Evidence map›Paper›PMID 35022051›Full record

ArticleBMC medicine2022

Modelling upper respiratory viral load dynamics of SARS-CoV-2.

Joseph D Challenger, Cher Y Foo, Yue Wu, Ada W C Yan, Mahdi Moradi Marjaneh, Felicity Liew, Ryan S Thwaites, Lucy C Okell, Aubrey J Cunnington

Abstract read
In one paragraph

Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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  14. The kinetics of SARS-CoV-2 infection based on a human challenge study.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

9 authors.

Joseph D ChallengerMedical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK. j.challenger@imperial.ac.uk.ORCID 0000-0001-6029-1733
Cher Y FooSchool of Medicine, Imperial College London, London, UK.
Yue WuSchool of Clinical Medicine, University of Cambridge, Cambridge, UK.
Ada W C YanDepartment of Infectious Disease, Imperial College London, London, UK.
Mahdi Moradi MarjanehSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
Felicity LiewNational Heart and Lung Institute, Imperial College London, London, UK.
Ryan S ThwaitesNational Heart and Lung Institute, Imperial College London, London, UK.
Lucy C OkellMedical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Aubrey J CunningtonSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.

Funding

imperial college london Covid-19 Respond FundMedical Research Council MC_PC_19012Medical Research Council MR/R015600/1Medical Research Council MR/V027409/1ukri MR/V027409/1
6 · The paper itself

Abstract

Relationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.

Indexed as

COVID-19SARS-CoV-2Antibodies, ViralHumansViral LoadAntibodies, Viral

Identifiers

PMID35022051
PMCPMC8755404

What OpenQuestion holds

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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.