Analyses of epidemic characteristics and spatiotemporal clustering of influenza in Beihai， Guangxi from 2014 to 2023
10.19428/j.cnki.sjpm.2025.24662
- VernacularTitle:2014—2023年广西北海市流行性感冒流行特征及时空聚集性分析
- Author:
Jiaping YE
1
;
Wen ZHOU
1
;
Silu LUO
2
;
Daru YE
1
;
Fengzhuo SU
2
Author Information
1. Beihai People's Hospital, Beihai, Guangxi 536000, China
2. Beihai Municipal Center for Disease Control and Prevention (Beihai Health Supervision Institute), Beihai, Guangxi 536000, China
- Publication Type:Journal Article
- Keywords:
influenza;
epidemic characteristic;
influenza virus;
spatiotemporal analysis;
cluster
- From:
Shanghai Journal of Preventive Medicine
2025;37(4):306-312
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo analyze the epidemiological characteristics and the pathogen dynamics of influenza in Beihai over the past ten years, and to provide a scientific basis for formulating targeted prevention and control measures. MethodsData for influenza cases, pathogen surveillance, and population statistics from 2014‒2023 were obtained from China Center for Disease Control and Prevention Information System. The analyses of epidemiological characteristics and spatiotemporal clustering of influenza were performed using Joinpoint 5.0, DeoDa 1.22 and SaTScan 10.1.3 software. ResultsThe average annual incidence of influenza was 254.53/100 000 in Beihai from 2014 to 2023,displaying an overall upward trend (APC=81.49%， P<0.001). Spatial autocorrelation analysis and spatiotemporal scanning results indicated that the main cluster of influenza cases was mainly located in Haicheng District. Spatiotemporal scanning results revealed that the peak periods of influenza onset were primarily from December to January of the following year, followed by June and March to April. The highest incidence of influenza was observed in 5‒9 years old group (1 289.66/100 000), followed by 0‒4 years old group (991.72/100 000). The majority of cases were students (44.24%), followed by kindergarten children (26.08%). The highest positive subtype proportion of influenza was A/H3N2 (42.01%), followed by A/H1N1 (24.96%) and B/Victoria (23.50%), while the lowest positive subtype proportion of influenza was B/Yamagata (9.33%). The risk of influenza virus positivity in winter, spring and summer was 4.70 times, 4.74 times, and 2.14 times, respectively, compared to that for autumn, with statistically significant differences (P<0.001). The dominant subtypes were A/H1N1 and A/H3N2 in 2019, 2022 and 2023. ConclusionThe overall incidence of influenza showed an upward trend in Beihai from 2014 to 2023. Continuous enhancement of influenza surveillance is necessary. It is suggested that relevant departments should carry out targeted publicity work to promote knowledge on influenza prevention and control and conduct influenza vaccination for populations in key places, such as schools and childcare institutions to prevent influenza outbreaks and epidemics of influenza.
