|本期目录/Table of Contents|

[1]王志心,刘治△,刘兆军.基于机器学习的新型冠状病毒（COVID-19）疫情分析及预测*[J].生物医学工程研究,2020,01:1-5.
　WANG Zhixin,LIU Zhi,LIU Zhaojun.COVID-19 analysis and forecast based on machine learning[J].Journal of Biomedical Engineering Research,2020,01:1-5.
点击复制

基于机器学习的新型冠状病毒（COVID-19）疫情分析及预测*(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2020年01期
页码:
1-5
栏目:
出版日期:
2020-03-25

文章信息/Info

Title:
COVID-19 analysis and forecast based on machine learning
文章编号:
16726278 (2020)01-0001-05
作者:
王志心; 刘治△; 刘兆军
山东大学信息科学与工程学院，青岛 266237
Author(s):
WANG Zhixin ;  LIU Zhi ;  LIU Zhaojun
School of Information Science and Engineering, Shandong University, Tsingtao 266237,China
关键词:
新型冠状病毒肺炎; 传播模型; 疫情拐点; 最小二乘准则; 梯度下降; 确诊人数预测
Keywords:
COVID-19 pneumonia;  Propagation model;  Inflection point; Least square error principle; Gradient descent; Prediction of diagnosed number
分类号:
R318
DOI:
10.19529/j.cnki.1672-6278.2020.01.01
文献标识码:
A
摘要:
本研究采用数学建模的方式，在有限的数据下，通过机器学习对近期爆发的新型冠状病毒(COVID-19)肺炎确诊人数趋势进行了预测，根据有关部门发布的信息，预测了疫情拐点出现的时间，并对比了各省预计最终确诊人数所占的比例，以此为依据，大致划分了疫情的严重程度，对各省市人民防护工作有指导意义。
Abstract:
We used mathematical modeling to predict the trend of the number of newly diagnosed pneumonia outbreaks caused by COVID-19 with limited data through machine learning, and compared the proportion of estimated final diagnoses in each province. Based on that, the epidemic situation was roughly divided. The degree of severity could also be a guiding significance for people′s self-protection work in various provinces and cities.

参考文献/References

备注/Memo

备注/Memo:
（收稿日期：2020-02-11）山东省自然科学基金重大基础研究资助项目(ZR2019ZD05)。△通信作者Email: liuzhi@sdu.edu.cn;zhaojunliu@sdu.edu.cn
更新日期/Last Update: 2020-04-14