Simplified within-host and Dose–response Models of SARS-CoV-2

Jingsi Xu; Jonathan Carruthers; Thomas Finnie; Ian Hall

doi:10.1016/j.jtbi.2023.111447

Simplified within-host and Dose–response Models of SARS-CoV-2

Jingsi Xu^*, Jonathan Carruthers, Thomas Finnie, Ian Hall

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose–response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose–response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

Original language	English
Article number	111447
Journal	Journal of Theoretical Biology
Volume	565
Early online date	8 Mar 2023
DOIs	https://doi.org/10.1016/j.jtbi.2023.111447
Publication status	Published - 21 May 2023

Bibliographical note

Funding Information: All authors acknowledge funding provided through PROTECT COVID-19 National Core Study on Transmission and the Environment. IH was also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1 ) and the Alan Turing Institute for Data Science and Artificial Intelligence . The views expressed are those of the authors and not necessarily those of the Department of Health and Social Care, the UK Health Security Agency or Health and Safety Executive.

Open Access: Free-to-read, but no Open Access licence.

Publisher Copyright: © 2023 Published by Elsevier Ltd.

Citation: Jingsi Xu, Jonathan Carruthers, Thomas Finnie, Ian Hall, Simplified within-host and Dose–response Models of SARS-CoV-2, Journal of Theoretical Biology, Volume 565, 2023, 111447, ISSN 0022-5193, https://doi.org/10.1016/j.jtbi.2023.111447.
(https://www.sciencedirect.com/science/article/pii/S0022519323000437)

DOI: https://doi.org/10.1016/j.jtbi.2023.111447.

Keywords

Dose–response
SARS-CoV-2
Within-host models

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jtbi.2023.111447

Cite this

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abstract = "Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose–response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose–response models, which illustrate the large variability of the periods of infection window observed for COVID-19.",

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note = "Funding Information: All authors acknowledge funding provided through PROTECT COVID-19 National Core Study on Transmission and the Environment. IH was also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1 ) and the Alan Turing Institute for Data Science and Artificial Intelligence . The views expressed are those of the authors and not necessarily those of the Department of Health and Social Care, the UK Health Security Agency or Health and Safety Executive. Open Access: Free-to-read, but no Open Access licence. Publisher Copyright: {\textcopyright} 2023 Published by Elsevier Ltd. Citation: Jingsi Xu, Jonathan Carruthers, Thomas Finnie, Ian Hall, Simplified within-host and Dose–response Models of SARS-CoV-2, Journal of Theoretical Biology, Volume 565, 2023, 111447, ISSN 0022-5193, https://doi.org/10.1016/j.jtbi.2023.111447. (https://www.sciencedirect.com/science/article/pii/S0022519323000437) DOI: https://doi.org/10.1016/j.jtbi.2023.111447.",

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Simplified within-host and Dose–response Models of SARS-CoV-2

Abstract

Bibliographical note

Keywords

UN SDGs

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