新冠肺炎疫情预测分析  

Prediction and Analysis of New Coronavirus Epidemic Situation

作　　者：林德双 金秀玲[1] 刘文鑫 黄正鹏 黄怡宣 Lin Deshuang;Jin Xiuling;Liu Wenxin;Huang Zhengpeng;Huang Yixuan(College of Mathematics and Data Science,Minjiang University,Fuzhou,Fujian 350108,China)

机构地区：[1]闽江学院数学与数据科学学院,福建福州350108

出　　处：《黑龙江工业学院学报（综合版）》2020年第9期114-119,共6页Journal of Heilongjiang University of Technology(Comprehensive Edition)

基　　金：2019年闽江学院校长基金项目(项目编号:103952019030);闽江学院2018年教改项目(项目编号:MJU2018B039)。

摘　　要：2019年底新冠肺炎(COVID-19)疫情暴发,对各行各业都带来了巨大的影响。对抗疫效果的分析和疫情走势预测是很有意义的事情。利用实时更新的全球COVID-19疫情数据,基于K-means聚类将世界各国疫情状况归为6大类,结合时事研究了这6大类国家的抗疫状况,并建立ARIMA模型对中国疫情发展状况进行了预测分析,预测精度达到99%。最后总结结果并提出相关建议,对疫情防控的指导有较高的理论价值。At the end of 2019,the outbreak of the Black Swan COVID-19 has had a huge impact on all walks of life.The analysis of anti-epidemic effect and the forecast of epidemic trend are of great significance.Based on real-time global COVID-19 epidemic data and K-means clustering,the epidemic situation of each country in the world was classified into 6 categories.The ARIMA model is established to forecast the development of epidemic situation in China,and the forecast precision is 99%.At last,we summarize the results and put forward some relevant suggestions,which are of great theoretical value for the guidance of epidemic prevention and control.

关 键 词：COVID-19 K-MEANS聚类 ARIMA模型 

分 类 号：O212[理学—概率论与数理统计]