论文标题
在无标度网络中的流行病的传播和缓解
Propagation and mitigation of epidemics in a scale-free network
论文作者
论文摘要
通常，使用SIR模型的早期指数升高的速率可以预测流行病曲线和COVID-19大流行的最终范围。这些预测隐含地采用了完整的社会混合，这通常是不合理的。在这里，我基于巴拉巴西（Albert）无标度网络模型的流行病的随机传播，对这些预测进行了反例。流行病的开始表明$ r_0 = 2.6 $，但与$ω\约70 \％{} $不同，由SIR模型预测，它们的最终范围仅为$ω\ ox \％{} $，而无需外部缓解，$ω\ $ $ω\ \ $ - $ 1.5 $ - $ 1.5 \％\％{} $ {} $ {} $ {} $ with Mitigatiatiatiatiatiatiation。顶部的每日感染率也比SIR模型少1-1.5个数量级。仅隔离1.5 \％{}大多数活跃的超级公布对范围和最高感染率的影响与盲人隔离整个社区的随机50 \％{}。
The epidemic curve and the final extent of the COVID-19 pandemic are usually predicted from the rate of early exponential raising using the SIR model. These predictions implicitly assume a full social mixing, which is not plausible generally. Here I am showing a counterexample to the these predictions, based on random propagation of an epidemic in Barabási--Albert scale-free network models. The start of the epidemic suggests $R_0=2.6$, but unlike $Ω\approx 70\%{}$ predicted by the SIR model, they reach a final extent of only $Ω\approx 4\%{}$ without external mitigation and $Ω\approx 0.5$--$1.5\%{}$ with mitigation. Daily infection rate at the top is also 1--1.5 orders of magnitude less than in SIR models. Quarantining only the 1.5\%{} most active superspreaders has similar effect on extent and top infection rate as blind quarantining a random 50\%{} of the full community.
