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How the individuals’ risk aversion affect the epidemic spreading

Author

Listed:
  • Han, Dun
  • Shao, Qi
  • Li, Dandan
  • Sun, Mei

Abstract

Some diseases can live in people for many years without making them sick, during this time, the bacteria can spread to others who come in contact with the infected person. However, explosive individuals in infected people will exhibit certain dominant states associated with infectious diseases, as a results, the uninfected persons will avoid contact with such individuals with dominant infectious diseases. Considering the individual’s ability to avoid risks in the epidemic season, we propose the epidemic spreading model with individuals’ sensitivity. The epidemic spreading threshold is calculated by means of the mean-field theory and the next-generation matrix method. In addition, the locally, globally and exponential asymptotically stable conditions in the disease-free equilibrium state are given. Finally, we simulate the proposed epidemic spreading modeling in the ER random network and the BA scale-free network. The numerical simulations results show that the probability of the latent individuals transforming into explosive individuals has a greater impact on the spread of infectious diseases. Meanwhile, self-protection is an effective measure to reduce the outbreak of infectious diseases.

Suggested Citation

  • Han, Dun & Shao, Qi & Li, Dandan & Sun, Mei, 2020. "How the individuals’ risk aversion affect the epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:apmaco:v:369:y:2020:i:c:s0096300319308860
    DOI: 10.1016/j.amc.2019.124894
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    References listed on IDEAS

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    1. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
    2. Han, She & Sun, Mei & Ampimah, Benjamin Chris & Han, Dun, 2018. "Epidemic spread in bipartite network by considering risk awareness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1909-1916.
    3. Matjaž Perc & Mahmut Ozer & Janja Hojnik, 2019. "Correction: Social and juristic challenges of artificial intelligence," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 5(1), pages 1-1, December.
    4. Dun, Han & Shuting, Yan & She, Han & Lingfei, Qian & Chris, Ampimah Benjamin, 2019. "Research on how the difference of personal propagation ability influences the epidemic spreading in activity-driven network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 311-318.
    5. Han, Dun & Sun, Mei, 2016. "An evolutionary vaccination game in the modified activity driven network by considering the closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 49-57.
    6. Matjaž Perc & Mahmut Ozer & Janja Hojnik, 2019. "Social and juristic challenges of artificial intelligence," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 5(1), pages 1-7, December.
    7. Eugenio Valdano & Chiara Poletto & Vittoria Colizza, 2015. "Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(12), pages 1-11, December.
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    Cited by:

    1. Chaeyoung Lee & Soobin Kwak & Junseok Kim, 2021. "Controlling COVID-19 Outbreaks with Financial Incentives," IJERPH, MDPI, vol. 18(2), pages 1-13, January.
    2. Munir Ahmad & Nadeem Akhtar & Gul Jabeen & Muhammad Irfan & Muhammad Khalid Anser & Haitao Wu & Cem Işık, 2021. "Intention-Based Critical Factors Affecting Willingness to Adopt Novel Coronavirus Prevention in Pakistan: Implications for Future Pandemics," IJERPH, MDPI, vol. 18(11), pages 1-28, June.
    3. Huang, He & Chen, Yahong & Ma, Yefeng, 2021. "Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 388(C).
    4. Shao, Qi & Han, Dun, 2022. "Epidemic spreading in metapopulation networks with heterogeneous mobility rates," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    5. Meng, Xueyu & Han, Sijie & Wu, Leilei & Si, Shubin & Cai, Zhiqiang, 2022. "Analysis of epidemic vaccination strategies by node importance and evolutionary game on complex networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Lv, Changchun & Zhang, Ye & Lei, Yulin & Duan, Dongli & Si, Shubin, 2024. "Activity centrality-based critical node identification in complex systems against cascade failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    7. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli & Yao, Shirui, 2022. "Cascading failure in networks with dynamical behavior against multi-node removal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. Duan, Dongli & Yan, Qi & Rong, Yisheng & Hou, Gege, 2022. "Predicting the cascading failure of dynamical networks based on a new dimension reduction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    9. Ying Ding & Guanghui Yan & Huayan Pei & Wenwen Chang, 2024. "Effects of self-protection awareness behavior on epidemic spreading in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(12), pages 1-10, December.
    10. Chang, Xinghua & Wang, Jianrong & Liu, Maoxing & Yan, Xue, 2025. "An SIS infectious disease model with nonlinear incidence and disease awareness on complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 196(C).

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