Evidence map›Paper›PMID 33245943›Full record

ReviewThe Journal of infection2021

SARS-CoV-2 (COVID-19) superspreader events.

Dasha Majra, Jayme Benson, Jennifer Pitts, Justin Stebbing

Abstract readReview
In one paragraph

Review in The Journal of infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

64 citing papers in PubMed.

  1. Trial
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  4. Review
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  10. Observational
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  15. Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Observational

4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Dasha MajraUniversity of Manchester Medical School, United Kingdom. Electronic address: dasha.majra@student.manchester.ac.uk.
Jayme BensonClare College, University of Cambridge, United Kingdom.
Jennifer PittsNewnham College, University of Cambridge, United Kingdom.
Justin StebbingDepartment of Surgery and Cancer, Imperial College, London, United Kingdom.

Funding

Department of Health NIHR-RP-011-053
6 · The paper itself

Abstract

BACKGROUND &

objectivesA significant number of reported COVID-19 cases can be traced back to superspreader events (SSEs), where a disproportionally large number of secondary cases relative to the standard reproductive rate, R0, are initiated. Although a superspreader is an individual who undergoes more viral shedding and transmission than others, it appears likely that environmental factors have a substantial role in SSEs. We categorise SSEs into two distinct groups: 'societal' and 'isolated' SSEs.

methodsWe summarise SSEs that have occurred using multiple databases that have been cross referenced to ensure numbers are as reliable as we can ascertain. This enables more focussed and productive control of the current pandemic and future pandemics, especially as countries and regions ease lockdown restrictions. RESULTS AND DISCUSSION: 'Societal' SSEs pose a significant threat as members of the event are free to mingle and can infect individuals in the outside community. On the other hand, 'isolated' SSEs can be effectively quarantined as only a few individuals can transmit the virus from the isolated community to the outside community, therefore lowering further societal infection.

Indexed as

CrowdingBasic Reproduction NumberContact TracingCOVID-19Disease Transmission, InfectiousHumansPersonal Protective EquipmentPhysical DistancingSARS-CoV-2COVID-19InfectionSARS-CoV-2SuperspreaderTransmissible

Identifiers

PMID33245943
PMCPMC7685932

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.