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

ABSTRACT 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. Results indicate that shifts in social behaviour—particularly among younger populations—significantly influenced transmission dynamics. The effective reproduction number ( R t ) for children aged 0–14 remained consistently above one, suggesting sustained transmission in this group, while reproduction number ( R t ) values for other age groups fluctuated around one with slight upward trends. 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.