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Haze risk: information diffusion based on cellular automata

Natural Hazards volume 107, pages 2605–2623 (2021)Cite this article

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.

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Acknowledgements

The authors are grateful to the case company for permitting and supporting this research. This work was financially supported by Humanities and social sciences research Project of the Ministry of Education (20YJAZH096), the Key Project of National Social and Scientific Fund Program in China (Grant Number. 18ZDA052), the Project of National Social and Scientific Fund Program in China (Grant Number.17BGL142), Open Project of Jiangsu Productivity Society (JSSCL2019B016).

Funding

Author Peng has received research grants from the National Natural Science Foundation of China (Grant Number. 71263040, 91546117), the Key Project of National Social and Scientific Fund Program in China (Grant Number. 18ZDA052), the Project of National Social and Scientific Fund Program in China (Grant Number. 17BGL142), Open Project of Jiangsu Productivity Society (JSSCL2019B016), HRSA, US DHHS (Grant Number. H49MC00068). Author Wan has received research grants from the Humanities and social sciences research project of the Ministry of Education (20YJAZH096).

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Authors and Affiliations

  1. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, People’s Republic of China

    Chaoyu Zheng, Benhong Peng, Xin Sheng & Anxia Wan

  2. Binjiang College, Nanjing University of Information Science and Technology, Wuxi, 214105, People’s Republic of China

    Benhong Peng

Authors
  1. Chaoyu Zheng
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  2. Benhong Peng
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  3. Xin Sheng
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  4. Anxia Wan
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Contributions

BP contributed to conceptualization, methodology and software. CZ was involved in writing—original draft, review and editing, and software. XS contributed to writing—review and editing, and investigation. AW was involved in validation.

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Correspondence to Chaoyu Zheng or Benhong Peng.

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Zheng, C., Peng, B., Sheng, X. et al. Haze risk: information diffusion based on cellular automata. Nat Hazards 107, 2605–2623 (2021). https://doi.org/10.1007/s11069-021-04521-2

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  • DOI: https://doi.org/10.1007/s11069-021-04521-2

Keywords

  • Haze risk
  • Information diffusion
  • Cellular automata
  • Life cycle
  • Intense-mild control strategy