[1]余锦芬,宋玉凯△,费菲,等.基于机器学习和动力学模型的湖北省新型冠状病毒肺炎疫情分析[J].生物医学工程研究,2020,03:294-299.
YU Jinfen,SONG Yukai,FEI Fei,et al.Analysis of COVID-19 in Hubei province based on machine learning and dynamics model[J].Journal of Biomedical Engineering Research,2020,03:294-299.
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基于机器学习和动力学模型的湖北省新型冠状病毒肺炎疫情分析(PDF)
《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]
- 期数:
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2020年03期
- 页码:
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294-299
- 栏目:
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- 出版日期:
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2020-09-25
文章信息/Info
- Title:
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Analysis of COVID-19 in Hubei province based on machine learning and dynamics model
- 文章编号:
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1672-6278(2020)03-294-06
- 作者:
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余锦芬1; 宋玉凯2△; 费菲1; 孙卫强1; 宋祖峰3
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1.华中科技大学同济医学院附属湖北妇幼保健院,武汉 430070;2.电子科技大学格拉斯哥学院,成都 611731;3.中冶南方工程技术有限公司,武汉 430223
- Author(s):
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YU Jinfen1; SONG Yukai2; FEI Fei1; SUN Weiqiang1; SONG Zufeng3
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1.Maternal and Child Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology,Wuhan 430070,China;2.Glasgow College,UESTC,Chengdu 611731,China;3. WISDRI Engineering & Research Incorporation Limited,Wuhan 430223
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- 关键词:
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机器学习; SIR模型; SIER模型; 元胞自动机模型; 新型冠状病毒肺炎
- Keywords:
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Machine learning; SIR model; SIER model; Cellular automata model; COVID-19
- 分类号:
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R318
- DOI:
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10.19529/j.cnki.1672-6278.2020.03.13
- 文献标识码:
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A
- 摘要:
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基于武昌方舱医院的一线数据和其他官方数据,我们首先利用机器学习对疫情规模进行预测,之后利用回溯传播模型和抽样检测模型对疫情规模进行估计,最后从宏观角度上用SIR和SIER模型结合MATLAB软件编程对疫情进行分析,采用元胞自动机模型在微观角度上的分析,从两个角度证明了中国政府强有力的防控措施的必要性。
- Abstract:
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Based on the data of the Wuchang mobile cabin hospital and other official data, we firstly used machine learning to predict the scale of the outbreak, and then adapted the trace transmission model and the sampling detection model to estimate the scale of the outbreak. Finally, SIR and SIER models were used in combination with MATLAB software programming to analyze the epidemic situation from the macro perspective, the cellular automata model was used in the micro perspective for simulation.It proves the necessity of the Chinese government′s strong prevention and control measures from two perspectives.
备注/Memo
- 备注/Memo:
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(收稿日期:2020-03-20)△通信作者Email: 2357612S@student.gla.ac.uk
更新日期/Last Update:
2020-10-16