Evidence map›Paper›PMID 36974986›Full record

ArticleGenome biology and evolution2023

Nonsystematic Reporting Biases of the SARS-CoV-2 Variant Mu Could Impact Our Understanding of the Epidemiological Dynamics of Emerging Variants.

Mary E Petrone, Carolina Lucas, Bridget Menasche, Mallery I Breban, Inci Yildirim, Melissa Campbell, Saad B Omer, Edward C Holmes, Albert I Ko, Nathan D Grubaugh and 4 more

Open access · goldAbstract read
In one paragraph

Article in Genome biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

14 authors at 3 institutions in 2 countries.

Mary E PetroneDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Carolina LucasDepartment of Immunobiology, Yale University School of Medicine.
Bridget MenascheDepartment of Laboratory Medicine, Yale University School of Medicine.
Mallery I BrebanDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Inci YildirimDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Melissa CampbellDepartment of Medicine, Section of Infectious Diseases, Yale University School of Medicine.
Saad B OmerDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Edward C HolmesSydney Institute for Infectious Diseases, School of Medical Sciences, University of Sydney, NSW, Australia.ORCID 0000-0001-9596-3552
Albert I KoDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Nathan D GrubaughDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Akiko IwasakiDepartment of Immunobiology, Yale University School of Medicine.
Craig B WilenDepartment of Immunobiology, Yale University School of Medicine.
Chantal B F VogelsDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Joseph R FauverDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health.
Yale University · USThe University of Sydney · AUHoward Hughes Medical Institute · US

Funding

IMMUNOHEMATOLOGY/TRANSFUSION MEDICINE RESEARCH TRAININGT32HL007974 · NHLBI · YALE UNIVERSITY · PI JEANNE E HENDRICKSON, Diane S Krause · 2001 to 2026
$8.1M
Howard Hughes Medical InstituteNHLBI NIH HHS T32 HL007974
6 · The paper itself

Abstract

Developing a timely and effective response to emerging SARS-CoV-2 variants of concern (VOCs) is of paramount public health importance. Global health surveillance does not rely on genomic data alone to identify concerning variants when they emerge. Instead, methods that utilize genomic data to estimate the epidemiological dynamics of emerging lineages have the potential to serve as an early warning system. However, these methods assume that genomic data are uniformly reported across circulating lineages. In this study, we analyze differences in reporting delays among SARS-CoV-2 VOCs as a plausible explanation for the timing of the global response to the former VOC Mu. Mu likely emerged in South America in mid-2020, where its circulation was largely confined. In this study, we demonstrate that Mu was designated as a VOC ∼1 year after it emerged and find that the reporting of genomic data for Mu differed significantly than that of other VOCs within countries, states, and individual laboratories. Our findings suggest that nonsystematic biases in the reporting of genomic data may have delayed the global response to Mu. Until they are resolved, the surveillance gaps that affected the global response to Mu could impede the rapid and accurate assessment of future emerging variants.

Indexed as

COVID-19BiasGenomicsHumansSARS-CoV-2COVID-19MuphylogeneticsSARS-CoV-2variant of concern

Identifiers

PMID36974986
PMCPMC10113931
OpenAlexW4361191167

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.