Dartmouth College, Hanover, NH, USA.
Sci Rep. 2020 Nov 27;10(1):20776. doi: 10.1038/s41598-020-77457-5.
Early forecasting of COVID-19 virus spread is crucial to decision making on lockdown or closure of cities, states or countries. In this paper we design a recursive bifurcation model for analyzing COVID-19 virus spread in different countries. The bifurcation facilitates recursive processing of infected population through linear least-squares fitting. In addition, a nonlinear least-squares fitting procedure is utilized to predict the future values of infected populations. Numerical results on the data from two countries (South Korea and Germany) indicate the effectiveness of our approach, compared to a logistic growth model and a Richards model in the context of early forecast. The limitation of our approach and future research are also mentioned at the end of this paper.
早期预测 COVID-19 病毒传播对于决定封锁或关闭城市、州或国家至关重要。在本文中，我们设计了一个递归分岔模型来分析不同国家的 COVID-19 病毒传播。分岔通过线性最小二乘拟合促进感染人群的递归处理。此外，还利用非线性最小二乘拟合程序来预测感染人群的未来值。对来自两个国家（韩国和德国）的数据进行的数值结果表明，与逻辑增长模型和 Richards 模型相比，我们的方法在早期预测方面是有效的。本文最后还提到了我们方法的局限性和未来的研究方向。
