Evidence map›Paper›PMID 34824245›Full record

ArticleNature communications2021

SARS-CoV-2 transmission across age groups in France and implications for control.

Cécile Tran Kiem, Paolo Bosetti, Juliette Paireau, Pascal Crépey, Henrik Salje, Noémie Lefrancq, Arnaud Fontanet, Daniel Benamouzig, Pierre-Yves Boëlle, Jean-Claude Desenclos and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Homophily within and across groups.Nature communications · 2025
    Article
  5. Article
  6. Article
  7. Use of cell phone data to correct Île-de-France population estimates and SARS-CoV-2 incidence, July to September, 2021: a proof-of-concept exercise.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

12 authors.

Cécile Tran KiemInstitut Pasteur, Université de Paris, Mathematical Modelling of Infectious Diseases Unit, CNRS UMR 2000, Paris, France.ORCID 0000-0003-0563-8428
Paolo BosettiInstitut Pasteur, Université de Paris, Mathematical Modelling of Infectious Diseases Unit, CNRS UMR 2000, Paris, France.
Juliette PaireauInstitut Pasteur, Université de Paris, Mathematical Modelling of Infectious Diseases Unit, CNRS UMR 2000, Paris, France.ORCID 0000-0002-5143-6256
Pascal CrépeyUniv Rennes, EHESP, REPERES (Recherche en Pharmaco-Epidémiologie et Recours aux Soins), EA 7449, Rennes, France.ORCID 0000-0003-1852-0752
Henrik SaljeInstitut Pasteur, Université de Paris, Mathematical Modelling of Infectious Diseases Unit, CNRS UMR 2000, Paris, France.ORCID 0000-0003-3626-4254
Noémie LefrancqInstitut Pasteur, Université de Paris, Mathematical Modelling of Infectious Diseases Unit, CNRS UMR 2000, Paris, France.ORCID 0000-0001-5991-6169
Arnaud FontanetInstitut Pasteur, Université de Paris, Emerging Diseases Epidemiology Unit, Paris, France.
Daniel BenamouzigSciences Po - Centre de sociologie des organisations and Chaire santé - CNRS, Paris, France.
Pierre-Yves BoëlleSorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.
Jean-Claude DesenclosSanté publique France, French National Public Health Agency, Saint-Maurice, France.ORCID 0000-0003-0892-8252
Lulla OpatowskiUniversité Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, Gif-sur-Yvette, France.
Simon CauchemezInstitut Pasteur, Université de Paris, Mathematical Modelling of Infectious Diseases Unit, CNRS UMR 2000, Paris, France. simon.cauchemez@pasteur.fr.ORCID 0000-0001-9186-4549

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 101003589EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 874735
6 · The paper itself

Abstract

The shielding of older individuals has been proposed to limit COVID-19 hospitalizations while relaxing general social distancing in the absence of vaccines. Evaluating such approaches requires a deep understanding of transmission dynamics across ages. Here, we use detailed age-specific case and hospitalization data to model the rebound in the French epidemic in summer 2020, characterize age-specific transmission dynamics and critically evaluate different age-targeted intervention measures in the absence of vaccines. We find that while the rebound started in young adults, it reached individuals aged ≥80 y.o. after 4 weeks, despite substantial contact reductions, indicating substantial transmission flows across ages. We derive the contribution of each age group to transmission. While shielding older individuals reduces mortality, it is insufficient to allow major relaxations of social distancing. When the epidemic remains manageable (R close to 1), targeting those most contributing to transmission is better than shielding at-risk individuals. Pandemic control requires an effort from all age groups.

Indexed as

SARS-CoV-2AdolescentAdultAgedAged, 80 and overAge FactorsChildChild, PreschoolComputer SimulationCOVID-19COVID-19 VaccinesFemaleFranceHospitalizationHumansInfantCOVID-19 Vaccines

Identifiers

PMID34824245
PMCPMC8617041

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.