Evidence map›Paper›PMID 34053266›Full record

ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2021

Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown.

Bram A D van Bunnik, Alex L K Morgan, Paul R Bessell, Giles Calder-Gerver, Feifei Zhang, Samuel Haynes, Jordan Ashworth, Shengyuan Zhao, Roo Nicola Rose Cave, Meghan R Perry and 6 more

Abstract read
In one paragraph

Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 5 pooled it
–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

25 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020
    Pooled it
  6. Review
  7. Article
  8. Review
  9. Antigenic evolution of SARS-CoV-2 in immunocompromised hosts.Evolution, medicine, and public health · 2023
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
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  17. Article
  18. Modelling that shaped the early COVID-19 pandemic response in the UK.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021
    Article
  19. Article
  20. 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

16 authors.

Bram A D van BunnikUsher Institute, University of Edinburgh, Edinburgh, UK.
Alex L K MorganSchool of Biological Sciences, University of Edinburgh, Edinburgh, UK.
Paul R BessellThe Roslin Institute, University of Edinburgh, Edinburgh, UK.
Giles Calder-GerverUsher Institute, University of Edinburgh, Edinburgh, UK.
Feifei ZhangUsher Institute, University of Edinburgh, Edinburgh, UK.
Samuel HaynesSchool of Biological Sciences, University of Edinburgh, Edinburgh, UK.
Jordan AshworthUsher Institute, University of Edinburgh, Edinburgh, UK.
Shengyuan ZhaoUsher Institute, University of Edinburgh, Edinburgh, UK.
Roo Nicola Rose CaveSchool of Biological Sciences, University of Edinburgh, Edinburgh, UK.
Meghan R PerryClinical Infection Research Group, Regional Infectious Diseases Unit, Western General Hospital, UK.
Hannah C LepperUsher Institute, University of Edinburgh, Edinburgh, UK.
Lu LuUsher Institute, University of Edinburgh, Edinburgh, UK.
Paul KellamDepartment of Medicine, Division of Infectious Diseases, Imperial College London, UK.
Aziz SheikhUsher Institute, University of Edinburgh, Edinburgh, UK.
Graham F MedleyCentre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
Mark E J WoolhouseUsher Institute, University of Edinburgh, Edinburgh, UK.

Funding

Wellcome TrustWellcome Trust 218492/Z/19/Z
6 · The paper itself

Abstract

This study demonstrates that an adoption of a segmenting and shielding strategy could increase the scope to partially exit COVID-19 lockdown while limiting the risk of an overwhelming second wave of infection. We illustrate this using a mathematical model that segments the vulnerable population and their closest contacts, the 'shielders'. Effects of extending the duration of lockdown and faster or slower transition to post-lockdown conditions and, most importantly, the trade-off between increased protection of the vulnerable segment and fewer restrictions on the general population are explored. Our study shows that the most important determinants of outcome are: (i) post-lockdown transmission rates within the general and between the general and vulnerable segments; (ii) fractions of the population in the vulnerable and shielder segments; (iii) adherence to protective measures; and (iv) build-up of population immunity. Additionally, we found that effective measures in the shielder segment, e.g. intensive routine screening, allow further relaxations in the general population. We find that the outcome of any future policy is strongly influenced by the contact matrix between segments and the relationships between physical distancing measures and transmission rates. This strategy has potential applications for any infectious disease for which there are defined proportions of the population who cannot be treated or who are at risk of severe outcomes. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Indexed as

PandemicsCommunicable Disease ControlCOVID-19HumansModels, TheoreticalSARS-CoV-2United KingdomCOVID-19exit strategymathematical modelSARS-CoV-2segmenting and shielding

Identifiers

PMID34053266
PMCPMC8165590

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Registered trials

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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.