Within-host SARS-CoV-2 viral kinetics informed by complex life course exposures reveals different intrinsic properties of Omicron and Delta variants

Abstract

The emergence of successive SARS-CoV-2 variants of concern (VOC) during 2020-22, each exhibiting increased epidemic growth relative to earlier circulating variants, has created a need to understand the drivers of such growth. However, both pathogen biology and changing host characteristics - such as varying levels of immunity - can combine to influence replication and transmission of SARS-CoV-2 within and between hosts. Disentangling the role of variant and host in individual-level viral shedding of VOCs is essential to inform COVID-19 planning and response, and interpret past epidemic trends. Using data from a prospective observational cohort study of healthy adult volunteers undergoing weekly occupational health PCR screening, we developed a Bayesian hierarchical model to reconstruct individual-level viral kinetics and estimate how different factors shaped viral dynamics, measured by PCR cycle threshold (Ct) values over time. Jointly accounting for both inter-individual variation in Ct values and complex host characteristics - such as vaccination status, exposure history and age - we found that age and number of prior exposures had a strong influence on peak viral replication. Older individuals and those who had at least five prior antigen exposures to vaccination and/or infection typically had much lower levels of shedding. Moreover, we found evidence of a correlation between the speed of early shedding and duration of incubation period when comparing different VOCs and age groups. Our findings illustrate the value of linking information on participant characteristics, symptom profile and infecting variant with prospective PCR sampling, and the importance of accounting for increasingly complex population exposure landscapes when analysing the viral kinetics of VOCs.

Figure 1.. Schematic of the study design and modelling procedure.

A. Schematic of the cohort and data collection procedure used for the subset of the Legacy study. B. Typical Ct value data and model fits for two representative individuals with different covariates. C. Schematic of the individual-level and covariate-level model fits. The model fits each participants viral kinetics and pools the estimates in a statistically robust manner.

Figure 2.. Summary of longitudinal cohort dataset analysed.

A. Ct value distribution for all participants, stratified by the interaction between the number of vaccines and the infecting VOC, the interaction between the number of infections and the infecting VOC and finally by the four covariate categories used throughout the study: VOC, symptom status, number of exposures and age. B. The individual-level Ct data, including the timing of each test, the Ct value of the result, given by the colour and the number of vaccinations, infections, and the total exposures for each participant on the left of their individual timeline.

Figure 3.. Fitted covariate-level cycle threshold trajectories and the differences in the parameter values stratified for each covariate.

All left panels: Model fits of the viral kinetics over the course of whole infections, beginning from the inferred exposure time. After fitting the model, we simulated 10,000 trajectories, using parameters sampled from the inferred posterior distributions for each covariate considered. We then calculate the median and 95% credible interval for each covariate and plot the resulting trajectories as coloured regions, with a sample of 100 trajectories plotted transparently beneath. All right panels: the median and 95% estimates of the inferred parameter values for the peak Ct value, the timing of the peak and the time at which the trajectory hits the LOD. Dashed line shows the value of the baseline participants inferred parameter values. A. Trajectories and parameter values for the VOCs considered. B. Trajectories and parameter values for the symptom statuses considered. C. Trajectories and parameter values for the numbers of exposures considered. D. Trajectories and parameter values for the age groups considered.

Figure 4.. Bivariate density plots of median incubation periods against the time at which trajectories surpassed an assumed Ct value corresponding to high viral loads, for each covariate.

All panels: We plot the posterior distributions for the estimated incubation periods, stratified by all covariates considered, against the time at which the simulated Ct trajectories, as shown in Figure 3, cross an assumed Ct value threshold for the first time (Ct value = 20). This Ct value represents a timepoint in the covariate-level trajectories of high viral load. A. Parameter values for the three VOCs considered. B. Parameter values corresponding to symptomatic infections. C. Parameter values corresponding to the numbers of exposures considered. D. Parameter values for the age groups considered. An alternative Ct value threshold (Ct = 25) was considered in an analogous analysis in the Supplementary Material (Figure S14).