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Haze risk: information diffusion based on cellular automata

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  • Chaoyu Zheng

    (Nanjing University of Information Science and Technology)

  • Benhong Peng

    (Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology)

  • Xin Sheng

    (Nanjing University of Information Science and Technology)

  • Anxia Wan

    (Nanjing University of Information Science and Technology)

Abstract

Negative effects of haze risk can easily spread faster and more widely, however, existing studies rarely investigate the whole period or cycle of diffusion events, which leads to lowered public knowledge, often resulting in exaggerated negative effects. In this paper, the diffusion simulation model based on cellular automata is used to evaluate the diffusion of haze risk information. Firstly, according to the whole-life cycle of emergencies, the public affected by haze risk information is classified by resembling the SEIR infectious disease model. Secondly, a diffusion rule from unknown to exposed individuals is developed based on the theory of cellular automata. Then, according to the individual state transformation at different stages, a whole-life cycle model regarding haze risk information diffusion and propagation model is constructed. Finally, appropriate parameters are selected to calculate the results without intervention. The results show that during the whole evolution process, the unknowns continue to decrease, and the lurkers continue to increase. Due to the existence of the immunization period, the immunized persons reach their maximum number before they are about to lose immunity, and the number of communicators reaches their minimum. Afterward, the number of immunized persons reduced to a stable level, and the number of communicators continues to increase toward an agglomeration benefit. Therefore, in order to achieve effective control of the spread of haze risk information, strong and weak control measures are taken for each type of individuals, and the immunity of unknowns and lurkers is increased for individual type control. For the entire information diffusion control, increase communicator immunity and reduce immunization conversion rate. The study of the spread of haze risk information helps to increase the public's sense of responsibility, helps to improve the government's credibility, and contributes to the establishment of a harmonious society.

Suggested Citation

  • Chaoyu Zheng & Benhong Peng & Xin Sheng & Anxia Wan, 2021. "Haze risk: information diffusion based on cellular automata," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2605-2623, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04521-2
    DOI: 10.1007/s11069-021-04521-2
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    References listed on IDEAS

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    1. Kim, Jooyoung & Ahn, Chiwon & Lee, Seungjae, 2018. "Modeling handicapped pedestrians considering physical characteristics using cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 507-517.
    2. Guseo, Renato & Guidolin, Mariangela, 2010. "Cellular Automata with network incubation in information technology diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2422-2433.
    3. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    4. Juan Benjamin Duarte Duarte & Leonardo Hernán Talero Sarmiento & Katherine Julieth Sierra Suárez, 2017. "Evaluation of the effect of investor psychology on an artificial stock market through its degree of efficiency," Contaduría y Administración, Accounting and Management, vol. 62(4), pages 1361-1376, Octubre-D.
    5. Guan, Junbiao & Wang, Kaihua & Chen, Fangyue, 2016. "A cellular automaton model for evacuation flow using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 655-661.
    6. Rui, Xiaobin & Meng, Fanrong & Wang, Zhixiao & Yuan, Guan & Du, Changjiang, 2018. "SPIR: The potential spreaders involved SIR model for information diffusion in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 254-269.
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