Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning
Nick Groves-Kirkby,
Ewan Wakeman,
Seema Patel,
Robert Hinch,
Tineke Poot,
Jonathan Pearson,
Lily Tang,
Edward Kendall,
Ming Tang,
Kim Moore,
Scott Stevenson,
Bryn Mathias,
Ilya Feige,
Simon Nakach,
Laura Stevenson,
Paul O'Dwyer,
William Probert,
Jasmina Panovska-Griffiths,
Christophe Fraser
Affiliations
Nick Groves-Kirkby
Economics and Strategic Analysis, NHS England, London, UK; Corresponding author.
Ewan Wakeman
Analytical Services, NHS England, London, UK
Seema Patel
Economics and Strategic Analysis, NHS England, London, UK
Robert Hinch
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
Tineke Poot
Economics and Strategic Analysis, NHS England, London, UK
Jonathan Pearson
Analytics Unit – Innovation, NHSX, London, UK
Lily Tang
Economics and Strategic Analysis, NHS England, London, UK
Edward Kendall
Economics and Strategic Analysis, NHS England, London, UK
Ming Tang
Directorate of the Chief Data & Analytics Officer, NHS England, London, UK
Kim Moore
Faculty, London, UK
Scott Stevenson
Faculty, London, UK
Bryn Mathias
Faculty, London, UK
Ilya Feige
Faculty, London, UK
Simon Nakach
Faculty, London, UK
Laura Stevenson
Faculty, London, UK
Paul O'Dwyer
Faculty, London, UK
William Probert
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
Jasmina Panovska-Griffiths
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen’s College, University of Oxford, Oxford, UK
Christophe Fraser
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
The COVID-19 pandemic has provided stiff challenges for planning and resourcing in health services in the UK and worldwide. Epidemiological models can provide simulations of how infectious disease might progress in a population given certain parameters. We adapted an agent-based model of COVID-19 to inform planning and decision-making within a healthcare setting, and created a software framework that automates processes for calibrating the model parameters to health data and allows the model to be run at national population scale on National Health Service (NHS) infrastructure. We developed a method for calibrating the model to three daily data streams (hospital admissions, intensive care occupancy, and deaths), and demonstrate that on cross-validation the model fits acceptably to unseen data streams including official estimates of COVID-19 incidence. Once calibrated, we use the model to simulate future scenarios of the spread of COVID-19 in England and show that the simulations provide useful projections of future COVID-19 clinical demand. These simulations were used to support operational planning in the NHS in England, and we present the example of the use of these simulations in projecting future clinical demand during the rollout of the national COVID-19 vaccination programme. Being able to investigate uncertainty and test sensitivities was particularly important to the operational planning team. This epidemiological model operates within an ecosystem of data technologies, drawing on a range of NHS, government and academic data sources, and provides results to strategists, planners and downstream data systems. We discuss the data resources that enabled this work and the data challenges that were faced.
