Max Planck Institute for Astrophysics, Garching, Germany.
Fakultät für Physik, Ludwig-Maximilians-Universität München, Munich, Germany.
PLoS One. 2022 Oct 5;17(10):e0275011. doi: 10.1371/journal.pone.0275011. eCollection 2022.
The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients' age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load's impact on patients' infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.
感染 SARS-CoV-2 的患者的病毒载量在对数尺度上有所不同，并且可能与年龄有关。文献中存在争议，即病毒载量分布是否实际上取决于患者的年龄。这种依赖性将对 COVID-19 的传播机制、年龄相关的免疫系统反应以及因此对决策产生影响。我们在此开发了一种方法，以便在灵活、非参数、分层、贝叶斯和因果模型中，根据患者的年龄分析病毒载量分布数据。所开发的重建的因果性质还允许测试数据中的偏差。这可能是由于例如患者检测和数据收集的偏差，或者病毒载量测量中的系统误差。我们通过计算每个隐含可能因果方向的贝叶斯证据来执行这些测试。在数据收集和识别因果方向方面进行偏差测试的可能性在其他情况下也非常有用。出于这个原因，我们免费提供我们的模型。当应用于公开的年龄和 SARS-CoV-2 病毒载量数据时，我们发现病毒载量随年龄呈统计学显著增加，但仅在分析的两个数据集之一中如此。如果我们考虑这个数据集，并且基于对病毒载量对患者传染性的当前理解，我们预计不同年龄组之间的传染性会有显著差异。然而，这种差异太小，不足以认为任何年龄组都没有传染性。
