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

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.

Keywords: Dose–response; SARS-CoV-2; Within-host models.

Fig. 1

(Scenario 1) Approximate participant-merged posterior distribution of parameter values from the result of ABC-SMC with model from Eq. (2.3) using throat data (Killingley et al., 2022).

Fig. 2

(Scenario 1) Approximate participant-merged Posterior distribution of parameter values from the result of ABC-SMC with model from Eq. (2.3) using mid-turbinate data (Killingley et al., 2022).

Fig. 3

(Scenario 2) Approximate Posterior distribution of parameter values from the result of ABC-SMC with model from Eq. (2.2) using throat data (Killingley et al., 2022).

Fig. 4

(Scenario 2) Approximate Posterior distribution of parameter values from the result of ABC-SMC with model from Eq. (2.2) using throat data (Killingley et al., 2022).

Fig. 5

(Scenario 3) Approximate Posterior distribution of parameter values from the result of ABC-SMC with model from Eq. (2.2) using throat data (Killingley et al., 2022).

Fig. 6

(Scenario 3) Approximate Posterior distribution of parameter values from the result of ABC-SMC with model from Eq. (2.2) using mid-turbinate data (Killingley et al., 2022).

Fig. 7

Viral load dependent probability of infection from exponential dose–response function (3.2).

Fig. 8

Viral load dependent probability of infection from approximate Beta-Poisson dose–response function (3.3) under different values of $\nu$.

Fig. 9

Viral load dependent probability of infection from logistic dose–response function (3.5) under different values of $\eta$.