新型冠状病毒肺炎传播动力学模型构建与分析

于振华; 黄山阁; 杨波; 高红霞; 卢思

doi:10.7652/xjtuxb202205005

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新型冠状病毒肺炎传播动力学模型构建与分析

更新时间：2025-07-09

- 新型冠状病毒肺炎传播动力学模型构建与分析
- Dynamics Modeling and Analysis of COVID-19
- 西安交通大学学报 2022年56卷第5期页码：43-53
- 作者机构：
  
  1. 西安科技大学计算机科学与技术学院,西安,710054
  2. 西安科技大学智能系统安全与控制研究所
  3. 西安交通大学自动化科学与工程学院
- 作者简介：
  
  : 2021-09-27。作者简介: 于振华(1977—),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61873277,62006184)
- 基金信息：
  
  : 2021-09-27。作者简介: 于振华(1977—),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61873277,62006184);陕西省重点研发计划资助项目(2019GY-056)
- DOI：10.7652/xjtuxb202205005
  中图分类号： TP399
- 网络首发：2022-05-10，
  
  纸质出版：2022
- 稿件说明：
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于振华, 黄山阁, 杨波, 等. 新型冠状病毒肺炎传播动力学模型构建与分析[J]. 西安交通大学学报, 2022,56(5):43-53.

YU Zhenhua, HUANG Shange, YANG Bo, et al. Dynamics Modeling and Analysis of COVID-19[J]. 2022, 56(5): 43-53.
于振华, 黄山阁, 杨波, 等. 新型冠状病毒肺炎传播动力学模型构建与分析[J]. 西安交通大学学报, 2022,56(5):43-53. DOI： 10.7652/xjtuxb202205005.

YU Zhenhua, HUANG Shange, YANG Bo, et al. Dynamics Modeling and Analysis of COVID-19[J]. 2022, 56(5): 43-53. DOI： 10.7652/xjtuxb202205005.

摘要

为研究新型冠状病毒肺炎(简称新冠肺炎)传播机理和传播风险

预测疫情发展趋势

对政府制定相关疫情防控政策提供帮助

提出了一种新的新冠肺炎传播非线性动力学模型(SLEIR)。该模型考虑到疫情中采取保护措施的人群

将其作为低危群体加入到模型中; 通过对模型的基本再生数、平衡点、稳定性和分岔等进行分析

揭示新冠肺炎传播机理; 利用印度新冠肺炎真实数据对模型参数和部分状态初值进行最小二乘拟合

根据拟合的参数对印度疫情发展趋势做出预测。该模型对印度3～4月、4～5月两阶段疫情预测平均相对误差分别为4.107%和2.805%

对于印度10月最新的疫情

预测平均相对误差为3.266%

预测结果表明SLEIR模型具有较好的预测效果。与传统SEIR模型相比

该模型能适应印度疫情复杂的变化趋势

且具有更高的预测精度

可以为政府选择合适的防控措施提供技术支撑。

Abstract

To study the spreading mechanism and risk and predict the spreading trend of COVID-19

and provide supports for the government to formulate prevention and control policies

a new nonlinear dynamic model

i.e.

the susceptible-low risk-exposed-infectious-removed(SLEIR)model is proposed

and the population with protection measures is regarded as a low-risk group and included into this model. The basic reproduction number

equilibrium

stability and bifurcation are analyzed to reveal the spreading mechanism

and the data on COVID-19 in India is used for least-squares fitting of model parameters and some initial values. The fitted parameters are used to predict the spreading trend in India. Predicting results show that the average relative errors of the epidemic prediction are 4.107% and 2.805% from March to April and from April to May

respectively. For the latest epidemic prediction of India in October

the average relative error is 3.266%. These simulations indicate that the proposed model can accurately predict COVID-19 spreading in India

is more suitable for analyzing its spreading in India with higher prediction accuracy compared with the traditional SEIR model

and can provide technical support for the government of India to select prevention and control measures.

关键词

Keywords

references

PINTO C M A, CARVALHO A R M. A latency fractional order model for HIV dynamics [J]. Journal of Computational and Applied Mathematics, 2017, 312: 240-256.

CANDIDO D S, CLARO I M, DE JESUS J G, et al. Evolution and epidemic spread of SARS-CoV-2 in Brazil [J]. Science, 2020, 369(6508): 1255-1260.

ROOSA K, LEE Y, LUO Ruiyan, et al. Short-term forecasts of the COVID-19 epidemic in Guangdong and Zhejiang, China: February 13-23, 2020 [J]. Journal of Clinical Medicine, 2020, 9(2): 596.

ARAZI R, FEIGEL A. Discontinuous transitions of social distancing in the SIR model [J]. Physica: A Statistical Mechanics and Its Applications, 2021, 566: 125632.

KIOUACH D, SABBAR Y. Global dynamics analysis of a stochastic SIRS epidemic model with vertical transmission and different periods of immunity [J]. International Journal of Dynamical Systems and Differential Equations, 2020, 10(5): 468-491.

ALRABAIAH H, ARFAN M, SHAH K, et al. A comparative study of spreading of novel corona virus disease by using fractional order modified SEIR model [J]. Alexandria Engineering Journal, 2021, 60(1): 573-585.

JIA Wangping, HAN Ke, SONG Yang, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China [J]. Frontiers in Medicine, 2020, 7: 169.

COOPER L, MONDAL A, ANTONOPOULOS C G. A SIR model assumption for the spread of COVID-19 in different communities [J]. Chaos, Solitons Fractals, 2020, 139: 110057.

喻孜, 张贵清, 刘庆珍, 等. 基于时变参数-SIR模型的COVID-19疫情评估和预测 [J]. 电子科技大学学报, 2020, 49(3): 357-361.

YU Zi, ZHANG Guiqing, LIU Qingzhen, et al. The outbreak assessment and prediction of COVID-19 based on time-varying SIR model [J]. Journal of University of Electronic Science and Technology of China, 2020, 49(3): 357-361.

KUDRYASHOV N A, CHMYKHOV M A, VIGDOROWITSCH M. Analytical features of the SIR model and their applications to COVID-19 [J]. Applied Mathematical Modelling, 2021, 90: 466-473.

ALENEZI M N, AL-ANZI F S, ALABDULRAZZAQ H. Building a sensible SIR estimation model for COVID-19 outspread in Kuwait [J]. Alexandria Engineering Journal, 2021, 60(3): 3161-3175.

LYMPEROPOULOS I N. #stayhome to contain Covid-19: Neuro-SIR: Neurodynamical epidemic modeling of infection patterns in social networks [J]. Expert Systems with Applications, 2021, 165: 113970.

LUX T. The social dynamics of COVID-19 [J]. Physica: A Statistical Mechanics and Its Applications, 2021, 567: 125710.

孙皓宸, 刘肖凡, 许小可, 等. 基于连续感染模型的新冠肺炎校园传播与防控策略分析 [J]. 物理学报, 2020, 69(24): 68-77.

SUN Haochen, LIU Xiaofan, XU Xiaoke, et al. Analysis of COVID-19 spreading and prevention strategy in schools based on continuous infection model [J]. Acta Physica Sinica, 2020, 69(24): 68-77.

王国强, 张烁, 杨俊元, 等. 耦合不同年龄层接触模式的新冠肺炎传播模型 [J]. 物理学报, 2021, 70(1): 204-214.

WANG Guoqiang, ZHANG Shuo, YANG Junyuan, et al. Study of coupling the age-structured contact patterns to the COVID-19 pandemic transmission [J]. Acta Physica Sinica, 2021, 70(1): 204-214.

李盈科, 赵时, 楼一均, 等. 新型冠状病毒肺炎的流行病学参数与模型 [J]. 物理学报, 2020, 69(9): 15-24.

LI Yingke, ZHAO Shi, LOU Yijun, et al. Epidemiological parameters and models of coronavirus disease 2019 [J]. Acta Physica Sinica, 2020, 69(9): 15-24.

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. SEIR-based COVID-19 transmission model and inflection point prediction analysis [J]. Journal of University of Electronic Science and Technology of China, 2020, 49(3): 369-374.

CARCIONE J M, SANTOS J E, BAGAINI C, et al. A simulation of a COVID-19 epidemic based on a deterministic SEIR model [J]. Frontiers in Public Health, 2020, 8: 230.

IVORRA B, FERRÁNDEZ M R, VELA-PÉREZ M, et al. Mathematical modeling of the spread of the coronavirus disease 2019(COVID-19)taking into account the undetected infections: the case of China [J]. Communications in Nonlinear Science and Numerical Simulation, 2020, 88: 105303.

JAHANSHAHI H, MUNOZ-PACHECO J M, BEKIROS S, et al. A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19 [J]. Chaos, Solitons Fractals, 2021, 143: 110632.

KUNIYA T. Prediction of the epidemic peak of coronavirus disease in Japan, 2020 [J]. Journal of Clinical Medicine, 2020, 9(3): 789.

周武略, 白迪, 赵继军. 武汉市发生的新冠病毒肺炎建模研究分析 [J]. 复杂系统与复杂性科学, 2020, 17(4): 58-65.

ZHOU Wulüe, BAI Di, ZHAO Jijun. Modeling and Analysis of COVID-19 in Wuhan [J]. Complex Systems and Complexity Science, 2020, 17(4): 58-65.

YARSKY P. Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states [J]. Mathematics and Computers in Simulation, 2021, 185: 687-695.

LÓPEZ L, RODÓ X. The end of social confinement and COVID-19 re-emergence risk [J]. Nature Human Behaviour, 2020, 4(7): 746-755.

RASLAN W E. Fractional mathematical modeling for epidemic prediction of COVID-19 in Egypt [J]. Ain Shams Engineering Journal, 2021, 12(3): 3057-3062.

杨波, 于振华, 蔡远利. 新型冠状病毒肺炎传播与控制数学建模研究[J]. 西安交通大学学报, 2021, 55(11): 162-172.

YANG Bo, YU Zhenhua, CAI Yuanli. Mathematical modeling for spread and control of COVID-19[J]. Journal of Xi'an Jiaotong University, 2021, 55(11): 162-172.

马知恩, 周义仓, 王稳地, 等. 传染病动力学的数学建模与研究 [M]. 北京: 科学出版社, 2004: 116-126.

观研报告网. 2010-2018年印度人口总数和年度增长率数据统计表[EB/OL].(2020-04-09)[2021-08-27]. http:∥data.chinabaogao.com/hgshj/2020/0494X005 2020.html.

Center for Global Development. Bloomberg: COVID may have claimed as many as 5 million lives in India[EB/OL].(2021-07-21)[2021-08-27]. https:∥www. cgdev.org/article/bloomberg-covid-may-have-cla imed-many-5-million-lives-india.

Center for Global Development. BBC Newshour: India COVID deaths may be ten times the official rate[EB/OL].(2021-07-20)[2021-08-27]. https:∥www. cgdev.org/article/bbc-newshour-india-covid-deaths-may-be-ten-times-official-rate.

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