Speaker
Description
Respiratory syncytial virus (RSV) is a leading cause of hospitalization among infants. An infant’s birth month relative to the RSV season is an important determinant of hospitalization risk, with infants born shortly before peak seasonal activity often experiencing the highest burden. Although existing epidemiological studies largely describe these patterns, mechanistic evaluation of how birth timing interacts with transmission dynamics and emerging immunization strategies remains limited.
Recent epidemiological observations suggest shifts in RSV seasonality, following the COVID-19 pandemic, with early seasonal onset in several European countries. Concurrently, Germany has introduced extended half-life monoclonal antibodies to protect newborns against severe RSV, though their effectiveness may depend on birth timing and epidemic variability.
We use the German Epidemic Microsimulation System (GEMS), an agent-based modelling framework, to simulate RSV transmission among infants while explicitly representing birth month cohorts, age and contact dependent susceptibility, waning immunity, and seasonal transmission dynamics. We simulate exposure trajectories and evaluate weekly RSV hospitalizations under varying monoclonal antibody coverage and epidemic timing scenarios.
We quantify how birth month and seasonal dynamics influence the effectiveness and timing of monoclonal antibody interventions and provide mechanistic insight into observed seasonal risk patterns.
