Multi-source agent-based modeling to optimize influenza mitigation strategies in Hong Kong

Seasonal influenza control faces challenges from variable vaccine effectiveness and uncertain non-pharmaceutical intervention (NPI) performance. While vaccination remains the primary strategy, its effectiveness varies between vaccine-matched and mismatched seasons. Increased post-COVID-19 NPI acceptance enhances influenza control feasibility, yet optimal combination approaches remain poorly understood. We use an agent-based model to analyze influenza transmission across six seasons in Hong Kong (2009-2013) with varying epidemic characteristics. We integrate surveillance, serological, and school absenteeism data for calibration, enabling accurate estimation of reported and unreported infections. We evaluate age-targeted vaccination, staying home when sick, mask use, and school-based interventions across diverse real-world scenarios. Compared to baseline, child vaccination consistently outperforms other strategies, with targeting those under 12 yielding the greatest population-level attack rate reduction (up to 8.5% relative reduction per 100,000 vaccinated). Among NPIs, 40% mask coverage reduces attack rates by 18%–43%, comparable to 25% of symptomatic individuals staying home. During vaccine-mismatched seasons, combining high-coverage mask use and staying home reduces attack rates by 79%–84%. High-coverage school-based vaccination is more effective than closures, reducing student attack rates by up to 85% versus 33% for 14-day closures. Our multi-source calibration approach provides robust evidence for prioritizing child vaccination and strategic NPI combinations. Modeling six influenza seasons to evaluate mitigation strategies, this study proposes a signal-responsive policy framework. Findings show child vaccination and mask use curb transmission; their combination is vital during vaccine mismatches.