Evidence map›Paper›PMID 36563470›Full record

ArticleEpidemics2023

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 and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Nick Groves-KirkbyEconomics and Strategic Analysis, NHS England, London, UK. Electronic address: n.groves-kirkby@nhs.net.
Ewan WakemanAnalytical Services, NHS England, London, UK.
Seema PatelEconomics and Strategic Analysis, NHS England, London, UK.
Robert HinchBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Tineke PootEconomics and Strategic Analysis, NHS England, London, UK.
Jonathan PearsonAnalytics Unit - Innovation, NHSX, London, UK.
Lily TangEconomics and Strategic Analysis, NHS England, London, UK.
Edward KendallEconomics and Strategic Analysis, NHS England, London, UK.
Ming TangDirectorate of the Chief Data & Analytics Officer, NHS England, London, UK.
Kim MooreFaculty, London, UK.
Scott StevensonFaculty, London, UK.
Bryn MathiasFaculty, London, UK.
Ilya FeigeFaculty, London, UK.
Simon NakachFaculty, London, UK.
Laura StevensonFaculty, London, UK.
Paul O'DwyerFaculty, London, UK.
William ProbertBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Jasmina Panovska-GriffithsBig 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 FraserBig 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19CalibrationCOVID-19 VaccinesDelivery of Health CareEcosystemHumansPandemicsState MedicineCOVID-19 VaccinesAgent-based modelsDataEpidemiologyHealthcareModel calibrationModelling

Identifiers

PMID36563470
PMCPMC9758760

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.