论文标题
在复杂网络上进行自适应预防的流行病暴发
Epidemic outbreaks with adaptive prevention on complex networks
论文作者
论文摘要
采用预防态度,例如社会隔离和面罩的使用,以缓解流行病爆发,这在很大程度上取决于对人群的支持。在这项工作中,我们研究了一种易感感染的(SIR)流行模型,在该模型中,对环境的流行病学感知可以使易感人物的行为适应预防行为。 {考虑了两个易感人群的隔室,以区分那些采用或不采用预防态度的人。}两个规则,具体取决于本地和全球流行病,以研究流行病在异质网络中的传播。我们介绍了异质平均场理论和随机模拟的结果。前者对全球规则表现良好,但错过了当地案件中模拟的相关结果。在模拟中,只有本地意识才能显着提高流行阈值,延迟患病率的高峰并降低暴发规模。有趣的是,我们观察到,增加局部感知率会导致更少的人招募到保护状态,但仍提高了缓解暴发的有效性。我们还报告,网络异质性大大降低了局部意识机制的疗效,因为HUB是SIR动力学的超级传播者,对低流行病患病率的流行环境几乎没有反应。我们的结果表明,改善对谁在社会上非常活跃的看法的策略可以改善对流行病暴发的缓解。
The adoption of prophylaxis attitudes, such as social isolation and use of face masks, to mitigate epidemic outbreaks strongly depends on the support of the population. In this work, we investigate a susceptible-infected-recovered (SIR) epidemic model, in which the epidemiological perception of the environment can adapt the behavior of susceptible individuals towards preventive behavior. {Two compartments of susceptible individuals are considered, to distinguish those that adopt or not prophylaxis attitudes.} Two rules, depending on local and global epidemic prevalence, for the spread of the epidemic in heterogeneous networks are investigated. We present the results of both heterogeneous mean-field theory and stochastic simulations. The former performs well for the global rule, but misses relevant outcomes of simulations in the local case. In simulations, only local awareness can significantly raise the epidemic threshold, delay the peak of prevalence, and reduce the outbreak size. Interestingly, we observed that increasing the local perception rate leads to less individuals recruited to the protected state, but still enhances the effectiveness in mitigating the outbreak. We also report that network heterogeneity substantially reduces the efficacy of local awareness mechanisms since hubs, the super-spreaders of the SIR dynamics, are little responsive to epidemic environments in the low epidemic prevalence regime. Our results indicate that strategies that improve the perception of who is socially very active can improve the mitigation of epidemic outbreaks.