Modelling the dynamics of viral load to reveal mechanisms of protection in COVID-19

  • Funded by Department of Health and Social Care / National Institute for Health and Care Research (DHSC-NIHR), UK Research and Innovation (UKRI)
  • Total publications:1 publications

Grant number: MR/V027409/1

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $128,463.84
  • Funder

    Department of Health and Social Care / National Institute for Health and Care Research (DHSC-NIHR), UK Research and Innovation (UKRI)
  • Principle Investigator

    Pending
  • Research Location

    United Kingdom, Europe
  • Lead Research Institution

    Imperial College London
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Immunity

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Characterising the protective host-response in COVID-19 is a critical step towards developing effective treatments and vaccines. Increasing pathogen load stimulates the host response to infection, but identifying the protective components of the response in humans is challenging in cross-sectional studies. Variation occurs between individuals in both the dynamics of pathogen load over time and the relationship between pathogen load and magnitude of the host response, and this variation can be harnessed to identify correlates of protection. A mathematical model of the relationships between pathogen load and host response can be developed using population data and then used to make quantitative estimates of the model parameters determining pathogen load for individual subjects. Importantly, we have shown that parameter estimates in individuals can then be correlated with measured host factors, to identify biological mechanisms controlling pathogen load (Georgiadou et al Nature Microbiology 2019). We propose to develop a within-host model of viral load dynamics in COVID-19 and use it to: i) quantify parameters of viral load control for individuals; ii) predict clinical outcome; iii) identify constitutive host characteristics associated with control of viral load; iv) identify components of the blood and mucosal immune responses which control viral load. We build on extensive clinical and biological data generated by NIHR priority studies ISARIC-4C and DIAMONDS, adding value to these projects and expediting their public health impact. Our approach will deliver a prioritized list of host factors which control viral load dynamics, underpinning development of more effective, and potentially personalised, treatment and vaccine strategies.

Publicationslinked via Europe PMC

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