Visual Analysis of Research Hotspots and Trends in the Field of Influenza Monitoring in China Based on CNKI from 1978 to 2024
OBJECTIVE To perform bibliometric and visual analysis for influenza monitoring work in China,Citespace software were utilized to offer comments and suggestions for the following days.METHODS Influenza monitoring-themed literature from 1st January 1952 to 20th Marth 2024 was retrieved from CNKI database.Publication numbers,keywords,clusters,affiliations,and authorship were analyzed and visualized subsequently.RESULTS 2 048 articles were encompassed after duplicated and selected manually.The number of published articles demonstrated a consistent annual increase from 1978 to 2019,followed by a sharply downturn from 2019 to 2023.Timeline analysis indicated a notable surge in the domains of influenza etiology,epidemiology,and immunology 2000.After 2010,there emerged a pronounced interest in molecular detection methods,prediction modeling,and population vaccinated strategies.After 2020,research hotspots underwent a further refinement,with the emergence of the keyword"machine learning"denoting an evolving research trend.Keyword burstiness showed that the latest research frontiers were epidemic characteristics,incidence rate and outbreaks.The presence of terms"safety"and"risk assessment"unveiled that quantitative evaluations of the impact of influenza on public health expenditure have been undertaken.Collaboration among institutions was constrained,with sole collaborative endeavors orchestrated by the Chinese Center for Disease Control and Prevention,engaging specific provincial-level entities.Predominantly,prolific authorship was attributed to Meng Ling and Tu Wenxiao.CONCLUSION The successful establishment of Chinese influenza monitoring network had significantly advanced influenza monitoring research in China.Nowadays,the key areas of influenza monitoring encompass pathogen,epidemiology,population immunity,and clinical drug resistance and treatment.In the future,it is imperative to bridge diverse systems,enhance interdisciplinary and interdepartmental collaboration to propel disease prevention and control forward.
NETL
NSTL
万方数据
