带缓解措施的易感-感染-易感模型于无尺度网络
摘要
我们使用应用于易感-感染-易感模型的异构均匀场方法，考察了在无尺度网络上的传染病扩散， Incorporating a mitigation factor. Individual heterogeneity is incorporated through a power-law distribution, while a mitigation factor accounts for behavioral responses and external effects that effectively reduce transmission from infected individuals. This mechanism, inspired by Malthus-Verhulst-type constraints, introduces a nonlinear saturation effect that encodes self-limiting dynamics in a tractable way. Analytical results are supported by stochastic simulations. We find that the mitigation factor induces a nontrivial behavior in the probability that a link points to an infected node, which develops a maximum at finite infection rates. In contrast, the overall prevalence remains a monotonically increasing function of the transmission rate. Additionally, the mitigation mechanism leads to an inversion in the dependence of epidemic observables on the degree exponent at sufficiently high transmission rates. While in the standard model smaller exponents yield higher endemic prevalence, in the modified model this trend reverses, with larger exponents producing higher prevalence and increased infection probability along network links.
引用
@article{arxiv.2605.10644,
title = {Susceptible-Infected-Susceptible Model with Mitigation on Scale-Free Networks},
author = {João Gabriel Simões Delboni and M. O. Hase},
journal= {arXiv preprint arXiv:2605.10644},
year = {2026}
}