基于元胞自动机的病毒传播和系统仿真模型

doi:10.16182/j.issn1004731x.joss.21-0131

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (10): 2449-2459.doi: 10.16182/j.issn1004731x.joss.21-0131

基于元胞自动机的病毒传播和系统仿真模型

张丽娟^1,2, 王福昌^1,2, 李振刚³

1.防灾科技学院,河北三河 065201;
2.中国地震局建筑物破坏机理与防御重点实验室,河北三河 065201;
3.中国社会科学院社会学研究所,北京 100732

收稿日期:2021-02-19 修回日期:2021-05-30 出版日期:2021-10-18 发布日期:2021-10-18
作者简介:张丽娟(1983-),女,硕士,副教授,研究方向为微分方程与动力系统。E-mail：Lijuan262658@126.com
基金资助:
国家自然科学基金(41674055); 中央高校基本科研专项(ZY20215155); 防灾科技学院教学教研教改项目(JY2021B22); 防灾科技学院高等数学金课建设项目(JK201912); 防灾科技学院大学生创新创业项目(S202111775086)

Virus Propagation and System Simulation Based on Cellular Automata Model

Zhang Lijuan^1,2, Wang Fuchang^1,2, Li Zhengang³

1. Institute of Disaster Prevention, Sanhe 065201, China;
2. Key Laboratory of Building Collapse Mechanism and Disaster Prevention, Sanhe 065201, China;
3. Chinese Academy of Social Sciences Institute of Sociology, Beijing 100732, China

Received:2021-02-19 Revised:2021-05-30 Online:2021-10-18 Published:2021-10-18

摘要/Abstract

摘要： 根据病毒传播特点及城市空间地图模式,建立了一类易感者,潜伏者,患病者,治愈者,免疫者,病毒传播模型,将环境中的个体看作Agent智能体,根据元胞自动机原理构建病毒传播机制。依据病毒的不同传播特点、不同防控策略对病毒的时空传播进行了仿真,讨论了关键因素对病毒扩散的影响,并以石家庄新冠疫情为例对模型进行了验证。研究结果表明：初始潜伏者人数、感染力、随机游走、疫苗接种比例等因素在病毒传播时空分布中起重要作用。仿真结果显示,在政策比较严格控制的情况下,易感人群10%的疫苗接种比率可初步筑起防疫屏障,较为有效控制疫情传播。模型能比较准确刻画疫情数据随时间的变化规律,也可以展示疫情在真实空间的分布特点,为采取有效防控策略提供决策依据。

关键词: 疫情传播, 智能体, 城市地图, 个体环境交互, 传播动力学

Abstract: A susceptible-latent-infected-cured-immune virus spreading model is established according to the characteristics of epidemic transmission and the actual urban spatial map model. Individuals in the environment are regarded as agents, and the spreading mechanism is established according to the principle of cellular. The effects of different strategies and different characteristics of virus spreading on epidemic have been studied. The role and effect of important factors on epidemic prevention and control method are discussed, and the model is validated taking Shijiazhuang epidemic as an example . The results show that the number of initial latent, the infectivity of disease, vaccination proportion are very important in the spatial and temporal distribution of epidemic transmission. Results show that 10% vaccination rate of susceptible population can build an epidemic prevention barrier and effectively control the spread of the epidemic when the prevention and control measures are relatively strict. The model can accurately describe the change rule of epidemic data over time, and it can clearly describe the spatial distribution characteristics. It is helpful to grasp the temporal and spatial characteristics of virus transmission, providing the basis for more effective and accurate policy implementation.

Key words: epidemic spreading, agent, city map, individual environment interaction, spreading dynamics

中图分类号:

TP391.9

张丽娟, 王福昌, 李振刚. 基于元胞自动机的病毒传播和系统仿真模型[J]. 系统仿真学报, 2021, 33(10): 2449-2459.

Zhang Lijuan, Wang Fuchang, Li Zhengang. Virus Propagation and System Simulation Based on Cellular Automata Model[J]. Journal of System Simulation, 2021, 33(10): 2449-2459.

参考文献

[1] Check Hayden E.World Health Organization rethinks its response to disease outbreaks[J]. Nature (S0028-0836), 2016, 540(7634): 494-495.
[2] 赵宏波, 魏甲晨, 王爽, 等. 大城市新冠肺炎疫情风险评估与精准防控对策——以郑州市为例[J]. 经济地理, 2020, 40(4): 103-109.
Zhao Hongbo, Wei Jiachen, Wang Shuang, et al.The Risk Assessment of Covid-2019 Epidemic in Metropolis and Precise Prevention and Control Measures: A Case Study of Zhengzhou City[J]. Economic Geography, 2020, 40(4): 103-109.
[3] 陈长坤, 童蕴贺. 基于元胞自动机的传染病跨区域传播模型研究[J]. 武汉理工大学学报(信息与管理工程版), 2018, 40(4): 359-363.
Chen Changkun, Tong Yunhe.Research on Inter-regional Epidemic Model of Infectious Diseases Based on Cellular Automata[J]. Journal of Wut (Infor Mation & Management Engineering), 2018, 40(4): 359-363.
[4] 盛华雄, 吴琳, 肖长亮. 新冠肺炎疫情传播建模分析与预测[J]. 系统仿真学报, 2020, 32(5): 759-766.
Sheng Huaxiong, Wu Lin, Xiao Changliang.Modeling Analysis and Prediction on NCP Epidemic Transmission[J]. Journal of System Simulation, 2020, 32(5): 759-766.
[5] 尹楠. 基于SIR模型的有限区域内新冠肺炎疫情传播仿真模拟[J]. 统计与决策, 2020, 36(5): 15-20.
Yin Nan, A Simulation of COVID-19 Epidemic Propagation in Limited Area Based on SIR Model[J], Statistics and Decision, 2020, 36(5): 15-20.
[6] 张艳霞, 李进. 基于SIR模型的新冠肺炎疫情传播预测分析[J]. 安徽工业大学学报(自然科学版), 2020, 37(1): 94-101.
Zhang Yanxia, Li Jin.Prediction and Analysis of Propagation of Novel Coronavirus Pneumonia Epidemic Based on SIR Model[J]. Journal of Anhui University of Technology (Natural Science), 2020, 37(1): 94-101.
[7] 范如国, 王奕博, 罗明, 等.基于SEIR的新冠肺炎传播模型及拐点预测分析[J].电子科技大学学报, 2020, 49(3): 369-374.
Fan Ruguo, Wang Yibo, Luo Ming, et al.Prediction and Analysis of Novel Coronavirus Pneumonia Transmission Model and Inflection Point Based on SEIR Model[J]. Journal of University of Electronic Science and Technology of China, 2020, 49(3): 369-374.
[8] 张宇, 田万利, 吴忠广, 等. 基于改进SEIR模型的新冠肺炎疫情沿交通线路传播机制[J]. 交通运输工程学报, 2020, 20(3): 150-158.
Zhang Yu, Tian Wanli, Wu Zhongguang, et al.Transmission Mechanism of COVID-19 Epidemic Along Traffic Routes Based on Improved SEIR Model[J], Journal of Traffic and Transportation Engineering, 2020, 20(3): 150-158.
[9] Yang Z, Zeng Z, Wang K, et al.Modified SEIR and AI Prediction of the Epidemics Trend of COVID-19 in China Under Public Health Interventions[J]. Journal of Thoracic Disease (S2072-1439), 2020, 12(3): 165-174.
[10] 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, 39(1): 1-5.
Wang Zhixin, Liu Zhi, Liu Zhaojun.COVID-19 Analysis and Forecast Based on Machine Learning[J]. Journal of Biomedical Engineering Research, 2020, 39(1): 1-5.
[11] 张琳. 新冠肺炎疫情传播的一般增长模型拟合与预测[J]. 电子科技大学学报, 2020, 49(3): 345-348.
Zhang Lin.Novel Coronavirus Pneumonia Epidemic Spread Model and Prediction[J]. Journal of University of Electronic Science and Technology, 2020, 49(3): 345-348.
[12] Huang G, Zhu Y P, Huang H Y.Application of Seasonal ARIMA Model in Epidemic Prediction of Hand Foot Mouth Disease in Jiang Men City[J]. China health Statistics (S1002-3674), 2019, 36(1): 65-67.
[13] 李昊, 段德光, 陶学强, 等. 传染病动力学模型及其在新型冠状病毒肺炎疫情仿真预测中的应用综述[J]. 医疗卫生装备, 2020, 41(3): 7-12.
Li Hao, Duan Deguang, Huang Huanying.Epidemic Dynamics Model and Its Application in New Coronavirus Pneumonia Epidemic Simulation Prediction[J]. Medical and Health Equipment, 2020, 41(3): 7-12.
[14] 丁莹,张健钦,杨木,等,新冠疫情城市仿真模型及防控措施评价——以武汉市为例[J/OL].清华大学学报(自然科学版), (2020-11-23) [2021-01-11]. https://kns. cnki.net/kcms/detail/11.2223.N.20201120.1511.001.html
Ding Ying, Zhang Jianqin, Yang Mu, et al. Communicable Disease Transmission Model for the Prevention and Control of COVID-19 in Wuhan, China[J/OL]. Journal of Tsinghua University (Natural Science Edition), (2020-11-23) [2021-01-11]. https://kns. cnki.net/kcms/detail/11.2223.N.20201120.1511.001.html
[15] Furati K M, Sarumi I O, Khaliq A.Fractional Model for the Spread of COVID-19 Subject to Government Intervention and Public Perception[J].] Applied Mathematical Modelling (S0307-904X), 2021, 95(4): 89-105.
[16] Aesa A, Gq B, D H. Stability Analysis in COVID-19 Within-host Model with Immune Response[J]. Communications in Nonlinear Science and Numerical Simulation (S1007-5704,), 2020, 95(2): 105-125.
[17] Alrabaiah H, Arfan M, Shah K, et al.A Comparative Study of Spreading of Novel Coronavirus Disease by Using Fractional Order Modified SEIR Model[J]. Alexandria Engineering Journal (S1110-0168), 2021, 60(1): 573-585.
[18] Gomes D S, Andrade L A, Ribeiro C, et al.Risk clusters of COVID-19 Transmission in Northeastern Brazil: Prospective Space-time Modeling[J]. Epidemiology and Infection (S0950-2688), 2020, 148(3): 1-23.
[19] Cuan W, Ni Z, Hu Y, et al.Clinical Characteristics of Coronavirus Disease 2019 in China[J]. New England Journal of Medicine (S0028-4793), 2020, 18(5): 395-400.
[20] Fofana A M, Hurford A.Mechanistic Move-ment Models to Understand Epidemic Spread[J]. Philosophical Transactions of the Royal Society of London (S0962-8436), 2017, 372(1719): 1-11.
[21] Gemmetto V, Barrat A, Cattuto C.Mitigation of Infectious Disease at School: Targeted Class Closure vs School Closure[J]. BMC Infectious Disease (S1471-2334), 2014, 14(1): 695.
[22] 刘耀宝, 曹俊. 我国境外输入性疟疾防控策略对当前新型冠状病毒肺炎防控工作的启示[J]. 中国血吸虫病防止杂志, 2020, 32(2): 113-118.
Liu Yaobao, Cao Jun.Management of Coronavirus Disease 2019 (COVID-19): Experiences From Imported Malaria Control in China[J]. Chinese Journal of Schistosomiasis Control, 2020, 32(2): 113-118.
[23] Wang D W, Hu B, Hu C, et al.Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-infected Pneumonia in Wuhan, China[J]. JAME the Journal of the American, Edical Association (S0098-7484), 2020, 323(11): 1061-1069.

基于元胞自动机的病毒传播和系统仿真模型

Virus Propagation and System Simulation Based on Cellular Automata Model

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