Quantifying the effects of viral load on multistate COVID-19 infection and the progression of the Alpha and Omicron VOCs: a Bayesian competing Markov exponential regression model

We developed a Bayesian competing four-state Markov exponential regression model to explore how viral shedding in terms of cycle threshold (Ct) values makes a relative contribution between persistent and nonpersistent asymptomatic phenotypes and whether viral shedding affects the subsequent progression to symptoms. The proposed model was applied to data from two large community-acquired outbreaks involving both the Alpha and Omicron variants of concern (VOCs) in Changhua, Taiwan. A multistate Markov exponential regression model was proposed for quantifying the adjusted odds ratio (aOR) of viral shedding measured by cycle threshold (Ct) values. A Bayesian Markov Chain Monte Carlo (MCMC) method was used to estimate the parameters of the posterior distribution. For Alpha VOCs, the estimated results showed that the odds of developing a nonpersistent asymptomatic phenotype, as opposed to a persistent asymptomatic phenotype, were reduced by 14% (aOR = 0.86, 95% CI: 0.81–0.92) for each unit increase in the Ct value, whereas this figure shrunk to 5% (aOR = 0.95, 95% CI: 0.93–0.98) for Omicron VOCs. Similar significant gradient relationships were also observed from low to high viral load levels. Similar, but not statistically significant, dose–response effects of viral load on the progression to symptoms for the nonpersistent asymptomatic phenotype were observed. The proposed model elucidates the dose–response effects of viral shedding in conjunction with state-specific covariates on the two pathways of the SARS-CoV-2 infectious process via a nonpersistent or asymptomatic phenotype for both the Alpha and Omicron VOCs. Modeling the infectious process and disease progression superimposed with dynamic viral load and other state-specific covariates following two competing pathways of nonpersistent and persistent asymptomatic cases provides new insight into the development of personalized virological surveillance and the deployment of precision containment measures for responding to emerging or re-emerging infectious diseases.