Evidence map›Paper›PMID 37732733›Full record

ArticleeLife2023

Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.

Arturo Torres Ortiz, Michelle Kendall, Nathaniel Storey, James Hatcher, Helen Dunn, Sunando Roy, Rachel Williams, Charlotte Williams, Richard A Goldstein, Xavier Didelot and 3 more

Open access · goldAbstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

14 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 5 institutions in 2 countries.

Arturo Torres OrtizDepartment of Infectious Diseases, Imperial College London, London, United Kingdom.ORCID 0000-0002-2492-0958
Michelle KendallDepartment of Statistics, University of Warwick, Coventry, United Kingdom.ORCID 0000-0001-7344-7071
Nathaniel StoreyDepartment of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
James HatcherDepartment of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
Helen DunnDepartment of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
Sunando RoyDepartment of Infection, Immunity and Inflammation, University College London, London, United Kingdom.
Rachel WilliamsUCL Genomics, University College London, London, United Kingdom.
Charlotte WilliamsUCL Genomics, University College London, London, United Kingdom.
Richard A GoldsteinUCL Genomics, University College London, London, United Kingdom.ORCID 0000-0001-5148-4672
Xavier DidelotDepartment of Statistics, University of Warwick, Coventry, United Kingdom.ORCID 0000-0003-1885-500X
Kathryn HarrisDepartment of Microbiology, Great Ormond Street Hospital, London, United Kingdom.
Judith BreuerDepartment of Infection, Immunity and Inflammation, University College London, London, United Kingdom.ORCID 0000-0001-8246-0534
Louis GrandjeanDepartment of Infection, Immunity and Inflammation, University College London, London, United Kingdom.
Institute of Child Health · INGreat Ormond Street Hospital · GBUniversity of Warwick · GBBarts Health NHS Trust · GBUniversity College London · GB

Funding

Using the Mycobacterium tuberculosis Genome to Predict Tuberculosis Pathology, Drug Resistance Acquisition and Identify Community Transmission SitesR01AI146338 · NIAID · JOHNS HOPKINS UNIVERSITY · PI GILMAN, ROBERT H · 2020 to 2024
$3.3M
NIAID NIH HHS R01 AI146338Wellcome TrustWellcome Trust 201470/Z/16/Z
6 · The paper itself

Abstract

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

Indexed as

Communicable DiseasesCOVID-19Disease OutbreaksHumansPhylogenySARS-CoV-2epidemiologygeneticsgenomicsglobal healthinfectious diseasephylogeneticsSARS-CoV-2viruseswithin-host diversity

Identifiers

PMID37732733
PMCPMC10602588
OpenAlexW4386910567

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.