Departamento de Ciencias, Facultad de Artes Liberales, Universidad Adolfo Ibáñez, Santiago 7491169, Chile.
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago 7491169, Chile.
Chaos. 2020 Oct;30(10):103120. doi: 10.1063/5.0019742.
We present a phenomenological procedure of dealing with the COVID-19 (coronavirus disease 2019) data provided by government health agencies of 11 different countries. Usually, the exact or approximate solutions of susceptible-infected-recovered (or other) model(s) are obtained fitting the data by adjusting the time-independent parameters that are included in those models. Instead of that, in this work, we introduce dynamical parameters whose time-dependence may be phenomenologically obtained by adequately extrapolating a chosen subset of the daily provided data. This phenomenological approach works extremely well to properly adjust the number of infected (and removed) individuals in time for the countries we consider. Besides, it can handle the sub-epidemic events that some countries may experience. In this way, we obtain the evolution of the pandemic without using any a priori model based on differential equations.
我们提出了一种处理来自 11 个不同国家政府卫生机构的 COVID-19（2019 年冠状病毒病）数据的现象学方法。通常，通过调整包含在这些模型中的时间独立参数，通过拟合数据来获得易感感染恢复（或其他）模型的精确或近似解。然而，在这项工作中，我们引入了动态参数，其时间依赖性可以通过适当外推所选的每日提供数据子集来获得。这种现象学方法非常适用于及时调整我们所考虑的国家的感染（和清除）人数。此外，它还可以处理某些国家可能经历的亚流行事件。通过这种方式，我们在不使用任何基于微分方程的先验模型的情况下获得了大流行的演变。
