The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis
Abstract Background Although Seasonal Influenza Vaccination (SIV) is a crucial preventive measure, achieving sufficient coverage to completely control influenza epidemics poses a significant challenge. This study aims to evaluate optimal strategies for SIV to prevent high-intensity level of influenz...
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2025-07-01
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| Online Access: | https://doi.org/10.1186/s12889-025-23648-6 |
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| author | Mengsha Chen Mengya Yang Rui Yan Zhengwei Liu Can Chen Rongrong Qu Wenkai Zhou Jiaxing Qi Kexin Cao Jiani Miao Xiaoyue Wu Jiaxin Chen Qianqian Feng HuiHui Zhang Anqi Dai Yi Yang Jingtong Zhou Qin Chen Jimin Sun Shigui Yang |
| author_facet | Mengsha Chen Mengya Yang Rui Yan Zhengwei Liu Can Chen Rongrong Qu Wenkai Zhou Jiaxing Qi Kexin Cao Jiani Miao Xiaoyue Wu Jiaxin Chen Qianqian Feng HuiHui Zhang Anqi Dai Yi Yang Jingtong Zhou Qin Chen Jimin Sun Shigui Yang |
| author_sort | Mengsha Chen |
| collection | DOAJ |
| description | Abstract Background Although Seasonal Influenza Vaccination (SIV) is a crucial preventive measure, achieving sufficient coverage to completely control influenza epidemics poses a significant challenge. This study aims to evaluate optimal strategies for SIV to prevent high-intensity level of influenza epidemics in Zhejiang Province, China. High-intensity outbreaks were defined as weekly incidence rates above 72.2 per 100,000. Methods This study estimated the incidence of influenza from 2018 to 2023 in Zhejiang Province, China, using influenza weekly surveillance data. We developed a Susceptible-Vaccinated-Infectious-Recovered-Susceptible (SVIRS) model to simulate influenza transmission and used a decision tree to assess seven vaccination strategies aimed at preventing high-intensity influenza outbreaks. These strategies differed in vaccine coverage across the three age groups: 0–14 years, 15–59 years, and 60 + years, despite having the same overall vaccination coverage. Results Between 2018 and 2020, influenza incidence in Zhejiang Province followed typical seasonal patterns. However, during the COVID-19 pandemic, these patterns became irregular, culminating in a high-intensity influenza season in 2022–2023. Model simulations indicated that increasing population-wide vaccination coverage to 36.17% could effectively prevent outbreaks from escalating to high-intensity levels. Among various allocation strategies for additional vaccines, targeting individuals aged 0–14 years and those aged 60 years and older was found to be the most effective. This approach reduced the peak weekly incidence rate from the observed 182.83 to a predicted 42.28 per 100,000, saved 23.45 CNY per capita, and yielded a health gain of 0.0102 quality-adjusted life days (QALDs) per person. Conclusion Achieving a population-wide vaccination coverage beyond 36.17% is projected to prevent high-intensity level of influenza epidemics. Targeting the 0–14 years and 60 + years age groups proves to be the most cost-effective strategy under constrained coverage conditions. These findings highlight the importance of age-prioritized vaccination policies to optimize resource allocation and mitigate the impact of influenza outbreaks, particularly in regions with limited healthcare capacity. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-4e29541cda0144b08c7748eddb9485572025-08-20T03:42:10ZengBMCBMC Public Health1471-24582025-07-0125111010.1186/s12889-025-23648-6The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysisMengsha Chen0Mengya Yang1Rui Yan2Zhengwei Liu3Can Chen4Rongrong Qu5Wenkai Zhou6Jiaxing Qi7Kexin Cao8Jiani Miao9Xiaoyue Wu10Jiaxin Chen11Qianqian Feng12HuiHui Zhang13Anqi Dai14Yi Yang15Jingtong Zhou16Qin Chen17Jimin Sun18Shigui Yang19Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineQuzhou People’s HospitalDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineDepartment of Public HealthDepartment of Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and PreventionDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of MedicineAbstract Background Although Seasonal Influenza Vaccination (SIV) is a crucial preventive measure, achieving sufficient coverage to completely control influenza epidemics poses a significant challenge. This study aims to evaluate optimal strategies for SIV to prevent high-intensity level of influenza epidemics in Zhejiang Province, China. High-intensity outbreaks were defined as weekly incidence rates above 72.2 per 100,000. Methods This study estimated the incidence of influenza from 2018 to 2023 in Zhejiang Province, China, using influenza weekly surveillance data. We developed a Susceptible-Vaccinated-Infectious-Recovered-Susceptible (SVIRS) model to simulate influenza transmission and used a decision tree to assess seven vaccination strategies aimed at preventing high-intensity influenza outbreaks. These strategies differed in vaccine coverage across the three age groups: 0–14 years, 15–59 years, and 60 + years, despite having the same overall vaccination coverage. Results Between 2018 and 2020, influenza incidence in Zhejiang Province followed typical seasonal patterns. However, during the COVID-19 pandemic, these patterns became irregular, culminating in a high-intensity influenza season in 2022–2023. Model simulations indicated that increasing population-wide vaccination coverage to 36.17% could effectively prevent outbreaks from escalating to high-intensity levels. Among various allocation strategies for additional vaccines, targeting individuals aged 0–14 years and those aged 60 years and older was found to be the most effective. This approach reduced the peak weekly incidence rate from the observed 182.83 to a predicted 42.28 per 100,000, saved 23.45 CNY per capita, and yielded a health gain of 0.0102 quality-adjusted life days (QALDs) per person. Conclusion Achieving a population-wide vaccination coverage beyond 36.17% is projected to prevent high-intensity level of influenza epidemics. Targeting the 0–14 years and 60 + years age groups proves to be the most cost-effective strategy under constrained coverage conditions. These findings highlight the importance of age-prioritized vaccination policies to optimize resource allocation and mitigate the impact of influenza outbreaks, particularly in regions with limited healthcare capacity.https://doi.org/10.1186/s12889-025-23648-6Seasonal influenza vaccinationSusceptible-vaccinated-infectious-recovered-susceptible modelDecision treeOptimal vaccination strategy |
| spellingShingle | Mengsha Chen Mengya Yang Rui Yan Zhengwei Liu Can Chen Rongrong Qu Wenkai Zhou Jiaxing Qi Kexin Cao Jiani Miao Xiaoyue Wu Jiaxin Chen Qianqian Feng HuiHui Zhang Anqi Dai Yi Yang Jingtong Zhou Qin Chen Jimin Sun Shigui Yang The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis BMC Public Health Seasonal influenza vaccination Susceptible-vaccinated-infectious-recovered-susceptible model Decision tree Optimal vaccination strategy |
| title | The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis |
| title_full | The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis |
| title_fullStr | The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis |
| title_full_unstemmed | The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis |
| title_short | The optimal strategy for seasonal influenza vaccination to prevent high-intensity level of influenza epidemics in Zhejiang, China: an integrated transmission-dynamic and health-economic modeling analysis |
| title_sort | optimal strategy for seasonal influenza vaccination to prevent high intensity level of influenza epidemics in zhejiang china an integrated transmission dynamic and health economic modeling analysis |
| topic | Seasonal influenza vaccination Susceptible-vaccinated-infectious-recovered-susceptible model Decision tree Optimal vaccination strategy |
| url | https://doi.org/10.1186/s12889-025-23648-6 |
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