Speaker
Description
Differential contributions to transmission across age groups have been reported for many respiratory infections, including SARS-CoV-2. They are crucial for estimating the impact of age-specific interventions, but disentangling these age-dependent contributions remains challenging.
We developed a Bayesian method to jointly estimate age-specific per-contact infectiousness and susceptibility by combining contact data with self-reported transmission pair data (who-infected-whom). We applied this approach to 197,840 household transmission pairs collected in the Netherlands during the COVID-19 pandemic.
Both infectiousness and susceptibility to SARS-CoV-2 infection were lowest in children aged 0-9 years and highest in adults over 30 years old. Using these estimates, we projected the expected impact of school closure and work-from-home measures during the early stages of an epidemic in the absence of other interventions. Results differed widely when age-specific susceptibility and infectiousness were or were not taken into account (8% vs 29% for school closure, 41% vs 20% for working-from-home policies).
Our method, which combines transmission pair and contact data, enables robust estimation of age-specific infectiousness and susceptibility. Accounting for age heterogeneity in these parameters is essential for projecting the impact of age-targeted interventions. Our approach is adaptable to other respiratory infections and can guide more tailored public health responses.
