| 引用本文: | 师亚勇,年福忠,刘金朔,曹军.COVID–19在高危人群动态网络中的传播动力学[J].控制理论与应用,2020,37(3):461~468.[点击复制] |
| SHI Ya-yong,NIAN Fu-zhong,LIU Jin-shuo,CAO Jun.Propagation dynamics of COVID–19 in high-risk population dynamic network[J].Control Theory and Technology,2020,37(3):461~468.[点击复制] |
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| COVID–19在高危人群动态网络中的传播动力学 |
| Propagation dynamics of COVID–19 in high-risk population dynamic network |
| 摘要点击 3391 全文点击 897 投稿时间：2020-02-13 修订日期：2020-04-01 |
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| DOI编号 10.7641/CTA.2020.00072 |
| 2020,37(3):461-468 |
| 中文关键词 高危人群 动态网络 新型冠状病毒肺炎 疫情生长指数 疾病控制；计算机模拟 |
| 英文关键词 high-risk population, dynamic network, COVID-19, epidemic-growth index, disease control, computer simulation |
| 基金项目 国家自然科学基金,甘肃省国际科技合作项目,陇原青年创新人才扶持计划,兰州理工大学博士基金项目 |
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| 中文摘要 |
| 为了弄清影响新型冠状病毒肺炎蔓延的影响因素，分析中国大陆地区的疫情发展趋势，本文提出了一种基于高危人群动态网络的COVID-19传播模型来模拟新型冠状病毒肺炎在人群中的传播过程。首先，本文统计了1月16日至2月6日，中国大陆各个地区的感染情况，通过各个地区的人口流动、地理位置和经济发展情况等因素，对各个地区的疫情数据进行了综合分析，提出了疫情生长指数来量化各个地区的疫情发展情况。然后，本文结合新型冠状病毒肺炎在人群中的传播特点，以高危人群为研究对象，构建了高危人群动态网络。COVID-19传播模型对SEIR模型中的感染率、潜伏率和退出率进行了重新定义，并基于高危人群动态网络对中国大陆地区的疫情发展趋势做出了预测和分析，模拟数据和已公布的确诊数据能够较好的拟合，也验证了模型的可靠性。最后，模型还验证了防护措施的有效性。 |
| 英文摘要 |
| In order to understand the influencing factors affecting the spread of new coronavirus pneumonia and analyze the development trend of the epidemic situation in mainland China, a COVID-19 transmission model based on the dynamic network of high-risk populations to simulate the spread of new coronavirus pneumonia in the population is proposed in this paper. First of all, this paper counts the infections in various regions of mainland China from January 16 to February 6, and analyzes the epidemic data of each region through factors such as population flow, geographical location, and economic development. An epidemic growth index was proposed to quantify the epidemic situation in various regions. Then, based on the propagation characteristics of COVID-19 in the population, this paper constructed the high-risk population dynamic network by taking the high-risk population as the research object，The COVID-19 propagation model redefines the infection rate, latent rate, and withdrawal rate in the SEIR model. Based on the high-risk population dynamic network, it predicts and analyzes the development trend of the epidemic situation in mainland China. The simulation data and the published confirmed data could fit well, which also verified the reliability of the model. Finally, the model also verifies the effectiveness of protective measures. |
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