Evidence map›Paper›PMID 33846321›Full record

ArticleNature communications2021

Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions.

Manon Ragonnet-Cronin, Olivia Boyd, Lily Geidelberg, David Jorgensen, Fabricia F Nascimento, Igor Siveroni, Robert A Johnson, Marc Baguelin, Zulma M Cucunubá, Elita Jauneikaite and 6 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 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  2. Article
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  5. Article
  6. Article
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  9. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  10. Article
  11. Review
  12. Article
  13. Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022
    Article
  14. Markov genealogy processes.Theoretical population biology · 2022
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Extreme immunotherapy: emergency immunology to defeat pandemics.Molecular medicine (Cambridge, Mass.) · 2021
    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.

Manon Ragonnet-CroninMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK. manonragonnet@imperial.ac.uk.ORCID 0000-0002-4879-2209
Olivia Boyd *MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Lily Geidelberg *MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
David Jorgensen *MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Fabricia F Nascimento *MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Igor Siveroni *MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0003-2595-3062
Robert A JohnsonMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Marc BaguelinMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Zulma M CucunubáMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0002-8165-3198
Elita JauneikaiteMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0002-7075-6896
Swapnil MishraMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0002-8759-5902
Oliver J WatsonMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0003-2374-0741
Neil FergusonMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0002-1154-8093
Anne CoriMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Christl A DonnellyMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0002-0195-2463
Erik VolzMRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK.ORCID 0000-0001-6268-8937

Funding

Department of HealthMedical Research Council MC_PC_19012Medical Research Council MR/R015600/1Wellcome TrustWellcome Trust 220885/Z/20/Z
6 · The paper itself

Abstract

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

Indexed as

PhylogenyCommunicable Disease ControlCOVID-19EpidemicsHumansPhylogeographyPublic HealthSARS-CoV-2Severity of Illness Index

Identifiers

PMID33846321
PMCPMC8041850

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.