Elsevier

Epidemics

Volume 42, March 2023, 100662
Epidemics

Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning

https://doi.org/10.1016/j.epidem.2022.100662Get rights and content
Under a Creative Commons license
open access

Highlights

  • An agent-based model of COVID-19 transmission has been used in a healthcare setting.

  • Simulated scenarios have provided useful projections of the course of the epidemic.

  • Scenario outputs have enabled operational decision making and local planning.

Abstract

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.

Keywords

Epidemiology
Modelling
Agent-based models
Model calibration
Healthcare
Data

Cited by (0)

1

Contributed equally.