Evidence map›Paper›PMID 34745593›Full record

ArticleHealth systems (Basingstoke, England)2021

Establishing an SEIR-based framework for local modelling of COVID-19 infections, hospitalisations and deaths.

R M Wood, A C Pratt, B J Murch, A L Powell, R D Booton, D G Thomas, J Twigger, E Diakou, S Coleborn, T Manning and 2 more

Abstract read
In one paragraph

Article in Health systems (Basingstoke, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

R M WoodBristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK.ORCID https://orcid.org/0000-0002-3476-395X
A C PrattBristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK.
B J MurchBristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK.
A L PowellBristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK.
R D BootonBristol Medical School, University of Bristol, Bristol, UK.
D G ThomasPublic Health, Bristol City Council, Bristol, UK.
J TwiggerPublic Health, Bristol City Council, Bristol, UK.
E DiakouBusiness Intelligence, North Somerset Council, Weston-Super-Mare, UK.
S ColebornPublic Health, South Gloucestershire Council, Yate, UK.
T ManningBristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK.
C DaviesBristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK.
K M TurnerBristol Medical School, University of Bristol, Bristol, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Without timely assessments of the number of COVID-19 cases requiring hospitalisation, healthcare providers will struggle to ensure an appropriate number of beds are made available. Too few could cause excess deaths while too many could result in additional waits for elective treatment. As well as supporting capacity considerations, reliably projecting future "waves" is important to inform the nature, timing and magnitude of any localised restrictions to reduce transmission. In making the case for locally owned and locally configurable models, this paper details the approach taken by one major healthcare system in founding a multi-disciplinary "Scenario Review Working Group", comprising commissioners, public health officials and academic epidemiologists. The role of this group, which met weekly during the pandemic, was to define and maintain an evolving library of plausible scenarios to underpin projections obtained through an SEIR-based compartmental model. Outputs have informed decision-making at the system's major incident Bronze, Silver and Gold Commands. This paper presents illustrated examples of use and offers practical considerations for other healthcare systems that may benefit from such a framework.

Indexed as

compartmental modellingcoronavirusCOVID-19public healthscenario analysis

Identifiers

PMID34745593
PMCPMC8567954

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.