[1]徐韧哲,汪家伟,叶声豪,等.基于SIQR模型的新冠肺炎期间深圳市防控措施分析[J].深圳大学学报理工版,2020,37(3):257-264.[doi:10.3724/SP.J.1249.2020.03257]
 XU Renzhe,WANG Jiawei,YE Shenghao,et al.Analysis of prevention measures in Shenzhen based on SIQR model during the novel coronavirus pneumonia[J].Journal of Shenzhen University Science and Engineering,2020,37(3):257-264.[doi:10.3724/SP.J.1249.2020.03257]
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基于SIQR模型的新冠肺炎期间深圳市防控措施分析()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第37卷
期数:
2020年第3期
页码:
257-264
栏目:
数学与应用数学
出版日期:
2020-05-20

文章信息/Info

Title:
Analysis of prevention measures in Shenzhen based on SIQR model during the novel coronavirus pneumonia
文章编号:
202003007
作者:
徐韧哲1汪家伟2叶声豪1王雄1
1) 深圳大学高等研究院,广东深圳518060
2)安徽农业大学信息与计算机学院,安徽合肥230036
Author(s):
XU Renzhe1 WANG Jiawei2 YE Shenghao1 and WANG Xiong1
1) Institute for Advanced Study, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
2) School of Information and Computer, Anhui Agricultural University, Hefei 230036, Anhui Province, P.R.China
关键词:
应用数学传播动力学定量分析新型冠状病毒防疫全国迁徙数据
Keywords:
applied mathematics transmission dynamics quantitative analysis novel coronavirus epidemic prevention national population mobility data
分类号:
O29
DOI:
10.3724/SP.J.1249.2020.03257
文献标志码:
A
摘要:
自新型冠状病毒肺炎爆发以来,以中国深圳市为代表的输入型地区疫情随着各项防控措施的出台很快趋于稳定.通过传播动力学SIQR(susceptible infectious quarantined recovered)模型模拟深圳市真实疫情发展,并对各项防控措施的力度进行打分,对不同措施得分下的疫情发展进行模拟.结果表明,若武汉推迟“封城”15 d,深圳的染病人数将增长69.5倍.当前深圳输入人口管控为80分、个人防疫措施为92分、城市内部人口流动管控为84分.与全球其他地区相比,深圳市全方位和大力度的疫情防控有效避免了疫情在当地的爆发.当放松人口流动管控至40分时,若个人防疫力度为60分,感染人数会增至约900人;但若个人防疫力度为20分,感染人数会增至4百万人以上.因此,在疫情期间适当放松人口流动管控时,应时刻保持高水平的个人防疫力度.
Abstract:
The epidemic situation in the novel coronavirus input areas represented by Shenzhen has quickly stabilized under various prevention and control measures, since the novel coronavirus outbreak in China. In this paper, we use the SIQR (susceptible infection quarantined recovered) model of transmission dynamics to simulate the real epidemic development of Shenzhen, and score the strength of various control measures, simulate the epidemic development under different measures scores. Our results show that if the lockdown of Wuhan had been delayed for 15 days, the number of people infected in Shenzhen would increase by 69.5 times. The scores of input population control, personal epidemic prevention measures, and urban internal population flow control are 80, 92 and 84 points, respectively. Compared with other regions of the world, the all-round and vigorous epidemic prevention in Shenzhen has effectively prevented the outbreak of the epidemic in the local area. When the population flow control was relaxed to 40 points, if the personal epidemic prevention was 60 points, the number of infected people would increase to around 9 hundred, but if the personal epidemic prevention was 20 points, the number of infected people would increase to more than 4 million. Therefore, it is necessary to maintain a high level of personal protection when the population flow control is relaxed.

参考文献/References:

[1] ANDERSON R M, ANDERSON B, MAY R M. Infectious diseases of humans: dynamics and control[M]. Oxford, UK: Oxford University Press, 1992: 52-84.
[2] JONATHAN M R, JESSICA R E B, DEREK A T C, et al. Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions[EB/OL]. (2020-01-28)[2020-02-20]. https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v2.
[3] WU J T, LEUNG K, LEUNG G M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study[J]. Lancet, 2020, 395(10225): 689-697.
[4] 周涛, 刘权辉, 杨紫陌, 等. 新型冠状病毒肺炎基本再生数的初步预测[J].中国循证医学杂志, 2020, 20(3):359-364.
ZHOU Tao, LIU Quanhui, YANG Zimo, et al. Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV[J]. Chinese Journal of Evidence Based Medicine, 2020, 20(3): 359-364.(in Chinese)
[5] TANG Biao, WANG Xia, LI Qian, et al. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions[J]. Journal of Clinical Medicine, 2020, 9(2): 462-474.
[6] THOMAS G. First attempt to model the dynamics of the coronavirus outbreak 2020
[EB/OL]. (2020-02-10) [2020-02-20]. https://arxiv.org/abs/2002.03821.
[7] 曹盛力,冯沛华,时朋朋.修正SEIR传染病动力学模型应用于湖北省2019冠状病毒病(COVID-19)疫情预测和评估[J/OL].浙江大学学报医学版,
[2020-02-28]. http://www.zjujournals.com/med/CN/10.3785/j.issn.1008-9292.2020.02.05. DOI: 10.3785/j.issn.1008-9292.2020.02.05.
CAO Shengli, FENG Peihua, SHI Pengpeng. Study on the epidemic development of corona virus disease-19 (COVID-19) in Hubei province by a modified SEIR model[J/OL].Journal of Zhejiang University Medical Sciences,
[2020-02-28]. http://www.zjujournals.com/med/CN/10.3785/j.issn.1008-9292.2020.02.05. DOI: 10.3785/j.issn.1008-9292.2020.02.05.(in Chinese)
[8] 中华人民共和国国家卫生健康委员会.截至3月10日24时新型冠状病毒肺炎疫情最新情况[EB/OL].(2020-03-11)[2020-03-15] . http://www.nhc.gov.cn/xcs/xxgzbd/gzbd\_index.shtml.
National Health and Health Commission of the Peoples Republic of China.Up to 24 February 26th novel coronavirus pneumonia epidemic situation[EB/OL]. (2020-03-11)[2020-03-15]. http://www.nhc.gov.cn/xcs/xxgzbd/gzbd\_index.shtml.(in Chinese)
[9] 金启轩.中国新冠肺炎疫情预测建模与理性评估[J].统计与决策, 2020(5):11-14.
JIN Qixuan. Novel coronavirus pneumonia epidemic prediction modeling and rational evaluation in China[J]. Statistics and Decision, 2020(5):11-14.(in Chinese)
[10] 百度. 全国迁徙详情——百度地图迁徙大数据[EB/OL].(2020-03-10)[2020-03-15].http://qianxi.baidu.com/.
Baidu. National migration details: Baidu map migration big data [EB/OL].(2020-03-10)[2020-03-15].http://qianxi.baidu.com/.(in Chinese)
[11] 深圳市政府数据开放平台.深圳市新型冠状病毒感染肺炎疫情数据开放专题[EB/OL]. (2020-03-10)[2020-03-15].https://opendata.sz.gov.cn/data/epidemicDataSet/toEpidemicDataSet/epidemic/showEpidemicData.
Open Data Platform for Shenzhen Municipal Government. Novel coronavirus infection data for Shenzhen [EB/OL].(2020-03-10)[2020-03-15]. https://opendata.sz.gov.cn/data/epidemicDataSet/toEpidemicDataSet/epidemic/showEpidemicData.(in Chinese)
[12] 金安楠,李钢,王皎贝,等.深圳市新型冠状病毒肺炎(COVID-19)疫情时空演化与防控对策[J].陕西师范大学学报自然科学版, 2020,48(3):18-32.
JIN Annan, LI Gang, WANG Jiaobei, et al. Spatio-temporal evolution and control strategies of COVID-19 epidemic in Shenzhen, China[J]. Journal of Shanxi Normal University Natural Science Edition, 2020,48(3):18-32.(in Chinese)

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备注/Memo

备注/Memo:
Received:2020-02-29;Accepted:2020-04-28
Foundation:National Natural Science Foundation of China (61601306)
Corresponding author:Professor WANG Xiong.E-mail:wangxiong8686@qq.com
Citation:XU Renzhe, WANG Jiawei, YE Shenghao, et al. Analysis of prevention measures in Shenzhen based on SIQR model during the novel coronavirus pneumonia[J]. Journal of Shenzhen University Science and Engineering, 2020, 37(3): 257-264.(in Chinese)
基金项目:国家自然科学基金资助项目(61601306)
作者简介:徐韧哲(1998—),深圳大学硕士研究生.研究方向:复杂系统与数据科学. E-mail:893068674@qq.com
引文:徐韧哲,汪家伟,叶声豪,等.基于SIQR模型的新冠肺炎期间深圳市防控措施分析[J]. 深圳大学学报理工版,2020,37(3):257-264.
更新日期/Last Update: 2020-05-30