Evidence map›Paper›PMID 33501443›Full record

ArticlebioRxiv : the preprint server for biology2021

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts.

Andrew L Valesano, Kalee E Rumfelt, Derek E Dimcheff, Christopher N Blair, William J Fitzsimmons, Joshua G Petrie, Emily T Martin, Adam S Lauring

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 31 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Andrew L ValesanoDivision of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Kalee E RumfeltDivision of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Derek E DimcheffDivision of Hospital Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Christopher N BlairDivision of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
William J FitzsimmonsDivision of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Joshua G PetrieDepartment of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
Emily T MartinDepartment of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
Adam S LauringDivision of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
University of Michigan · US

Funding

US Hospital Vaccine Effectiveness (VE) Network - SupplementU01IP000974 · IP · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MONTO, ARNOLD S · 2015 to 2019
$4.5M
Hospital-Associated Respiratory Virus Infections: Molecular Epidemiology, Clinical Outcomes, and Cost-Effectiveness of Interventions - COVID-19 Administrative SupplementK01AI141579 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PETRIE, JOSHUA GLENN · 2019 to 2023
$634k
NCIRD CDC HHS U01 IP000974NIAID NIH HHS K01 AI141579
6 · The paper itself

Abstract

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified a viral load threshold that minimizes false positives. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.

Identifiers

PMID33501443
PMCPMC7836113
OpenAlexW3124390264

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.