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.

中文翻译：

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通过谐波分析构建真正的流行病增长。

在本文中，我们提出了一个两阶段程序（结合离散图和小波）来构建真正的流行病增长。在第一阶段，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可信的完整数据。我们提供了两个数值例子。该过程新颖且可实施，适用于机器学习建模框架。