AI Insight
This study develops a Bayesian joint statistical model of within-host SARS-CoV-2 viral kinetics, integrating longitudinal data from approximately 2,000 infections across five prospective cohorts. By combining multiple proxies of viral shedding, including PCR, antigen tests, viral culture, and symptom onset, the model infers trajectories of infectious virus shedding even when only PCR data are available. The framework enables estimation of population-level probabilities and durations of infectiousness stratified by variant, vaccination status, and infection history, as well as real-time, personalized infectiousness estimates that update sequentially with new test results.
Why it matters
This model provides actionable, evidence-based guidance for isolation policy decisions, including quantifying the residual transmission risk of releasing individuals from isolation at different timepoints. Its ability to generate individualized infectiousness estimates from routinely collected test data could directly inform clinical and public health practice during future outbreaks.
arXiv:2605.20692v1 Announce Type: cross
Abstract: During an infectious disease outbreak, providing accurate answers to policy questions about transmission requires a detailed model of the natural history of infectiousness. Unfortunately, direct measures of infectiousness are generally unavailable. Instead, we often rely on indirect proxies, such as viral load measured by PCR or antigen tests, viral culture to detect replication-competent virus, or symptom onset, each of which reflects different aspects of viral dynamics or host response. However, these proxies vary in terms of the ease of collection, scalability, and their relationship to viral shedding and therefore underlying infectiousness. Here, we use data from five prospective, densely sampled cohorts with longitudinal data on multiple proxies of viral shedding for approximately 2,000 infections to develop a Bayesian joint model for the within-host viral kinetics of SARS-CoV-2 infection. Modeling the joint distribution allows us to infer the trajectory of infectious virus shedding — the most direct correlate of infectiousness — for individuals who contribute only PCR data, and to compute derived quantities that are inaccessible from any single proxy alone. These include the population-level probability and expected duration of ongoing infectiousness as a function of time since diagnosis, stratified by variant, vaccination status, and infection history; the residual risk of releasing an individual from isolation; and personalized, real-time estimates of infectiousness that are sequentially updated as new test results become available.
Source: Inferring infectiousness: a joint model of the within-host viral kinetics of SARS-CoV-2