Quantifying the Effects of Viral Load on Multistate COVID-19 Infection and Progression of Alpha and Omicron VOCs: A Bayesian Competing Markov Exponential Regression Model

Abstract

Abstract We used a Bayesian competing four-state Markov model to explore how viral shedding in terms of cycle threshold (Ct) value makes relative contribution between persistent and non-persistent asymptomatic mode, and whether it affects the subsequent progression to show symptoms. The proposed model was applied to data from two large outbreaks on Alpha and Omicron variants of concern (VOCs) in Changhua, Taiwan. A multistate Markov exponential regression model was proposed for quantifying the odds ratio (OR) of viral shedding measured by cycle threshold (Ct). A Bayesian Markov Chain Monte Carlo (MCMC) method was used for estimating the parameters of the posterior distribution. The estimated results show that developing non-persistent asymptomatic mode relative to persistent asymptomatic mode was reduced by 14% (adjusted OR = 0.86, 95% CI: 0.81–0.92) per one increasing unit of Ct for Alpha VOC, whereas these figures were shrunk to 5% (aOR = 0.95, 95% CI: 0.93–0.98) for Omicron VOC. Similar significant gradient relationships were also observed between three viral load levels. Similar, but not statistically significant, dose-response effects of viral load on the progression to symptoms for non-persistent asymptomatic mode were observed. The application of statistical model helps elucidate the pathways of SARS-CoV-2 infectious process associated with viral shedding that demonstrate viral shedding plays a crucial role in determining the path of either non-persistent or persistent asymptomatic mode in a dose-response manner, which was more pronounced for the Alpha than the Omicron. Modelling such a multistate infectious process with two competing pathways would provide a new insight into the transmissibility and the duration of insidious infection before onset of symptom and the deployment of precision containment measures with a better use of the Ct value as virologic surveillance for projecting the individual epidemic course.