Author: Song-Kyoo Kim
Title: AAEDM: Theoretical Dynamic Epidemic Diffusion Model and Covid-19 Korea Pandemic Cases Document date: 2020_3_20
ID: jf36as70_3
Snippet: This article consists of six sections. Section 2 describes the AAEDM which is an enhanced mathematical epidemic diffusion model to predict the number of infected people by typical epidemics. It also introduces the and which are kappa zeta factors capable to determine the characteristics of this prediction model. These factors could be used for understanding the current Covid-19 pandemic situations without analyzing heavy amount of dataset which o.....
Document: This article consists of six sections. Section 2 describes the AAEDM which is an enhanced mathematical epidemic diffusion model to predict the number of infected people by typical epidemics. It also introduces the and which are kappa zeta factors capable to determine the characteristics of this prediction model. These factors could be used for understanding the current Covid-19 pandemic situations without analyzing heavy amount of dataset which only available for related professionals. Section 3 provides for data scientists or data analysts who want adapt the AAEDM into their statistical systems or the machine learning systems. There are couple of examples to simplify for gathering data and they could freely modify based on their circumstances with only one solid condition. Section 4 discusses the testing and validation of the AAEDM by using the dataset from the Covid-19 statistics in Korea. Although the AAEDM is a simple and handy model, the performance (mainly, an accuracy of future predictions) is fairly acceptable to be used for a statistical model by using Bigdata. Finally, the conclusion and further discussion regarding this research are provided in Section 5 and 6.
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