Evidence map›Paper›PMID 34799659›Full record

ArticleScientific reports2021

Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure.

Debra Van Egeren, Alexander Novokhodko, Madison Stoddard, Uyen Tran, Bruce Zetter, Michael S Rogers, Diane Joseph-McCarthy, Arijit Chakravarty

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 15% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
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  5. Review
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  9. Observational
  10. Antigenic evolution of SARS-CoV-2 in immunocompromised hosts.Evolution, medicine, and public health · 2023
    Article
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  12. Review
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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

8 authors at 3 institutions in 1 country.

Debra Van EgerenHarvard Medical School, Boston, MA, USA.
Alexander NovokhodkoUniversity of Washington, Seattle, WA, USA.
Madison StoddardFractal Therapeutics, Cambridge, MA, USA.
Uyen TranFractal Therapeutics, Cambridge, MA, USA.
Bruce ZetterHarvard Medical School, Boston, MA, USA.
Michael S RogersHarvard Medical School, Boston, MA, USA.
Diane Joseph-McCarthyBoston University, Boston, MA, USA.
Arijit ChakravartyFractal Therapeutics, Cambridge, MA, USA. arijit@fractaltx.com.
Boston Children's Hospital · USDana-Farber Cancer Institute · USUniversity of Washington · US

Funding

National Science Foundation DGE-1762114
6 · The paper itself

Abstract

The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution.

Indexed as

Computer SimulationCOVID-19Evolution, MolecularHumansImmune EvasionMutationSARS-CoV-2TimeTreatment FailureViral Load

Identifiers

PMID34799659
PMCPMC8604936
OpenAlexW3211874812

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.