Simplified within-host and Dose–response Models of SARS-CoV-2. (21st May 2023)

• Record Type: Journal Article
• Authors: Xu, Jingsi; Carruthers, Jonathan; Finnie, Thomas; Hall, Ian
• Is Part Of: Journal of theoretical biology. Volume 565(2023)
• Publication Date: 2023-05-21
• Subjects: Within-host models -- Dose–response -- SARS-CoV-2; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2023.111447
• Languages: English
• ISSNs: 0022-5193

Abstract

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. Highlights: Simplified within-host models explain two viral decay mechanisms. Simplified model limits unidentifiability issues and explain human challenge data. Dose–response models under the competing risk framework show a broader infectious window. Comparison between dose–response models under competing risk framework and previous studies. Viral load is used as a strong determinant of the probability of infection.