no code implementations • 24 Nov 2023 • Matthew J. Penn, Christl A. Donnelly, Samir Bhatt
Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used.
1 code implementation • 25 Oct 2022 • Matthew J. Penn, Daniel J. Laydon, Joseph Penn, Charles Whittaker, Christian Morgenstern, Oliver Ratmann, Swapnil Mishra, Mikko S. Pakkanen, Christl A. Donnelly, Samir Bhatt
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters).
no code implementations • 8 Jun 2022 • Emmanuelle A. Dankwa, Andrew F. Brouwer, Christl A. Donnelly
In this work, we studied the structural identifiability of some typical deterministic compartmental models for infectious disease transmission, focusing on the influence of the data type considered as model output on the identifiability of unknown model parameters, including initial conditions.
1 code implementation • 23 Apr 2020 • Seth Flaxman, Swapnil Mishra, Axel Gandy, H Juliette T Unwin, Helen Coupland, Thomas A. Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Callizo, Imperial College COVID-19 Response Team, Charles Whittaker, Peter Winskill, Xiaoyue Xi, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A. C. Vollmer, Neil M. Ferguson, Samir Bhatt
Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
Applications Methodology