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Research on how the difference of personal propagation ability influences the epidemic spreading in activity-driven network

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  • Dun, Han
  • Shuting, Yan
  • She, Han
  • Lingfei, Qian
  • Chris, Ampimah Benjamin

Abstract

Considering the differences of individuals’ activities and personal propagation ability in real life, a new type of epidemic propagation model in activity-driven network is proposed. By employing the mean-field theory, we theoretically derive the epidemic spreading threshold, and using numerical simulation methods to further study our model in cases where the individual’s activity rate is not related to propagation ability or related conditions. The results indicate that, when individuals’ activity rate has no correlation to personal propagation ability, the epidemic threshold has a wide-ranging with the change of the average activity rate and the average propagation ability. When the recovery rate is small, individuals’ average activity rate has less effect on the density of infected. If an individual’s activity rate negatively correlates to his propagation ability, there is no significant correction between propagation ability and personal infected frequency. However, when the individual’s activity rate and propagation ability are positively correlated, propagation ability and personal infected frequency show a strong positive correlation. Our research provide a feasible method to explore how the differences of individuals’ activities and personal propagation ability affect the epidemic spreading.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:311-318
    DOI: 10.1016/j.physa.2018.09.077
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    References listed on IDEAS

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    1. Eugenio Valdano & Chiara Poletto & Armando Giovannini & Diana Palma & Lara Savini & Vittoria Colizza, 2015. "Predicting Epidemic Risk from Past Temporal Contact Data," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-19, March.
    2. Ciro Cattuto & Wouter Van den Broeck & Alain Barrat & Vittoria Colizza & Jean-François Pinton & Alessandro Vespignani, 2010. "Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-9, July.
    3. Han, Dun & Sun, Mei & Li, Dandan, 2015. "Epidemic process on activity-driven modular networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 354-362.
    4. Zhan, Xiu-Xiu & Liu, Chuang & Sun, Gui-Quan & Zhang, Zi-Ke, 2018. "Epidemic dynamics on information-driven adaptive networks," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 196-204.
    5. Diaz, Paul & Constantine, Paul & Kalmbach, Kelsey & Jones, Eric & Pankavich, Stephen, 2018. "A modified SEIR model for the spread of Ebola in Western Africa and metrics for resource allocation," Applied Mathematics and Computation, Elsevier, vol. 324(C), pages 141-155.
    6. Kaiyuan Sun & Andrea Baronchelli & Nicola Perra, 2015. "Contrasting effects of strong ties on SIR and SIS processes in temporal networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(12), pages 1-8, December.
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    Cited by:

    1. Zhu, He & Ma, Jing & Li, Shan, 2019. "Effects of online and offline interaction on rumor propagation in activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1124-1135.
    2. 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).

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