论文标题
COVID-19-19大流行数据的新兴多项式增长趋势及其与隔室模型的核对
Emerging Polynomial Growth Trends in COVID-19 Pandemic Data and Their Reconciliation with Compartment Based Models
论文作者
论文摘要
我们研究了2019年1月至2020年5月在119个国家的Covid -19 -19-19大流行暴发的报告。我们观察到，各个国家的活动病例的时间序列（确认感染总数的差异以及所报告的死亡人数和恢复病例的总数）与多项式增长表现出很强的一致性，在后来的流行阶段，同时的多项式生长与指数衰减相结合。我们的结果也是根据流行病的隔室类型数学模型来提出的。在这些模型中，在高级流行阶段中表征所观察到的状态的通用缩放可以解释为相对复制号$ r_0 $的代数衰减，为$ t_m/t $，其中$ t_m $是常数，$ t $是流行病暴发的持续时间。我们展示了如何应用我们的发现来改善报告的大流行数据的预测并估计一些流行病参数。请注意，尽管该模型与报告的数据显示出良好的一致性，但我们没有对大流行的实际规模提出任何要求，因为观察到的报告数据与人群中感染总数的关系仍然未知。
We study the reported data from the COVID-19 pandemic outbreak in January - May 2020 in 119 countries. We observe that the time series of active cases in individual countries (the difference of the total number of confirmed infections and the sum of the total number of reported deaths and recovered cases) display a strong agreement with polynomial growth and at a later epidemic stage also with a combined polynomial growth with exponential decay. Our results are also formulated in terms of compartment type mathematical models of epidemics. Within these models the universal scaling characterizing the observed regime in an advanced epidemic stage can be interpreted as an algebraic decay of the relative reproduction number $R_0$ as $T_M/t$, where $T_M$ is a constant and $t$ is the duration of the epidemic outbreak. We show how our findings can be applied to improve predictions of the reported pandemic data and estimate some epidemic parameters. Note that although the model shows a good agreement with the reported data we do not make any claims about the real size of the pandemics as the relation of the observed reported data to the total number of infected in the population is still unknown.
