Shuqi Wang, Zhigao Chen, Qi Tan, Zengyang Shao, Yu. Preplanned Studies: Epidemiological Assessment and Optimization of School-Based Influenza Vaccination - Shenzhen City, Guangdong Province, China, 2023~2024. China CDC Weekly, 2025, 7(44): 1383-1388
Introduction: School-aged children are primary vectors for influenza transmission through their frequent close contact in educational settings and developing immune awareness. Since 2019, the Shenzhen municipal government has implemented annual, free, influenza vaccination programs targeting eligible primary and secondary school students. However, evidence-based strategies specifically tailored to this demographic remain insufficient.
Methods: This study analyzed weekly influenza-like illness (ILI) surveillance data and laboratory-confirmed positivity rates from Shenzhen during the 2023–2024 season. It developed an age-stratified Susceptible–Exposed–Symptomatic–Asymptomatic–Recovered–Hospitalized–Deceased–Vaccinated compartmental model integrated with the Ensemble Adjustment Kalman Filter (EAKF) algorithm to estimate historical transmission parameters and quantify vaccination impact. The Upper Confidence Bound applied to Trees (UCT) algorithm was used to optimize the vaccination schedule and evaluate multiple strategic scenarios comparatively.
Results: Compared to a no-vaccination scenario, the current government strategy prevented approximately 1,285,925 [95% confidence interval (CI): 1,240,671–1,331,180] symptomatic infections and 56,956 (95% CI: 55,118–58,793) hospitalizations. Under identical vaccine supply conditions, the optimized strategy recommends vaccinating 30%, 25%, and 5% of school-aged children in November, December, and January, respectively. This optimized approach would avert approximately 1,469,368 (95% CI: 1,392,734–1,546,002) symptomatic infections and 64,442 (95% CI: 61,269–67,615) hospitalizations — representing 14.3% and 13.1% improvements over the government strategy, respectively. Additionally, a generic strategy developed using 2017–2019 data performed well during 2023–2024, demonstrating cross-seasonal adaptability.
Conclusions: Concentrating influenza vaccination efforts among school-enrolled children during November and December significantly reduces disease burden and represents a critical strategy for controlling influenza transmission.
Methods: This study analyzed weekly influenza-like illness (ILI) surveillance data and laboratory-confirmed positivity rates from Shenzhen during the 2023–2024 season. It developed an age-stratified Susceptible–Exposed–Symptomatic–Asymptomatic–Recovered–Hospitalized–Deceased–Vaccinated compartmental model integrated with the Ensemble Adjustment Kalman Filter (EAKF) algorithm to estimate historical transmission parameters and quantify vaccination impact. The Upper Confidence Bound applied to Trees (UCT) algorithm was used to optimize the vaccination schedule and evaluate multiple strategic scenarios comparatively.
Results: Compared to a no-vaccination scenario, the current government strategy prevented approximately 1,285,925 [95% confidence interval (CI): 1,240,671–1,331,180] symptomatic infections and 56,956 (95% CI: 55,118–58,793) hospitalizations. Under identical vaccine supply conditions, the optimized strategy recommends vaccinating 30%, 25%, and 5% of school-aged children in November, December, and January, respectively. This optimized approach would avert approximately 1,469,368 (95% CI: 1,392,734–1,546,002) symptomatic infections and 64,442 (95% CI: 61,269–67,615) hospitalizations — representing 14.3% and 13.1% improvements over the government strategy, respectively. Additionally, a generic strategy developed using 2017–2019 data performed well during 2023–2024, demonstrating cross-seasonal adaptability.
Conclusions: Concentrating influenza vaccination efforts among school-enrolled children during November and December significantly reduces disease burden and represents a critical strategy for controlling influenza transmission.
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