论文标题
在COVID-19建模上
On COVID-19 Modelling
论文作者
论文摘要
该贡献通过相对简单的数学和数值方法来分析Covid-19爆发。最终目标是通过可靠的技术预测每个国家流行病爆发的峰值。这是由标准Sir Models激励的算法完成的，并与约翰·霍普金斯大学（Johns Hopkins University）提供的标准数据一致。为了重建未注册感染的数据，该算法使用感染死亡率的当前值以及对恢复率的特定形式的数据驱动估计。所有其他成分也是数据驱动的。提供了各种预测示例以供插图。
This contribution analyzes the COVID-19 outbreak by comparably simple mathematical and numerical methods. The final goal is to predict the peak of the epidemic outbreak per country with a reliable technique. This is done by an algorithm motivated by standard SIR models and aligned with the standard data provided by the Johns Hopkins University. To reconstruct data for the unregistered Infected, the algorithm uses current values of the infection fatality rate and a data-driven estimation of a specific form of the recovery rate. All other ingredients are data-driven as well. Various examples of predictions are provided for illustration.
