COVID-19: the role of modelling in UK response
The House of Lords Science and Technology Committee will next week continue its inquiry into the science of COVID-19. These sessions will examine the modelling of epidemic and pandemics, including a closer look at the R number, as well as modelling of different approaches to ease restrictions.
The committee will hear from leading academics, and will cover topics including: the assumptions that epidemiological models make, differences in approach between modelling groups, how R is estimated and the extent to which the epidemic in the UK is panning out in the way that models have suggested.
The evidence session will be conducted on zoom and can be followed at www.parliamentlive.tv from 10am on Tuesday 2 June. Giving evidence will be:
- Dr Ellen Brooks-Pollock, Lecturer in Infectious Disease Mathematical Modelling, University of Bristol
- Professor Neil Ferguson FMedSci OBE, Head of the Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London
- Professor Matt Keeling, Professor of Mathematics and Life Sciences, University of Warwick
- Dr Adam Kucharski, Associate Professor, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (LSHTM)
In the second session, the Committee will hear from further leading experts, as it delves deeper into the UK modelling approaches. This will include questions on model evaluation, the consideration of uncertainty in model inputs and parameters, how modelling is being used to assess how we ease restrictions, and where further modelling work is needed.
The second evidence session will continue on zoom and can be followed at www.parliamentlive.tv from 11am on Tuesday 2 June. Giving evidence will be:
- Dr Paul Birrell, Postdoctoral Researcher, Cambridge Medical Research Council (MRC) Biostatistics Group
- Professor Deirdre Hollingsworth, Senior Group Leader, Big Data Institute, University of Oxford
- Professor Mark Woolhouse FRSE FMedSci OBE, Professor of Infectious Disease Epidemiology, University of Edinburgh
