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The viral load of patients infected with SARS-CoV-2 varies on logarithmicscales and possibly with age. Controversial claims have been made in theliterature regarding whether the viral load distribution actually depends onthe age of the patients. Such a dependence would have implications for theCOVID-19 spreading mechanism, the age-dependent immune system reaction, andthus for policymaking. We hereby develop a method to analyze viral-loaddistribution data as a function of the patients' age within a flexible,non-parametric, hierarchical, Bayesian, and causal model. The causal nature ofthe 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 orsystematic errors in the measurement of the viral load. We perform these testsby calculating the Bayesian evidence for each implied possible causaldirection. The possibility of testing for bias in data collection andidentifying causal directions can be very useful in other contexts as well. Forthis reason we make our model freely available. When applied to publiclyavailable age and SARS-CoV-2 viral load data, we find a statisticallysignificant increase in the viral load with age, but only for one of the twoanalyzed datasets. If we consider this dataset, and based on the currentunderstanding of viral load's impact on patients' infectivity, we expect anon-negligible difference in the infectivity of different age groups. Thisdifference is nonetheless too small to justify considering any age group asnoninfectious.

Causal, Bayesian, & Non-parametric Modeling of the SARS-CoV-2 Viral Load Distribution vs. Patient's Age