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Estimating the impact of vaccination and long-acting monoclonal antibodies for RSV epidemics across Hong Kong, Beijing, and Thailand: a modelling study

Background Respiratory syncytial virus (RSV) is a major cause of acute lower respiratory infections worldwide, with high morbidity among infants and older adults. Recent approvals of long-acting monoclonal antibodies (mAbs) and vaccines offer new prevention opportunities, but their impact in Asian settings remains uncertain. Methods We developed age-structured SEIR transmission models for Hong Kong, Beijing, and Thailand, calibrated to region-specific surveillance and seroprevalence data from 2014-2019. Using Bayesian inference, we estimated RSV transmission dynamics and simulated intervention scenarios involving long-acting mAbs for high-risk infants, maternal vaccination, and older adult vaccination. Findings RSV showed distinct seasonality: year-round in Hong Kong, winter peaks in Beijing, and rainy-season peaks in Thailand. Estimated annual infection attack rates among infants <1 year were 51.1% in Hong Kong and 22.5% in Beijing, and 75.8% and 70.1% among children aged 1-4 years, compared with 27.9% among 0-4 years in Thailand. Simulations suggest long-acting mAbs and maternal vaccination (coverage 38.5%) could avert 15.6-19.5% and 18.0-25.2% of severe infant outcomes, respectively. Vaccination of older adults (coverage 30-40%) reduced RSV-associated outcomes by 21.7-27.7% in Hong Kong, 33.9-39.7% in Beijing and 34.8-49.8% in Thailand. Combined interventions achieved reductions of 39.2% (27.5-55.8), 49.4% (42.0-59.7), and 53.8% (44.1-64.8) in severe outcomes among infants <1 year, 19.3% (18.9-21.7), 27.7% (26.9-31.5) and 31.5% (31.3-33.9) among 60-74 years, 26.5% (26.2-28.7), 52.3% (51.5-54.8) and 37.8% (37.6-40.0) among 75 years or above, in Hong Kong, Beijing, and Thailand, respectively. Interpretation Our modelling framework provides a novel approach to evaluate RSV prevention strategies in Asian populations with diverse seasonality. As real-world effectiveness data emerge, future research should refine estimates and optimise intervention combinations for maximum public health impact.

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