论文标题
在数据驱动模型上的评论，用于预测COVID-19的流行病的过程
Remarks on a data-driven model for predicting the course of COVID-19 epidemic
论文作者
论文摘要
Norden E. Huang，Fangli Qiao和Ka Kit Tung提出了COVID-19的数据驱动模型，其中相关功能取决于从可用数据的统计分析获得的七个参数。这些参数不是独立的，它们是通过一组关系链接的作者称为主要结果，这些结果通过数据的统计分析来验证。作者并不总是会阐明问题及其之间的关系的参数。通过在这里给予他们（简单）数学公式，所有描述动态的相关功能都可以明确写下来。所有显式公式都源于以下事实：被感染的日志是时间的二次函数。此处介绍的公式本身不是近似值 - 但是它们所涉及的参数当然是从数据中得出的统计数量。这些公式可能是在某种程度上用于验证模型本身，以更新模型或找到相关数量的近似值。
Norden E. Huang, Fangli Qiao and Ka Kit Tung presented a data-driven model for the COVID-19 epidemic in which the relevant functions depend on a set of seven parameters obtained from a statistical analysis of the available data. These parameters are not independent, they are linked through a set of relations the authors call Main Results which are validated by a statistical analysis of the data. The parameters in questions and the relations between them are not always explicitated by the authors. By given them here their (simple) mathematical formulations all the relevant functions describing the dynamic can be explicitely written down. All the explicit formulas follow from the fact that the log of the number of infected, is a quadratic function of time. The formulas presented here are not themselves approximations - but the parameters they involve are of course statistical quantities derived from the data. These formulas could maybe be of some use either to validate the data, the model itself, to update the model or to find approximations to the relevant quantities.
