Evidence map›Paper›PMID 35710987›Full record

ArticleNature medicine2022

Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance.

Lirong Cao, Jingzhi Lou, See Yeung Chan, Hong Zheng, Caiqi Liu, Shi Zhao, Qi Li, Chris Ka Pun Mok, Renee Wan Yi Chan, Marc Ka Chun Chong and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
4.3field-weighted citation impact, top 4% of its field
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

31 citing papers in PubMed, 48 citations in OpenAlex.

  1. Trial
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  6. Integrating Genomic Data into Test-negative Designs for Estimating Lineage-specific COVID-19 Vaccine Effectiveness.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
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  9. Review
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  15. Observational
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  20. Review
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

17 authors at 1 institution in 2 countries.

Lirong CaoJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Jingzhi LouBeth Bioinformatics Co. Ltd., Hong Kong SAR, China.
See Yeung ChanJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Hong ZhengJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Caiqi LiuJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Shi ZhaoJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Qi LiJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-8943-3202
Chris Ka Pun MokJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-0525-6772
Renee Wan Yi ChanDepartment of Paediatrics, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0001-6485-0926
Marc Ka Chun ChongJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0001-5610-1298
William Ka Kei WuLi Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-5662-5240
Zigui ChenDepartment of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-8577-1298
Eliza Lai Yi WongJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Paul Kay Sheung ChanDepartment of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-6360-4608
Benny Chung Ying ZeeJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Eng Kiong YeohJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Maggie Haitian WangJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China. maggiew@cuhk.edu.hk.ORCID http://orcid.org/0000-0003-1223-4595
Chinese University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Timely evaluation of the protective effects of Coronavirus Disease 2019 (COVID-19) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is urgently needed to inform pandemic control planning. Based on 78 vaccine efficacy or effectiveness (VE) data from 49 studies and 1,984,241 SARS-CoV-2 sequences collected from 31 regions, we analyzed the relationship between genetic distance (GD) of circulating viruses against the vaccine strain and VE against symptomatic infection. We found that the GD of the receptor-binding domain of the SARS-CoV-2 spike protein is highly predictive of vaccine protection and accounted for 86.3% (P = 0.038) of the VE change in a vaccine platform-based mixed-effects model and 87.9% (P = 0.006) in a manufacturer-based model. We applied the VE-GD model to predict protection mediated by existing vaccines against new genetic variants and validated the results by published real-world and clinical trial data, finding high concordance of predicted VE with observed VE. We estimated the VE against the Delta variant to be 82.8% (95% prediction interval: 68.7-96.0) using the mRNA vaccine platform, closely matching the reported VE of 83.0% from an observational study. Among the four sublineages of Omicron, the predicted VE varied between 11.9% and 33.3%, with the highest VE predicted against BA.1 and the lowest against BA.2, using the mRNA vaccine platform. The VE-GD framework enables predictions of vaccine protection in real time and offers a rapid evaluation method against novel variants that may inform vaccine deployment and public health responses.

Indexed as

COVID-19Viral VaccinesCOVID-19 VaccinesHumansmRNA VaccinesSARS-CoV-2Spike Glycoprotein, CoronavirusVaccine EfficacyVaccines, SyntheticCOVID-19 VaccinesmRNA VaccinesSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2Vaccines, SyntheticViral Vaccines

Identifiers

PMID35710987
PMCPMC9388371
OpenAlexW4282972656

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.