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Living with COVID-19 requires continued vigilance against the spread and emergence of variants of concern (VOCs). Rapid and accurate saliva diagnostic testing, alongside basic public health responses, is a viable option contributing to effective transmission control. Nevertheless, our knowledge regarding the dynamics of SARS-CoV-2 infection in saliva is not as advanced as our understanding of the respiratory tract. Here, we analyzed longitudinal viral load data of SARS-CoV-2 in saliva samples from 144 patients with mild COVID-19 (a combination of our collected data and published data). Using a mathematical model, we quantified individual-level viral dynamics and stratified them into three groups using a clustering approach. Notably, the three groups exhibited distinct differences in viral RNA detection durations: 11.5 days (95% CI: 10.6-12.4), 17.4 days (16.6-18.2), and 30.0 days (28.1-31.8), respectively. Surprisingly, this stratified grouping remained unexplained despite our analysis of 47 types of clinical data, including basic demographic information, clinical symptoms, results of blood tests, and vital signs. Additionally, we quantified the expression levels of 92 micro-RNAs in a subset of saliva samples, but these also failed to explain the observed stratification, although the mir-1846 level may have been weakly correlated with peak viral load. Our study provides insights into SARS-CoV-2 infection dynamics in saliva, highlighting the challenges in predicting the duration of viral RNA detection without indicators that directly reflect an individual's immune response, such as antibody induction. Given the significant individual heterogeneity in the kinetics of saliva viral shedding, identifying biomarker(s) for viral shedding patterns will be crucial for improving public health interventions in the era of living with COVID-19.
Pubmed ID: 41543519
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scikit-learn: machine learning in Python
View all literature mentionsCommercial developer of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and Physiologically-Based Pharmacokinetic (PBPK) modeling and simulation software for the pharmaceutical and biotechnology, industrial chemical, cosmetic, herbicide, and food ingredient industries. Their software allows pharmaceutical scientists to predict certain key potential endpoints and dynamics, in silico, thereby reducing research & development costs and helping clients make better projects decisions sooner. Consulting services: Their team of highly-skilled scientists is available to assist with your discovery and development activities on a project-by-project basis. They now offer expanded pharmacodynamic modeling services, NONMEM analysis, and regulatory report writing assistance. Their ultimate goal is to develop tools and systems which bridge PBPK modeling with clinical trial data analysis. Training workshops & User Groups: they offer hands-on introductory and advanced training workshops in the United States, Europe, and Asia throughout the year.
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