Evidence map›Paper›PMID 41256046›Full record

ArticleChina CDC weekly2025

Epidemiological Assessment and Optimization of School-Based Influenza Vaccination - Shenzhen City, Guangdong Province, China, 2023-2024.

Shuqi Wang, Zhigao Chen, Qi Tan, Zengyang Shao, Yushuang Chen, Fang Huang, Yanpeng Cheng, Jianxing Yu, Ting Zhang, Xin Wang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shuqi Wang *WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
Zhigao Chen *Shenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.
Qi TanCollege of Computer and Information Engineering (College of Artificial Intelligence), Nanjing Tech University, Nanjing City, Jiangsu Province, China.
Zengyang ShaoWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
Yushuang ChenWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
Fang HuangShenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.
Yanpeng ChengShenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.
Jianxing YuSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Ting ZhangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Xin WangShenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.
Xiujuan TangShenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.
Zhen ZhangShenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.
Chao GaoSchool of Artificial Intelligence, Optics, and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an City, Shaanxi Province, China.
Zhongjie LiSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Zhanwei DuSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen City, Guangdong Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is already known about this topic?: School-aged children represent a particularly vulnerable population for influenza transmission due to their dense social interactions and limited awareness of protective measures. Since 2019, Shenzhen has provided free influenza immunizations to this demographic, with vaccination campaigns typically initiated during the autumn months. What is added by this report?: This study utilized influenza surveillance data from Shenzhen to develop an age-stratified compartmental model for epidemiological simulations, evaluating the disease burden prevented by influenza vaccinations among school-aged children during the 2023-2024 season. Additionally, an optimization framework was developed to design strategic vaccination schedules while considering the importance of maintaining stable public health policies over time. What are the implications for public health practice?: The findings suggest concentrating vaccination efforts during November and December; however, optimal strategies may vary depending on specific influenza transmission patterns. A more robust approach involves implementing a generalized strategy optimized using historical seasonal data with comparable transmission characteristics.

Indexed as

Epidemiological simulationInfluenza vaccinationSchool-aged childrenVaccination schedule optimization

Identifiers

PMID41256046
PMCPMC12620573

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.