Evidence map›Paper›PMID 41369134›Full record

ArticleAnnals of medicine2025

Comparative analysis of influenza epidemiological characteristics in Quzhou, China from 2016 to 2024.

Hui Yang, Feipeng Chen, Bingdong Zhan, Shuangqing Wang, Zhao Yu, Zhidan Hua

Abstract readComparative Study
In one paragraph

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

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

6 authors.

Hui YangDepartment of Infectious Disease Control and Prevention, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang Province, China.
Feipeng ChenDepartment of Infectious Disease Control and Prevention, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang Province, China.
Bingdong ZhanDepartment of Infectious Disease Control and Prevention, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang Province, China.
Shuangqing WangDepartment of Infectious Disease Control and Prevention, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang Province, China.
Zhao YuDepartment of Infectious Disease Control and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
Zhidan HuaDepartment of Pulmonary and Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study analyzed influenza-like illness (ILI) and influenza-positive rate (IPR) in Quzhou from 2016 to 2024 to assess the impact of the COVID-19 pandemic.

methodsThe weekly surveillance data of ILI were collected from sentinel hospitals and network laboratories. Real-time reverse transcription-polymerase chain reaction (RT-PCR) was used to detect and identify the specific types of influenza viruses.

resultsA total of 77,912 ILI cases were reported (ILI% = 3.9%). Both ILI% and IPR showed seasonal winter-spring peaks. A marked decline in ILI% (4.7% to 2.7%) and IPR (19.2% to 10.7%) was observed during the COVID-19 pandemic period (2020-2022), followed by a post-pandemic rebound. Children under 15 years old accounted for the highest proportion of ILI cases (65.9%). The 5-14 age group had the highest IPR (33.6%). Influenza A(H3N2) (39.2%) and B/Victoria (36.0%) were the predominant strains. The correlation between ILI% and IPR varied significantly across COVID-19 pandemic phases: weak positive pre-pandemic (

conclusionThe COVID-19 pandemic has significantly influenced influenza activity and altered the relationship between ILI% and IPR. The inconsistent correlation between ILI and IPR, especially during the COVID-19 pandemic, highlights the necessity of integrated virological surveillance and appropriate definition of ILI for effective influenza monitoring and public health response.

Indexed as

COVID-19Influenza, HumanAdolescentAdultAgedChildChild, PreschoolChinaFemaleHumansInfantInfant, NewbornMaleMiddle AgedPandemicsSARS-CoV-2COVID-19epidemiological characteristicsInfluenza-like illnesstrend analysisvirology

Identifiers

PMID41369134
PMCPMC12697262

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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.