Department of Mathematics, Washington University in St. Louis, Campus Box 1146 One Brookings Drive St. Louis, Missouri, MI 63130, USA.
Department of Mathematics, University of Wyoming, Laramie, Wyoming, 82071, USA.
J Theor Biol. 2020 Jun 7;494:110243. doi: 10.1016/j.jtbi.2020.110243. Epub 2020 Mar 12.
In this paper, we have proposed a two-phase procedure (combining discrete graphs and wavelets) for constructing true epidemic growth. In the first phase, a graph-theory-based approach was developed to update partial data available and in the second phase, we used this partial data to generate plausible complete data through wavelets. We have provided two numerical examples. This procedure is novel and implementable and adaptable to machine learning modeling framework.
在本文中，我们提出了一种两阶段方法（结合离散图和小波）来构建真实的疫情增长。在第一阶段，我们开发了一种基于图论的方法来更新可用的部分数据，在第二阶段，我们使用这些部分数据通过小波生成合理的完整数据。我们提供了两个数值示例。该方法具有新颖性、可实现性和可适应性，可以应用于机器学习建模框架。
