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.

翻译：在传染病暴发期间，为有关传播的政策问题提供准确答案需要建立传染性自然史的详细模型。遗憾的是，传染性的直接测量通常不可行。相反，我们常依赖间接代用指标，如通过PCR或抗原检测测定的病毒载量、检测具有复制能力病毒的病毒培养，或症状发作，这些指标分别反映了病毒动力学或宿主反应的不同方面。然而，这些代用指标在采集便利性、可扩展性及其与病毒脱落——进而与潜在传染性——的关系上存在差异。在此，我们利用五个前瞻性、密集采样队列的数据，这些队列包含约2,000例感染者的多个病毒脱落代用指标的纵向数据，以开发一个用于SARS-CoV-2感染的宿主体内病毒动力学的贝叶斯联合模型。通过模拟联合分布，我们能够推断仅提供PCR数据的个体的传染性病毒脱落的轨迹——这是传染性最直接的关联指标，并计算仅凭任何单一代用指标无法获得的衍生量。这些包括作为诊断后时间函数的、按变异株、疫苗接种状态和感染史分层的人群水平持续传染性概率和预期持续时间；解除个体隔离的残余风险；以及随着新检测结果出现而顺序更新的个性化、实时传染性估计。