tetano
Editor, Senior Moderator
Nat Commun
. 2026 Aug 25;17(1):10154.
doi: 10.1038/s41467-026-74857-5.
Liping Peng 1 , Yiyang Guo 1 , Nicole N Y Tsang 1 , Xiaotong Huang 1 , Hau Chi So 1 , Benjamin J Cowling 1 , Dennis K M Ip # 2 , Tim K Tsang # 3
Affiliations
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.
. 2026 Aug 25;17(1):10154.
doi: 10.1038/s41467-026-74857-5.
Multi-source agent-based modeling to optimize influenza mitigation strategies in Hong Kong
Liping Peng 1 , Yiyang Guo 1 , Nicole N Y Tsang 1 , Xiaotong Huang 1 , Hau Chi So 1 , Benjamin J Cowling 1 , Dennis K M Ip # 2 , Tim K Tsang # 3
Affiliations
- PMID: 42778535
- PMCID: PMC13601595
- DOI: 10.1038/s41467-026-74857-5
Abstract
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.