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Pathogenic–dynamic epidemic agent model with an epidemic threshold

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  • Wang, Shih-Chieh
  • Ito, Nobuyasu

Abstract

An agent model of epidemic spreading is proposed. When a susceptible agent is exposed to pathogen levels above its specified infection-tolerance threshold F, its state changes to infected, where a pathogen is a virus, microparasite, or any other disease-causing organism or material. The spreading sources of pathogens are the agents that are in the infected state. The growth and decay of a pathogen in an infected host obeys a given function, ft. This function increases linearly in the early period of infection t1, and then decreases linearly to zero in the latter period t2. The simulation results show that as the agent density increases, the model undergoes a phase transition from a local epidemic phase to a pandemic phase. For immobile agents, transition density ρC equals transition density ρP of the corresponding site percolation model. For random-walking agents, the transition density decreases as ρCU≈ρP×U−0.3, where U denotes the average path length of the walking agent during the period t1+t2. This model provides a reliable alternative to the standard SIR model, which is a simple compartmental model of susceptibility, infection, and pathogen removal. Moreover, it can predict epidemic phenomena using fact-based parameters.

Suggested Citation

  • Wang, Shih-Chieh & Ito, Nobuyasu, 2018. "Pathogenic–dynamic epidemic agent model with an epidemic threshold," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1038-1045.
  • Handle: RePEc:eee:phsmap:v:505:y:2018:i:c:p:1038-1045
    DOI: 10.1016/j.physa.2018.04.035
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    References listed on IDEAS

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    1. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
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    Cited by:

    1. Shih-Chieh Wang & Nobuyasu Ito, 2019. "On principal eigenpair of temporal-joined adjacency matrix for spreading phenomenon," Journal of Computational Social Science, Springer, vol. 2(1), pages 67-76, January.

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