论文标题
数据驱动的建模揭示了社会疏远下的共同19岁大流行的基本动态
Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing
论文作者
论文摘要
我们表明，社会距离下的Covid-19大流行展示了普遍的动态。感染和死亡的累积数量迅速从早期的指数增长到更长的电力法增长，然后最终放缓。与IHME的最新统计预测模型一致，我们表明这种动态由ERF函数很好地描述。使用这种功能形式，我们在国家和美国各州进行人口特征和社会疏远政策的数据崩溃，证实了Covid-19-19爆发的普遍行为。我们表明，统计模型的预测能力受到限制，直到曲线变平之前的几天，预测死亡和感染，假设当前政策继续进行，并将我们的预测与IHME模型进行比较。我们提出了模拟，显示这种通用动态与无标度网络和具有非马克维亚传播动态的随机网络的疾病传播一致。
We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics.
