Evidence map›Paper›PMID 33750978›Full record

ArticlePLoS biology2021

A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2.

Kwang Su Kim, Keisuke Ejima, Shoya Iwanami, Yasuhisa Fujita, Hirofumi Ohashi, Yoshiki Koizumi, Yusuke Asai, Shinji Nakaoka, Koichi Watashi, Kazuyuki Aihara and 4 more

Abstract read
In one paragraph

Article in PLoS biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing 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

85 citing papers in PubMed.

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  19. Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025
    Review
  20. Article

25 more citing papers are in PubMed but not listed here.

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.

Kwang Su KimDepartment of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.ORCID 0000-0001-6626-316X
Keisuke EjimaDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, United States of America.ORCID 0000-0002-1185-3987
Shoya IwanamiDepartment of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.ORCID 0000-0003-1474-5348
Yasuhisa FujitaDepartment of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.ORCID 0000-0002-1720-3942
Hirofumi OhashiDepartment of Virology II, National Institute of Infectious Diseases, Tokyo, Japan.
Yoshiki KoizumiNational Center for Global Health and Medicine, Tokyo, Japan.
Yusuke AsaiNational Center for Global Health and Medicine, Tokyo, Japan.
Shinji NakaokaFaculty of Advanced Life Science, Hokkaido University, Sapporo, Japan.
Koichi WatashiDepartment of Virology II, National Institute of Infectious Diseases, Tokyo, Japan.
Kazuyuki AiharaInternational Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study, University of Tokyo, Tokyo, Japan.
Robin N ThompsonMathematics Institute, University of Warwick, Coventry, United Kingdom.ORCID 0000-0001-8545-5212
Ruian KeNew Mexico Consortium, Los Alamos, New Mexico, United States of America.ORCID 0000-0001-5307-8934
Alan S PerelsonNew Mexico Consortium, Los Alamos, New Mexico, United States of America.ORCID 0000-0002-2455-0002
Shingo IwamiDepartment of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.ORCID 0000-0002-1780-350X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

Indexed as

Antiviral AgentsBetacoronavirusCoronavirus InfectionsCOVID-19HumansLongitudinal StudiesMiddle East Respiratory Syndrome CoronavirusModels, BiologicalSARS-CoV-2Severe acute respiratory syndrome-related coronavirusViral LoadAntiviral Agents

Identifiers

PMID33750978
PMCPMC7984623

What OpenQuestion holds

Textmetadata
LicenceCC0
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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.