Simulation study of societal propagation dynamics model for early-warning information dissemination

doi:10.16265/j.cnki.issn1003-3033.2026.06.0363

Abstract

Abstract:

To address insufficient timeliness and incomplete coverage in current early-warning information propagation, a dynamical model incorporating forced intervention mechanisms is developed based on dynamical systems theory, using early-warning information dissemination as a case study. This model introduces quantitative classification of warning levels, illuminating the coupling mechanisms between spontaneous diffusion and forced intervention. Numerical simulations are conducted to analyze the impact of key parameters such as the number of initial disseminators and the basic reproduction number on dissemination effectiveness. Model analysis indicates that the basic reproduction number serves as the criterion for information diffusion. Low-level early-warnings have a limited dissemination scale, while high-level early-warnings can achieve full coverage through forced intervention. Simulation results show that an eightfold increase in the annihilation rate can shorten the dissemination timeliness by 40%. The basic reproduction number is inversely proportional to dissemination timeliness.

Key words: early-warning information dissemination, dissemination dynamics, basic reproduction number, forced intervention, simulation

CLC Number:

X913.2安全信息论

Shi Yuheng, Qiao Yuan, Ji Xuewei, Wu Bingxiang, Gao Yayun, Jiang Jiang. Simulation study of societal propagation dynamics model for early-warning information dissemination[J]. China Safety Science Journal, 2026, 36(6): 9-14.

Figures/Tables 6

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References 21

[1]	石玉恒, 陈东宁, 林倩, 等. 预警信息发布业务发展分析[J]. 中国应急管理, 2022(12):64-65.
[2]	石玉恒, 陈东宁, 乔媛, 等. 预警信息发布的工作原则和实现途径[J]. 城市与减灾, 2024(2):20-24.
[3]	陈安滢, 朱昊然, 苏国锋. 微博用户的应急预警信息传播行为研究[J]. 清华大学学报(自然科学版), 2021, 61(6):527-535. doi: 10.16511/j.cnki.qhdxxb.2020.26.045
	Chen Anying, Zhu Haoran, Su Guofeng. Emergency warning information repost behavior of Weibo users[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(6):527-535. doi: 10.16511/j.cnki.qhdxxb.2020.26.045
[4]	曹之玉, 朱小祥, 兰海波, 等. 自然灾害预警信息与传播手段自动适配策略研究:以湖北为例[J]. 灾害学, 2021, 36(3):180-183.
	Cao Zhiyu, Zhu Xiaoxiang, Lan Haibo, et al. Research on automatic adaptation strategy of early warning information and dissemination means of natural disasters:taking Hubei for example[J]. Journal of Catastrophology, 2021, 36(3):180-183.
[5]	王景丽, 赵宇. 预警信息传播需求与通信网传播渠道匹配研究[J]. 电信科学, 2022, 38(5):104-113. doi: 10.11959/j.issn.1000-0801.2022101
	Wang Jingli, Zhao Yu. Study on the matching scheme between early warning information dissemination demands and communication network dissemination channels[J]. Telecommunications Science, 2022, 38(5):104-113. doi: 10.11959/j.issn.1000-0801.2022101
[6]	Daley D J, Kendall D G. Epidemics and rumours[J]. Nature, 1965, 204(4963):DOI:10.1038/2041118a0.
[7]	郭云龙. 基于SIR模型的机动车内传染病传播研究[J]. 中国安全科学学报, 2021, 31(1): 173-178. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.025
	Guo Yunlong. Research on transmission of infectious diseases in motor vehicles based on SIR model[J]. China Safety Science Journal, 2021, 31(1): 173-178. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.025
[8]	Zachilas L, Benos C. Modeling and visualizing the dynamic spread of epidemic diseases:the COVID-19 case[J]. AppliedMath, 2024, 4(1):1-19. doi: 10.3390/appliedmath4010001
[9]	Jia Jiwei, Liu Siyu, Liu Yawen, et al. Modeling and reviewing analysis of the COVID-19 epidemic in Algeria with diagnostic shadow[J]. Cold Spring Harbor Laboratory Press, 2021:DOI:10.1101/2021.06.09.21258668.
[10]	Georgiev S G, Vulkov L G. Coefficient identification in a SIS fractional-order modelling of economic losses in the propagation of COVID-19[J]. Journal of computational science, 2023:69.DOI:10.1016/j.jocs.2023.102007.
[11]	Huang Lidong, Xia Yue, Qin Wenjie. Study on SEAI model of COVID-19 based on asymptomatic infection[J]. Axioms (2075-1680), 2024, 13(5):1-17.
[12]	Huang Jiajia, Su Qiang. A rumor spreading model based on user browsing behavior analysis in microblog[C]. Service Systems and Service Management (ICSSSM), 2013 10^th International Conference on IEEE, 2013:170-173.
[13]	Zhao Zhenjun, Liu Yongmei, Wang Kexi. An analysis of rumor propagation based on propagation force[J]. Physica A Statistical Mechanics & Its Applications, 2016, 443:263-271.
[14]	朱冠桦, 蒋国平, 夏玲玲. 社交网络上从众现象对谣言传播影响的研究[J]. 计算机科学, 2016, 43(2):135-139,143. doi: 10.11896/j.issn.1002-137X.2016.02.030
	Zhu Guanhua, Jiang Guoping, Xia Lingling. Rumor spreading model considering conformity phenomena in complex social networks[J]. Computer Science, 2016, 43(2):135-139,143. doi: 10.11896/j.issn.1002-137X.2016.02.030
[15]	Xiao Yunpeng, Song Chenguang, Liu Yanbing. Social hotspot propagation dynamics model based on multidimensional attributes and evolutionary games[J]. Communications in Nonlinear Science and Numerical Simulation, 2019, 67:13-25. doi: 10.1016/j.cnsns.2018.06.017
[16]	陈鑫, 谢科范. 基于主动安全的重大事故网络舆情智能建模与仿真[J]. 中国安全科学学报, 2024, 34(1): 53-61. doi: 10.16265/j.cnki.issn1003-3033.2024.01.0741
	Chen Xin, Xie Kefan. Intelligent modeling and simulation of online public opinion for major accidents based on proactive safety[J]. China Safety Science Journal, 2024, 34(1): 53-61. doi: 10.16265/j.cnki.issn1003-3033.2024.01.0741
[17]	Zhang Yuexia, Feng Yixuan. Two-layer coupled network model for topic derivation in public opinion propagation[J]. China Communications, 2020, 17(3):176-187.
[18]	夏玲玲. 复杂网络上的信息传播动力学建模与免疫策略研究[D]. 南京: 南京邮电大学, 2017.
	Xia Lingling. Modeling of information transmission dynamics and immunization strategies on complex networks[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2017.
[19]	陈波, 于泠, 刘君亭, 等. 泛在媒体环境下的网络舆情传播控制模型[J]. 系统工程理论与实践, 2011, 31(11):2140-2150.
	Chen Bo, Yu Ling, Liu Junting, et al. Dissemination and control model of internet public opinion in the ubiquitous media environments[J]. Systems Engineering-Theory & Practice, 2011, 31(11):2140-2150.
[20]	马知恩, 周义仓. 常微分方程定性与稳定性方法[M]. 北京: 科学出版社,2001:319-321.
[21]	Yin Fulian, Xia Xinyu, Song Nan, et al. Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog[J]. Plos One, 2020, 15:1-20.