior for Bayesian deep learning with MCMC S Mishra, S Flaxman, T Berah, H Zhu, M Pakkanen, S Bhatt Statistics and Computing 32 (6), 96, 2022 | 11 | 2022 |
| Bayesian Probabilistic Numerical Integration with Tree-Based Models H Zhu, X Liu, R Kang, Z Shen, S Flaxman, FX Briol Advances in Neural Information Processing Systems, NeurIPS, 2020, 2020 | 7 | 2020 |