时间序列在呼吸道传染病研究中的可视化分析

Visual analysis of time series in the study of respiratory infectious diseases

目的探索时间序列在国内外呼吸道传染病领域的发展趋势,为选择时间序列模型预测呼吸道传染病的发病情况提供参考依据。方法采用CiteSpace、Ucinet软件,对中国知网、万方、维普和Web of Science核心合集收录建库以来至2022年12月31日的939篇文献进行可视化分析。结果检索到中文文献283篇,英文656篇。时间序列在呼吸道传染病研究应用中,国内外总体呈现运动式波动上升的趋势。发文量最多的是2022年(中文49篇、英文127篇)。国内作者和机构间的合作关系较国外不足。研究热点:国内常用ARIMA模型短期预测呼吸道传染病的发病趋势及季节趋势;国外涉及众多领域,气象因素、季节性和空气污染等研究,主要使用分布式滞后非线性模型(DLNM)、广义相加模型(GAM)和SARIMA模型。研究前沿:国内转向空间分布、趋势分析;国外则是气象因素、空气污染等方面。结论时间序列分析在呼吸道传染病应用中发展迅速,我国相关研究仍存在一定的发展空间。未来应继续拓宽时间序列分析的深度和广度,以提高对重大疫情的预测能力。

Objective To explore the development trend of time series in respiratory infectious diseases at home and abroad,and to provide a reference for selecting a time series model to predict the incidence of respiratory infectious diseases.Methods CiteSpace and Ucinet software were used to visually analyze 939 documents from CNKI,Wanfang,VIP and Web of Science until December 31,2022.Results About 283 Chinese articles and 656 English articles were retrieved.In the research and application of respiratory infectious diseases,the trend of movement fluctuation rises was shown at home and abroad.The largest number of publications was in 2022(49 articles in Chinese and 127 articles in English).The cooperation between domestic authors and institutions was less than that in foreign countries.Research hotspots:In China,ARIMA model predicts shortterm incidence and seasonal trend of respiratory infectious diseases;On abroad,research on many fields,such as meteorological factors,seasonality and air pollution,including distributed lag nonlinear model(DLNM),generalized additive model(GAM)and SARIMA model.Research frontier:in domestic,it was diversion analysis of spatial distribution and trend;On abroad,it was meteorological factors,air pollution,etc.Conclusions Time series analysis has developed rapidly in the application of respiratory infectious diseases,and there is still some space for related research in China.In the future,the depth and breadth of time-series analysis should be further expanded to improve the predictive capacity for major outbreaks.