Evaluating age-dependent transmission and vaccination policy in Singapore's SARS-CoV-2 epidemic: A computational modelling approach.

The SARS-CoV-2 pandemic in Singapore revealed pronounced age-specific differences in disease transmission and severity. To investigate these dynamics, we developed a semi-mechanistic Bayesian hierarchical model that jointly estimated SARS-CoV-2 transmission across seven age groups from 2021 to 2023. Our model integrates both case and hospitalization data, while accounting for vaccination rollouts and time-varying contact patterns between age groups. Using a non-parametric approach, we estimated changes in contact behaviour directly from the epidemiological data, avoiding assumptions about their functional form. The effective reproduction number (Rt,1) for children aged 0-14 fluctuated around one before October 2021 followed by a fluctuation with a mean smaller than one thereafter. Rt,a values for 2≤a≤6 fluctuated around one while Rt,7 fluctuated around one before September 2021 followed by a fluctuation with a mean of 1.5 thereafter. The model closely matched empirical trends, with posterior estimates and 95% credible intervals aligning well with observed case and hospitalization data across all age groups. These findings underscore the critical role of age-specific contact behaviour and highlight the importance of targeted interventions during evolving pandemic phases.