Evidence map›Paper›PMID 33918060›Full record

ArticleViruses2021

Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2.

Shi Zhao, Jingzhi Lou, Marc K C Chong, Lirong Cao, Hong Zheng, Zigui Chen, Renee W Y Chan, Benny C Y Zee, Paul K S Chan, Maggie H Wang

Abstract read
In one paragraph

Article in Viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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  10. Article
  11. Article
  12. Human genetic basis of coronavirus disease 2019.Signal transduction and targeted therapy · 2021
    Review
  13. Article
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  15. 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

10 authors.

Shi ZhaoJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0001-8722-6149
Jingzhi LouJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
Marc K C ChongJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0001-5610-1298
Lirong CaoJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0002-5793-3878
Hong ZhengJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
Zigui ChenDepartment of Microbiology, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0002-8577-1298
Renee W Y ChanDepartment of Pediatrics, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0001-6485-0926
Benny C Y ZeeJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0002-7238-845X
Paul K S ChanDepartment of Microbiology, Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0002-6360-4608
Maggie H WangJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.

Funding

Health and Medical Research Fund INF-CUHK-1
6 · The paper itself

Abstract

As COVID-19 is posing a serious threat to global health, the emerging mutation in SARS-CoV-2 genomes, for example, N501Y substitution, is one of the major challenges against control of the pandemic. Characterizing the relationship between mutation activities and the risk of severe clinical outcomes is of public health importance for informing the healthcare decision-making process. Using a likelihood-based approach, we developed a statistical framework to reconstruct a time-varying and variant-specific case fatality ratio (CFR), and to estimate changes in CFR associated with a single mutation empirically. For illustration, the statistical framework is implemented to the COVID-19 surveillance data in the United Kingdom (UK). The reconstructed instantaneous CFR gradually increased from 1.0% in September to 2.2% in November 2020 and stabilized at this level thereafter, which monitors the mortality risk of COVID-19 on a real-time basis. We identified a link between the SARS-CoV-2 mutation activity at molecular scale and COVID-19 mortality risk at population scale, and found that the 501Y variants may slightly but not significantly increase 18% of fatality risk than the preceding 501N variants. We found no statistically significant evidence of change in COVID-19 mortality risk associated with 501Y variants, and highlighted the real-time estimating potentials of the modelling framework.

Indexed as

MutationCOVID-19HumansLikelihood FunctionsModels, BiologicalPandemicsPublic HealthSARS-CoV-2United KingdomB.1.1.7 lineagecase fatalityCOVID-19N501Y substitutionSARS-CoV-2statistical modelling

Identifiers

PMID33918060
PMCPMC8070306

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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.