传染病传播数据分析与预测方法的研究进展——以Covid-19为例  

Research Progress of Infectious Disease Transmission Data Analysis and Prediction Methods——Through Taking Covid-19 as an Example

作　　者：杨敏华 张诗涵 吴雄周[1] 马谷阳 邓婧姝 Yang Minhua;Zhang Shihan;Wu Xiongzhou;Ma Guyang;Deng Jingshu(Business School of Jishou University,Jishou,Hunan,416000,China)

机构地区：[1]吉首大学商学院,湖南吉首416000

出　　处：《黑龙江科学》2024年第22期128-130,共3页Heilongjiang Science

基　　金：湖南省教育厅2022年度湖南省大学生创新创业训练计划项目(2022-3487)。

摘　　要：以新型冠状病毒肺炎(Covid-19)为例,对学术界在疫情传播数据分析与预测方面的研究进展进行综述,以提高传染病的防控和应急管理能力。将统计学分析(回归和拟合分析、时间序列分析及函数型数据分析)、动力学模型分析(SIR模型、SEIR模型和其他拓展模型)和LSTM神经网络模型分析的优缺点进行比较,分析结果可为未来的传染病防控管理与决策提供理论参考。Through taking Covid-19 as an example,the study reviews the research progress in the analysis and prediction of epidemic transmission data,so as to improve the prevention,control and emergency management capacity of infectious diseases.The study compares the advantages and disadvantages of statistical analysis(regression and fitting analysis,time series analysis,and functional data analysis),dynamic model analysis(SIR Model,SEIR model and other extended models)and LSTM neural network model analysis.The analysis results can provide theoretical reference for future management and decision-making of infectious disease prevention and control.

关 键 词：Covid-19 传染病防控 数据分析 预测方法 

分 类 号：R181.8[医药卫生—流行病学]