异质性可显著增加流行病SIR模型中的最终暴发规模
Heterogeneity can markedly increase final outbreak size in the SIR model of epidemics
AI总结：
该研究针对异质网络上的流行病SIR模型，发现存在临界基本再生数$R_0^c$，低于该值时有限网络异质性会增大最终暴发规模，且种群异质性可增强该效应，为现实场景下SIR模型的暴发规模预测提供了关键参考。
摘要：
我们研究了具有种群异质性的正相关异质网络上的流行病SIR模型，探究了最终暴发规模对网络异质性强度与基本再生数$R_0$（即个体感染率与恢复率的比值）的依赖关系。我们发现存在一个临界值$R_0^c$：当$R_0$高于该临界值时，最大暴发规模出现在异质性为零的情况下；而当$R_0$低于该临界值时，最大暴发规模出现在有限异质性强度下。这种二阶相变对所有具有有限标准化矩的网络分布都具有普适性，表明网络异质性可大幅提升最终暴发规模。我们还表明，以个体间易感性和传染性存在差异形式存在的种群异质性会增强这一效应。值得注意的是，我们的结果为现实场景下均匀混合SIR模型对最终暴发规模的可预测性提供了关键洞见。
英文摘要：
We study the SIR model of epidemics on positively correlated heterogeneous networks with population variability, and explore the dependence of the final outbreak size on the network heterogeneity strength and basic reproduction number $R_0$ -- the ratio between the infection and recovery rates per individual. We reveal a critical value $R_0^c$, above which the maximal outbreak size is obtained at zero heterogeneity, but below which, the maximum is obtained at finite heterogeneity strength. This second-order phase transition, universal for all network distributions with finite standardized moments indicates that, network heterogeneity can greatly increase the final outbreak size. We also show that this effect can be enhanced by adding population heterogeneity, in the form of varying inter-individual susceptibility and infectiousness. Notably, our results provide key insight as to the predictability of the well-mixed SIR model for the final outbreak size, in realistic scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
